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Does It Pay to Pay Attention?

Does It Pay to Pay Attention? Abstract We employ a novel brokerage account data set to investigate which individual investors are the most attentive, how investors allocate their attention, and the relation between investor attention and performance. Attention is positively related to investment performance, at both the portfolio return level and the individual trades level. We provide evidence that the superior performance of high-attention investors arises because they purchase attention-grabbing stocks whose positive performance persists for up to six months. Finally, we show that paying attention is particularly profitable when trading stocks with high uncertainty, but for which a lot of public information is available. Received April 24, 2017; editorial decision February 14, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. Traditional asset pricing models assume that investors continuously incorporate all the available information into their investment decisions. In reality, however, attention is a scarce resource and individuals display limited attention. Rational models argue that attention-constrained investors should benefit from paying attention and should pay attention up to the point where the improvement in investment performance equals the costs of information acquisition (see, among others, Peress 2004; Peng and Xiong 2006; Van Nieuwerburgh and Veldkamp 2009, 2010; Kacperczyk, Van Nieuwerburgh, and Veldkamp 2016). The behavioral literature, on the other hand, shows that investors tend to be overconfident and are subject to numerous biases, such as the disposition effect, suggesting that paying more attention may harm rather than improve investor performance (see, among others, Odean 1998a, 1998b, 1999; Barber and Odean 2001; Guiso and Jappelli 2006). While the number of normative models on the behavior of attention-constrained investors has exploded in recent years, lack of data on individual attention has made it virtually impossible to test directly many of the theories, and the literature is still lacking satisfactory answers to seemingly simple questions, such as, how often do investors pay attention to their investment portfolio? What individual characteristics lead certain investors to pay more attention to their investment portfolios compared with others? How do investors allocate their attention? And, more importantly, does paying more attention lead to better or worse investment decisions? We answer these questions using a unique novel brokerage account data set. The data set is unique in that it contains—at the individual investor level—detailed information regarding both investor attention and trading behavior. For approximately 11,000 accounts, we observe the time stamp of investors’ login to the brokerage account website, what web pages they browse within the brokerage account domain, and how much time they spend on each web page. We use this information to construct various measures of attention, such as the number of minutes spent on the brokerage account website, the number of web pages browsed, and the number of logins. The extreme granularity of our data allows us to even identify what type of information and what stocks investors focus their attention on. For the same accounts, we also have detailed trading activity information. For every trade placed by each investor, we observe whether it is a buy or a sell, an identifier of the security traded, the time stamp of when the trade is ordered and executed, the quantity traded, and the price. Finally, the data set contains quarterly holdings information and clients’ biographical characteristics. We first explore the determinants of investor attention, that is, the relation between investor characteristics and investor attention. We find a very large heterogeneity in attention across investors that can be explained by the size and risk of the investors’ portfolios, as well as by investor trading habits and demographic characteristics. Account holders with higher invested wealth and higher exposure to small capitalization stocks, growth stocks, momentum stocks, and the overall market are more attentive. The same is true for investors who trade more frequently. On the other hand, investors who hold a higher fraction of their invested wealth in cash or exchange-traded funds (ETFs) are less attentive. Finally, we find that males pay more attention than females and attention is an increasing function of investors’ age. We then analyze how investors allocate their attention. We show the average annualized number of stocks researched by investors is equal to 31. Additionally, we find that the number of stocks researched by investors is positively related to the overall amount of time spent on the website, the number of stocks in their portfolio, and the number of stocks they trade. On the contrary, investors who have more concentrated and riskier portfolios tend to pay less attention to stock-specific information. In addition to studying the number of stocks researched, we analyze how investors concentrate their attention. We find that older investors and investors who pay more attention have less concentrated attention patterns. Investors who hold fewer stocks, have more concentrated portfolios, trade less, and have a smaller fraction of their portfolio in cash allocate their attention in a more concentrated fashion. Finally, we find that investors pay more attention to local stocks, stocks with a higher weight in their portfolio, stocks that have a higher squared Sharpe ratio, and stocks of companies with a larger market capitalization—as predicted by Van Nieuwerburgh and Veldkamp (2009, 2010). We complete the picture by analyzing what stock characteristics are related to investor attention. Investors pay more attention to companies with higher R&D expenditures, market-to-book ratios, market capitalization, and leverage, suggesting that investors prefer to research large companies that, while risky, have high growth potentials. Finally, our results confirm that many proxies used in the literature, such as stocks’ volume, turnover, news, and analyst coverage, are indeed positively related to investor attention—with turnover being the most economically and statistically significant. We then turn to studying the relation between attention and performance. We find a strong and positive cross-sectional relation between attention and performance, in that more attentive investors achieve higher risk-adjusted returns and portfolio Sharpe ratios—even after controlling for covariates related to investment style. Our baseline results show that a standard-deviation increase in overall attention is associated with a 1.5% (3.36 |$t$|-statistic) increase in annualized risk-adjusted returns and a 0.09 (7.08 |$t$|-statistic) increase in investors’ annualized Sharpe ratios. The results are consistent when we use other measures of attention—such as the amount of time spent on the research pages of the brokerage account website. Finally, the results that use the number of pages browsed or the number of logins as measures of attention are qualitatively similar, but economically smaller, suggesting that—compared with seconds—pages and logins may be poorer proxies for the process of information acquisition. We also find that, for a given level of attention, those investors who specialize and focus their research efforts on specific stocks perform better compared with those who allocate their attention evenly across all the stocks in their information set. Our cross-sectional analysis cannot distinguish whether investors perform well because they pay more attention from the alternative hypothesis that investors pay more attention because their portfolio has been performing well. To disentangle the two effects, we present results for panel regressions that relate each investor attention to future performance. The additional advantage is that panel regressions allow us to control for (average) investor skills using fixed effects and overall market conditions using time effects. At the one-month horizon, we find that a one-standard-deviation increase in attention is associated with an annualized increase in portfolio risk-adjusted performance of |$0.38\%$| (2.27 |$t$|-statistic). The effect increases to |$0.62\%$| (4.21 |$t$|-statistic) at the two-month horizon and decreases slightly to |$0.55\%$| (3.66 |$t$|-statistic) at the three-month horizon. This indicates that paying attention allows individual investors to improve their portfolio performance in the short term, suggesting that the improvement in performance hinges on attentive investors being able to purchase (sell) stocks that—in the short run—realize relatively large positive (negative) returns. Controlling for trading fees does not have a significant impact on our results. Finally, we find that the effect of attention is stronger for those investors who are—on average—less attentive. In particular, we divide our investors in five quintiles based on their unconditional attention and show that the relation between attention and future performance is monotonically decreasing across the five groups. Furthermore, while the coefficients on attention for the first four groups are positive and significant, the coefficient on attention for the last group is small, negative, and not statistically different from zero—indicating that there are diminishing marginal returns to attention. Because portfolio performance does not necessarily capture investors’ active management, we provide results on the relation between attention and the performance of individual trades. Attention is positively related to the future performance of the stocks purchased up to four months after the trade is placed. The economic magnitudes are large. At the three-month horizon, for example, a unit-standard-deviation increase in attention increases the average annualized adjusted returns of the stocks purchased by |$2.13\%$| (3.57 |$t$|-statistic). We find—on the other hand—no discernible effect of attention on the performance of the stocks sold. To understand the economic mechanism relating attention to the performance of the stocks traded, we conduct a number of auxiliary exercises. First, by analyzing the performance of the stocks before they are traded, we provide evidence that the superior performance of high-attention investors arises because they purchase attention-grabbing stocks that have appreciated in the recent past and whose positive performance persists for up to six months. However, the relation between attention and future stock returns remains significant when we include attention-grabbing proxies as controls in the performance regressions. Second, we show that attention is particularly profitable when investors trade stocks with high market capitalization, trading volume, volatility, number of analysts, dispersion of analyst forecasts, and news—indicating that it is for the stocks with high uncertainty, but for which a lot of public information is available, that it pays to pay attention. Third, we show that Odean (1999)’s result that the stocks sold by individuals outperform the ones purchased disappears for high-attention trades, but is very strong for low-attention trades. For high-attention trades, the average annualized three-month abnormal return of the stocks purchased equals 3.16%, the one for the stocks sold equals 4.20%, and their difference is statistically insignificant, with a |$p$|-value of 0.29. For low-attention trades, on the other hand, the average annualized three-month abnormal return of the stocks purchased equals |$-0.28$|%, the one for the stocks sold equals 3.08%, and their difference is statistically significant, with a |$p$|-value of 0.00. 1. Related Literature Our paper is related to the theoretical literature that studies the behavior of investors with information capacity constraints. A first strand of this literature focuses on the cross-sectional dimension of inattention—that is, what sources of information or assets should attention-constrained investors pay attention to when facing the problem of allocating their wealth across a number of assets. Peng and Xiong (2006) show that, theoretically, investors with limited attention capacity engage in category-learning, in that they focus not on stock-specific information, but on market- and sector-wide information. Van Nieuwerburgh and Veldkamp (2010) propose a model in which, depending on their preferences, investors behave as “specialized learners” who focus their attention on a single asset or “generalized learners” who focus their attention on multiple assets. Van Nieuwerburgh and Veldkamp (2009) predict that investors should pay more attention to the assets that have a higher squared Sharpe ratio and market capitalization. We contribute to this literature by providing evidence on how brokerage account investors allocate their attention—that is, how many individual stocks they focus their attention on, whether they spread their attention evenly among them, and what stock characteristics drive investor attention. A second strand of this literature focuses on the time-series dimension of attention and shows that, if information acquisition is costly, it is optimal to alternate long periods of inaction to brief spurs of attention, where information is acquired and investment decisions are made (see Gabaix and Laibson 2002; Abel, Eberly, and Panageas 2007, 2013; Huang and Liu 2007; and Alvarez, Guiso, and Lippi 2012). We contribute to this literature by providing empirical evidence on the patterns of investor attention and trading—that is, how often investors pay attention to their investment portfolio, whether they evaluate their portfolio allocations at equally spaced intervals (as many of the theoretical models predict) or they alternate periods of high attention to periods of low attention. Finally, we study what demographic and portfolio characteristics are associated with higher or lower degrees of attention. Our paper is also related to the literature that studies the performance of individual investors. For the most part, the literature has documented the mistakes of individual investors. For example, Odean (1999) and Barber and Odean (2000) show that—on average—individual investors trade too frequently and that trading is detrimental to their wealth. Subsequent studies have uncovered substantial cross-sectional variation among investors’ trading performance. Superior trading performance has been linked to investors’ IQ (Grinblatt, Keloharju, and Linnainmaa 2012; Korniotis and Kumar 2013), education (Von Gaudecker 2015), wealth (Calvet, Campbell, and Sodini 2007), experience (Korniotis and Kumar 2011; Nicolosi, Peng, and Zhu 2009), and portfolio concentration (Ivkovic, Sialm, and Weisbenner 2008). More recently, Frydman, Hartzmark, and Solomon (2017) document that investors make better investment decisions when they sell one asset and quickly buy another one. On reinvestment days, investors display no disposition effect and make better selling decisions. Our results provide novel empirical evidence on the relation between attention and trading performance. If investors acquire valuable information while spending time on the trading platform, we expect their trades to be more profitable as they pay more attention. If investors are incapable of processing the information they acquire, on the other hand, we expect to find no relation between attention and performance. Finally, if investors systematically misinterpret the information they acquire, we expect to find a negative relation between attention and performance. The fact that we find a positive relation between attention and performance, even in the presence of investor fixed-effects, is an indication that at least certain investors are systematically able to understand and exploit the information they acquire. We also contribute to the empirical literature that investigates investor attention, its determinants, and its impact on asset prices. Since its inception, this literature has faced significant challenges in measuring attention itself, leading researchers to resort to attention proxies such as trading volume (Gervais, Kaniel, and Mingelgrin 2001), price limits (Li and Yu 2012; Seasholes and Wu 2007), and news (Yuan 2015; Barber and Odean 2007), and making the implicit assumption that investors are likely to pay attention to stocks that are mentioned in the news or that have been heavily traded on a given day. More recently, Da, Engelberg, and Gao (2011) propose the use of Google searches as a direct measure of aggregate attention and—using Google searches—Vlastakis and Markellos (2012) and Andrei and Hasler (2015) show that aggregate attention varies as a function of stock market volatility. The advantage of using aggregate Google web searches over news is that they identify the information investors actively seek, rather than the information they are potentially exposed to. A key shortcoming, on the other hand, is that they are not specific to the individual investor and therefore cannot shed new light on how attention and the process of information acquisition relate to trading at the individual investor level. The only studies that obtain direct measures of investor attention at the individual level—and are therefore closest to ours—are Karlsson, Loewenstein, and Seppi (2009) and Sicherman et al. (2016). Using a large panel of investors’ logins to 401K accounts as a measure of attention, they show that investors pay less attention to their investment portfolio after stock market declines. They also show that investors’ attention varies as a function of portfolio holdings, wealth, and demographic characteristics such as age and gender. While our work confirms many of the findings in Sicherman et al. (2016), it is different along several dimensions. First, we have information on brokerage rather than 401K accounts. This is important, because 401K investors can only choose among a limited number of fixed income and equity funds, and are completely unable to purchase or sell individual stocks. Furthermore, as shown by Agnew, Balduzzi, and Sunden (2003), Madrian and Shea (2001), and Sialm, Starks, and Zhang (2015), 401K investors display very limited trading, a high degree of inertia, and extreme asset allocations.1 Second, our measures of attention are not limited to investors’ logins, because—for each investor—we observe what information he or she browses and how much time he or she spends doing it. This means that we are able to provide results regarding what information investors pay attention to and how much time they spend thinking about their portfolio decisions. Finally, we have detailed information regarding investors’ portfolio allocations and trades, meaning that we can relate investors’ attention to the type of stocks they own and to the performance of the stocks they purchase and sell. 2. Measuring Attention Using Investor Web Activity In this section, we discuss the measures of attention used in the paper. We start by presenting the data we have access to in terms of investors’ web activity, and we show that—once aggregated across all the investors in our data set—the information measures we construct have an information content similar to that of Google’s Search Volume Index. The advantage of our measures, however, is that they are available for each investor and therefore allow us to study the relation between attention and investment decisions at the individual level. 2.1 The web activity we observe Before describing the measures of attention we construct, we provide an example of the web activity we observe for each investor in our sample. Table 1 shows the web behavior of a sample account holder on January 28, 2014.2 To preserve the anonymity of the brokerage account house, we mask the URLs we have access to and, rather than reporting the full string characterizing each URL, we report only the content of the web page each URL is associated with. Table 1 Example of web activity within the brokerage account website Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 This table displays the web activity—within the brokerage account website—of an account holder on January 28, 2014. Timestamp includes the date, hour, minute, and second of the first click on the web page; Masked URL is the masked URL of the web page browsed by the investor. |$Duration$| is the number of seconds spent on the web page, and |$Session$| is the web-session number within the trading day. The URLs presented in the table have been masked to preserve the anonymity of the brokerage account house. Table 1 Example of web activity within the brokerage account website Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 This table displays the web activity—within the brokerage account website—of an account holder on January 28, 2014. Timestamp includes the date, hour, minute, and second of the first click on the web page; Masked URL is the masked URL of the web page browsed by the investor. |$Duration$| is the number of seconds spent on the web page, and |$Session$| is the web-session number within the trading day. The URLs presented in the table have been masked to preserve the anonymity of the brokerage account house. The table shows that the account holder had a total of four sessions over the day. The first connection occurred at 7:58:05 am (Central Time) and, after logging to the Home page, the investor checked his or her Balances and Positions as well as his or her stocks Watchlist. The Balances and Positions page reports not only the amount of wealth invested in each stock, but also important stock information such as past returns, historical and forecasted earnings and dividends, key accounting information such as price-to-book and price-to-earnings ratios, basic facts such as revenues and institutional ownership, and a summary of analysts’ opinions as well as recent news. The Watchlist is a web page containing information on all the stocks and other assets the investor decides to pay attention to. The second session starts only thirty minutes later, two minutes after the markets open in New York. This second session is much longer, approximately forty minutes, and entails more actions. The investor first checks the Research page of the website detailing the latest news on the |$SPX$|⁠, the S&P 500 index. Right after, he or she connects to the page displaying the stocks Watchlist to quickly switch to the Research page relative to the |$VIX$|⁠. The rest of the session is dedicated to assessing Balances and Positions of his or her trading account and the Watchlist. The third session occurs right around lunch time, it lasts a little more than eighteen minutes, and it involves a look at the Watchlist as well as the Balances and Positions pages. Finally, the last session of the day occurs at 14:28:54 pm, thirty minutes before the markets close. It lasts only forty seconds and involves just a quick look at his or her Balances and Positions and the Watchlist. 2.2 From links to attention The web activity information presented in Table 1 is a small example of the web-activity data we were granted access to. In particular, as part of a large SQL relational database described in more details in Appendix A, the brokerage house gave us access to a web-activity table containing the web pages visited—within its website domain—for approximately 11,000 randomly chosen accounts over the period January 2013–June 2014. The data set is very granular and contains in excess of 17 million observations. Each observation contains a unique numeric account identifier, the URL of the web page visited within the brokerage account domain, the date and time of the first click on the page, the number of seconds spent on the page, and the number of the web sessions within the trading day. Next, we explain how we use this data to construct a variety of attention measures. 2.2.1 Overall attention measures The first set of attention measures is related to the overall attention paid by investors to their brokerage account. The first measure is the number of seconds spent on the brokerage account website over a given time interval. The second measure computes the total number of pages visited by the investor. Finally, the third measure is the number of investor logins to the brokerage account website. Using three measures is important for robustness purposes. One may prefer the number of pages visited rather than the number of seconds spent on the website, because it is possible for some individuals to stay logged into the brokerage account website and leave it in the background while performing other activities. On the other hand, one may prefer the number of seconds rather than the number of pages visited, because it is unlikely for someone to understand the content of a stock report if he or she spends only one or two seconds reading it. Considering the number of logins is also important, because it potentially allows us to distinguish between the extensive and intensive margin of investors’ attention. For example, an investor who connects multiple times a day, but stays connected for just a few seconds, is probably looking for very different information compared with an investor who connects once or twice but spends an hour or longer on the trading platform. To show that the attention measures we extract from our data are related to the ones that have been proposed in the literature, we present in Figure 1 the time-series—at the weekly frequency—of the total number of seconds aggregated across all investors, and four attention proxies:3 the Google Search Volume Index for “S&P 500” (panel A), the total number of news pertaining to stocks in the S&P 500 (panel B), the trading volume on the S&P 500 (panel C), and the State Street Investor Confidence Index (panel D).4 Each panel reports our measure of attention as a dashed red line and one of the alternative measures as a solid blue line. The plots clearly show that there is a very tight relation between our attention measure and both Google’s Search Volume Index for the S&P 500 (78.2% correlation, statistically significant at the 1% level) and the trading volume on the S&P 500 (66.1% correlation, statistically significant at the 1% level). The news variable is also quite related to our attention measure (43.1% correlation, statistically significant at the 1% level), while the correlation with the confidence index is relatively low—at only 27.7%—and is statistically insignificant. Figure 1 View largeDownload slide Comparison of alternative attention measures This figure compares the weekly total number of seconds spent on the brokerage account website—computed across all account holders—to four proxies of investor attention that have been proposed in the literature: the Google Search Volume Index for the word “S&P 500,” reported in the top-left panel; the total number of news pertaining to the stocks in the S&P 500, reported in the top-right panel; the trading volume on the S&P 500, reported in the bottom-left panel; and the State Street Investor Confidence Index, reported in the bottom-right panel. In each panel, our measure of attention is reported as a dashed red line, while the alternative measure of attention is reported as a solid blue line. Figure 1 View largeDownload slide Comparison of alternative attention measures This figure compares the weekly total number of seconds spent on the brokerage account website—computed across all account holders—to four proxies of investor attention that have been proposed in the literature: the Google Search Volume Index for the word “S&P 500,” reported in the top-left panel; the total number of news pertaining to the stocks in the S&P 500, reported in the top-right panel; the trading volume on the S&P 500, reported in the bottom-left panel; and the State Street Investor Confidence Index, reported in the bottom-right panel. In each panel, our measure of attention is reported as a dashed red line, while the alternative measure of attention is reported as a solid blue line. 2.2.2 Categorical attention measures The second set of attention measures also uses seconds, pages, and number of logins, but focuses on the various sections of the website visited by investors. The brokerage account website has a hierarchical structure, whereby—for example— all the web pages related to specific tickers like |$SPX$| and |$VIX$| fall under the “Research” category. By parsing all the URLs and categorizing them, we can obtain many other categories such as “Home Page,” “Balances and Positions,” and “Watchlist.” To maintain parsimony, we classify all the available URLs into fourteen categories: Balances and Positions, Research, Trading, Homepage, Account, Watchlist, History Statement, Bank, Mail, Tax, Help, Search, Retirement, and Client.5 Panel A of Table 2 reports the daily total number of hours spent across investors for the top six categories. The most viewed section of the website is—on average—Balances and Positions. This page contains not only information regarding investors’ performance and portfolio weights, but also important stock information such as past returns, historical and forecasted earnings and dividends, key accounting information such as price-to-book and price-to-earnings ratios, basic facts such as revenues and institutional ownership, and a summary of analysts’ opinions as well as recent news. The average time spent across all investors is 787 hours per day, but this aggregate measure has a large standard deviation equal to 412, so the total number of hours spent ranges from 516 to 930 hours for the 25th and 75th percentiles, respectively. Research is the second most popular category, with 483 hours per day, followed by Trading and Homepage. The remaining categories are much less visited by the investors. For example, the average number of hours spent on Watchlist is only 46 per day. Table 2 Summary statistics of investor attention Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 This table reports summary statistics of investor attention. Panel A reports summary statistics of the total daily number of hours spent across all account holders on various sections of the brokerage account website. For each section, we report the mean (⁠|$Mean$|⁠), the standard deviation (⁠|$St. \ dev.$|⁠), and the 25th, 50th, and 75th percentiles of the daily number of hours—all computed in the time-series dimension. Panel B reports the top 20 companies and ETFs researched by brokerage account holders. The three columns show the rank based on the number of minutes, pages, and visits, respectively. Table 2 Summary statistics of investor attention Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 This table reports summary statistics of investor attention. Panel A reports summary statistics of the total daily number of hours spent across all account holders on various sections of the brokerage account website. For each section, we report the mean (⁠|$Mean$|⁠), the standard deviation (⁠|$St. \ dev.$|⁠), and the 25th, 50th, and 75th percentiles of the daily number of hours—all computed in the time-series dimension. Panel B reports the top 20 companies and ETFs researched by brokerage account holders. The three columns show the rank based on the number of minutes, pages, and visits, respectively. Focusing on the various subcategories is important because it allows us to discern whether looking at different types of information leads to different trading behavior and performance. 2.2.3 Stock attention measures The third and final set of attention measures uses only information associated with the Research URLs and focuses on the tickers researched by investors. Panel B of Table 2 reports the top 20 companies and exchange traded funds (ETFs) researched by the investors in our data set over the time period October 1, 2013–June 10, 2014.6 To compute the table, we first sum the number of minutes spent on each ticker across all the account holders in our data set and over the full sample. We then report the rank of each company (or ETF) according to the number of minutes (first column), pages (second column), and visits (third column). Starting from the first column, the table shows that individual investors focus on companies that belong to the consumer-tech space. Interestingly, while we find tech giants such as Facebook and Apple ranked first and second, respectively, we also find companies that have much smaller market capitalization, such as Twitter (7), AT&T (6), Verizon (10), Tesla (11), Sirius XM (15), and Netflix (19), ranked higher or similarly to much larger firms such as Microsoft (14). All in all, it is remarkable that 11 out of the top 20 stocks researched by investors are in the technology space. As expected, large conglomerates and banks also populate the list. For example, among the companies listed, we find Bank of America (3), Ford (4), and General Electric (9). Finally, we find four ETFs in the list: SPDR S&P 500 ETF (5), SPDR Gold Trust (17), SPDR Dow Jones ETF (18), and Market Vectors ETF (20). The results in the second and third columns are similar, indicating that number of minutes, pages, and visits capture similar patterns of behavior. 3. Summary Statistics The source of the proprietary data used in this study is a large U.S. discount broker. Unlike the discount brokers described in Odean (1999), today’s discount brokers operate online and—while providing access to analysts’ research, market news, and a large number of tools to help investors with their trading—they charge very small trading fees (less than |${\$}$|10 per trade). We relegate to Appendix A the detailed description of the various databases employed in our study, and proceed to present the key summary statistics of our final data set. Table 3 reports the summary statistics for the subset of accounts (approximately 11,000) for which we have web-activity information.7 Starting from the biographical traits, the first row of panel A shows that the average age of account holders in our data set is approximately 51, the second row shows that 73% of the account holders are males. While the average and median age are very much aligned with the ones of previous studies, our data set has a slightly higher percentage of women — 27% in our study, compared with 21% in Barber and Odean (2001), for example. The average number of accounts per client is 1.34. This occurs because although 80% of the clients have only one account, 20% of the clients have more than two accounts. Finally, as of June 2014, the average account age is 8.55, which suggests that the average account holder in our sample is quite experienced. Table 3 Summary Statistics Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 This table reports summary statistics of the biographic characteristics (panel A), the portfolio characteristics (panel B), and the trading (panel C) and attention behavior (panel D) of the brokerage account holders in our web-activity data set. For each variable in each panel, we report the sample mean (⁠|$Mean$|⁠), median (⁠|$Median$|⁠), and standard deviation (⁠|$St.\ dev.$|⁠). The statistics reported in panels A and B are computed across accounts. The statistics in panels C and D are computed first in the time-series dimension at the account-holder level, considering only days when the stock markets are open. They are then computed cross-sectionally across account holders. All dollar values were rounded to the nearest thousand, and the percentiles of “dollar value of trades” have been masked upon request of the data provider for confidentiality reasons. Table 3 Summary Statistics Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 This table reports summary statistics of the biographic characteristics (panel A), the portfolio characteristics (panel B), and the trading (panel C) and attention behavior (panel D) of the brokerage account holders in our web-activity data set. For each variable in each panel, we report the sample mean (⁠|$Mean$|⁠), median (⁠|$Median$|⁠), and standard deviation (⁠|$St.\ dev.$|⁠). The statistics reported in panels A and B are computed across accounts. The statistics in panels C and D are computed first in the time-series dimension at the account-holder level, considering only days when the stock markets are open. They are then computed cross-sectionally across account holders. All dollar values were rounded to the nearest thousand, and the percentiles of “dollar value of trades” have been masked upon request of the data provider for confidentiality reasons. Next, we turn to the portfolio characteristics of account holders as of March 31, 2014. As reported in panel B, the average (median) household has a portfolio value that equals |${\$}$|94,000 (⁠|${\$}$|18,000), indicating that the distribution is heavily skewed to the right. Cash holdings average |${\$}$|16,000, and their distribution is also heavily skewed to the right. Conditional on having at least one stock in the portfolio, the average account holds 6.51 stocks worth |${\$}$|82,000, and the median counterpart is four stocks for a total of |${\$}$|15,000.8 The median values for stock holdings are in line with those in Barber and Odean (2000), who report that the median household holds 2.61 stocks worth |${\$}$|16,210. Panel C of Table 3 reports summary statistics for the trading behavior of account holders. The results are computed as follows. In the first step, we compute the results for each account holder using his or her full time series, eliminating the days when the stock markets are closed—mainly weekends and holidays—as individuals tend to connect much less at those times. In the second step, we compute cross-sectional results across account holders. The first row shows that, on average, investors trade on 3% of the days, with 50% of the investors not trading at all, and 1% of the investors trading more than 44% of the days. For those investors placing at least two trades, the number of days between trades averages 47, resulting in approximately one trade every two months. The median counterpart is 25 days. Conditioning on placing a trade, the average investor places 1.72 trades per day, the median being 1.31. This indicates that investors tend to cluster their trades, consistent with the idea that—once they decide to reoptimize their investment positions—investors like to make multiple transactions on the same day. Finally, the average trade size is |${\$}$|16,000, in line with Barber and Odean (2000), who report an average trade size of |${\$}$|13,707 (⁠|${\$}$|11,205) for stock purchases (sales). Panel D reports summary statistics for the attention behavior of account holders. The average percentage of days with logins (across investors) equals 17%—which is almost six times larger than the trades’ frequency. The most active 1% of the investors log in 96% of the days, while the median investor logs in 6% of the days. The number of days between logins also shows that login and trading behaviors are quite different in terms of magnitude. The average number of days between logins averages 27.51 across account holders, while the median value is 11.20. Note that these numbers are not only much smaller than their trades counterparts, but they are also computed using information for twice as many account holders: that is, those accounts that do not trade at all over our sample. Conditional on logging in, investors revisit their trading account several times within a given day—the average is 10.61 and the median is 7.33. This indicates that there is a large degree of clustering in the visits we observe and that investors do not log in at regular intervals. Once they decide to pay attention, investors seem to spend a substantial amount of time on the trading platform. In particular, the average number of minutes spent on the website—conditional on logging in—equals 29, while the median value is 8. As expected, the shortest 1% of the sessions lasts only 18 seconds, while the longest 1% of the sessions can be as long as 366 minutes (more than 6 hours). To help the visualization of the cross-sectional variation in investor attention and to display how the within-investor attention varies over time, we use a heat-map graph in Figure 2. The figure is constructed as follows. For each account holder, we generate a time series from January 2013 through June 2014, eliminating the days when the stock markets are closed—mainly weekends and holidays—as individuals tend to connect much less frequently at those times. We then compute, for each investor, the daily number of minutes spent on the investment account.9 Finally, to ease the visualization, we sort the accounts by the total number of minutes over the full sample, so that the more active accounts are at the top of the figure. Figure 2 uncovers considerable heterogeneity in behavior across accounts. At the top, we find the more attentive investors that consistently log in and spend about one to two hours per day on their account. At the very bottom, on the other hand, we find those individuals that rarely log in. The figure also highlights some heterogeneity in individual accounts’ behavior over time. For example, the horizontal lines of “colder” colors—that appear in multiple parts of the figure—identify periods when a given investor pays more attention than usual to his or her investment portfolio. The opposite holds true for the horizontal lines of “warmer” colors, even though these are harder to discern. The first eight days of the sample are characterized by lighter colors for all clients. This is because the company was introducing and testing the tracking system over that week and the system was not fully functional. We do not include these observations in the computation of the results reported in the rest of the paper. Figure 2 View largeDownload slide Heat map of investors’ attention This figure reports the heat map of the daily number of minutes spent on the brokerage account website by each account holder. Each point on the |$x$|-axis represents a business day, and each point on the |$y$|-axis represents an account holder. By focusing on one value on the |$y$|-axis and moving from left to right, the reader can assess how the attention of an individual investor varies over time. Likewise, by focusing on one value on the |$x$|-axis and moving from top to bottom, the reader can observe how—for a given day—attention varies across account holders. The accounts are sorted by the total number of minutes spent on the brokerage account website over the full sample, so that the more active investors are at the top of the figure. The number of minutes is winsorized at the 95th percentile, so that the number of minutes spent on the brokerage account website ranges from 0 to 150. Figure 2 View largeDownload slide Heat map of investors’ attention This figure reports the heat map of the daily number of minutes spent on the brokerage account website by each account holder. Each point on the |$x$|-axis represents a business day, and each point on the |$y$|-axis represents an account holder. By focusing on one value on the |$y$|-axis and moving from left to right, the reader can assess how the attention of an individual investor varies over time. Likewise, by focusing on one value on the |$x$|-axis and moving from top to bottom, the reader can observe how—for a given day—attention varies across account holders. The accounts are sorted by the total number of minutes spent on the brokerage account website over the full sample, so that the more active investors are at the top of the figure. The number of minutes is winsorized at the 95th percentile, so that the number of minutes spent on the brokerage account website ranges from 0 to 150. Taken together, these findings represent a new benchmark for the models of optimal inattention, which often imply inattention intervals much longer than the ones we observe. For example, the model of Gabaix and Laibson (2002) implies an inattention interval of approximately twelve months, the one by Abel, Eberly, and Panageas (2007) an inattention interval of eight months. With values slightly greater than one month, the only model that predicts inattention intervals in line with the ones we find in the data is the one by Abel, Eberly, and Panageas (2013). Our findings also show that it is crucial for inattention models to capture the asynchronicity between attention and trading, as featured in Abel, Eberly, and Panageas (2013) and Alvarez, Guiso, and Lippi (2012). Finally, the clustering of attention, whereby investors pay attention to their portfolios for several days or weeks and then decide to be inattentive for months (or even years) is difficult to reconcile with standard models of optimal inattention—that predict instead regular inattention intervals. 4. Attention Allocation We start this section by analyzing the determinants of investor attention. That is, we explore the observable traits that characterize more and less attentive investors. The second part of this section is dedicated to providing new evidence on how investors allocate their attention. We first provide novel facts regarding the degree of investor specialization in information acquisition. We then relate the number of stocks investors pay attention to, both inside and outside their investment portfolio, to their characteristics. Finally, we explore how investors allocate their attention among the stocks they track and the characteristics of the stocks they research. 4.1 Who are the most attentive investors? In Van Nieuwerburgh and Veldkamp (2010), information acquisition involves two dimensions: how much attention to allocate, and how to allocate attention across the assets considered. Thanks to the richness of our data, we are able to study empirically the process of information acquisition at the individual investor level. In this subsection, we focus on the first dimension and study the cross-sectional determinants of investor attention. We use the number of minutes investors spend on the brokerage account website—and its various sections— as a measure of attention and relate it to investor holdings, trading, and demographic characteristics. We estimate the following cross-sectional regression at the account level: \begin{align} Attention_i= \alpha+ \boldsymbol x'_i \ \boldsymbol \beta+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N \label{cross_reg_acctid}, \end{align} (1) where |$Attention_i$| is the (log) total number of minutes spent over the 18-month period on the brokerage account website by account holder |$i$| and |$N$| is the total number of accounts in our data set.10 We divide the conditioning variables into three groups. The first group comprises demographic variables: Male, a male dummy variable; Brokerage, a brokerage account dummy; Age, the age of the investor; and Account Age, the age of the account. The second category comprises portfolio holdings variables as of December 31, 2013: Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of wealth held in cash, traded funds, and mutual funds, respectively. The third category comprises regressors related to portfolio risk and trading activity: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, small-minus-big, high-minus-low, and momentum factors—computed using daily returns over the full sample; and N. of Stocks Traded, the number of stocks traded over the period. To ease the economic interpretation of our estimates, we de-mean and standardize all regressors so that they have unit variance.11 The results for this cross-sectional regression are reported in Table 4. The first column reports the coefficient of each regressor, the second its |$t$|-statistics, and the third its economic magnitude. This last column is computed as follows. We first compute the number of minutes spent on the brokerage account website by the base-case investor, which equals 820.91 minutes, or 13.6 hours. We then multiply |$e^{\beta_k}-1$| to this base number for each coefficient |$\beta_k$| where |$k=1, \ldots , K$|⁠.12 For example, the economic magnitude of the |$Brokerage$| dummy is computed as |$820.91 \times (e^{0.590}-1)=660.11$|⁠, meaning that brokerage account holders spend 660.11 additional minutes—or 11.1 hours— compared with non–brokerage account holders, for a total of 820.91 + 660.11 = 1,481.02 minutes. This result is quite remarkable, as it shows that brokerage account investors spend twice as much time on their account compared with IRA and other account-type holders. Table 4 Characteristics of attentive investors Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 This table reports regression results on the relation between the time spent on the brokerage account website and account holder characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; N. of Assets, the total number of stocks, mutual funds, and exchange traded funds held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The third category comprises regressors related to portfolio risk and trading activity: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low, and Momentum factors computed using the full sample available using daily returns; and N. of Stocks Traded, the number of stocks traded over the period. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistics (⁠|$t$|-stat), and the economic magnitudes (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 4 Characteristics of attentive investors Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 This table reports regression results on the relation between the time spent on the brokerage account website and account holder characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; N. of Assets, the total number of stocks, mutual funds, and exchange traded funds held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The third category comprises regressors related to portfolio risk and trading activity: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low, and Momentum factors computed using the full sample available using daily returns; and N. of Stocks Traded, the number of stocks traded over the period. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistics (⁠|$t$|-stat), and the economic magnitudes (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The remaining regressors associated with demographic characteristics show that, first, males pay more attention than women to their investment portfolios — by an average of 299.14 minutes over the period. Second, investors pay more attention to their investment portfolios as they get older. The effect is also quite strong, as a standard-deviation increase in age is associated with an increase in attention of 277.52 minutes. Third, investors’ attention does not increase with the amount of time investors have had their account open. Turning to the regressors associated with portfolio characteristics, we find that investors with higher wealth pay more attention, while attention is negatively related to the fraction of the portfolio invested in cash or in exchange-traded funds. Finally, the fraction of wealth invested in mutual funds does not seem to be related to attention in any significant manner. In terms of portfolio performance and risk, we find that investors that have greater exposure to the aggregate stock market, as measured by the beta of their portfolio with respect to the market factor, pay more attention. The same is true for those investors who are more exposed to the SMB and MOM factors, indicating that those investors that invest in small caps and momentum stocks are, overall, more attentive to their portfolios. The opposite holds for HML exposure, where we find that investors who focus on growth stocks, as opposed to value stocks, are more attentive. The final regressor in this category is the number of trades undertaken by the account holders over our sample. As expected, the more trades they place, the greater the degree of attention they pay to their investment portfolio. The coefficient for this regressor is both statistically and economically very significant: a standard-deviation increase in the number of trades is associated with an increase in attention of 1,030.81 minutes (17.2 hours) over the sample. Online Table 2 shows that the results are similar when using Research and Balances and Positions as measures of attention, and Online Table 3 shows that our results are robust to using number of pages and logins. 4.2 How do investors allocate their attention? The second dimension of information acquisition—as modeled by Van Nieuwerburgh and Veldkamp (2009, 2010)— involves allocating attention across the assets considered. In this respect, we provide novel empirical evidence along two dimensions. We first focus on the number of assets investors decide to pay attention to—that is, how many stocks they have on their “radar.” We then analyze how investors allocate their attention among the assets on their radar. To conduct this analysis, we focus on the stock-specific attention measures discussed in Section 2.2.3. 4.2.1 How many stocks do investors pay attention to? If investors did not have attention constraints, they would continuously acquire information regarding all the stocks potentially available. Because of their limited time and resources, however, investors generally collect information regarding a rather small number of assets, and some investors do not collect stock-specific information at all. For example, out of the 10,768 accounts in our data set, only 4,970 collect stock-specific information over the time period October 1, 2013–June 10, 2014. Among the investors that collect stock-specific information, the average investor acquires information for a total of 31 stocks annually. The distribution, however, is very skewed to the right, with the median investor looking at 8 stocks and the |$75$|th, |$95$|th, and |$99$|th percentiles of the distribution equal to 25, 128, and 376, respectively. To understand whether there is a systematic relation between investor characteristics and the number of stocks in their radar, we estimate the following baseline cross-sectional regression: \begin{align} N\_Stocks\_Researched_{i}=\alpha+\boldsymbol x'_i \ \boldsymbol \beta+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N, \label{stocks\_searched} \end{align} (2) where |$N\_Stocks\_Researched_{i}$| is the (log) number of stocks searched by investor |$i$|⁠, |$N$| is the total number of investors included in the analysis, and |$\boldsymbol {x}'_i$| is the same vector of investor characteristics contained in Equation (1), augmented with four regressors: Total Attention, the total amount of time spent by the investor on the website (see Section 2.2 for details) Stock Herfindahl, the Herfindahl index of each investor’s portfolio holdings as of December 31, 2013; N. Stocks, the total number of stocks held as of December 31, 2013; and N. of Stocks Traded, the number of stocks traded during the sample. We also estimate modifications of the baseline specification, where we focus, respectively, on the attention paid to the stocks inside or outside investors’ portfolios. As in Section 4.1, we de-mean and standardize all regressors so that they have unit variance. The results of the three specifications—reported in panel A of Table 5—suggest the following. First, across all specifications, we find that the number of stocks researched by investors is positively related to the overall amount of time spent on the website (Total Attention), the number of stocks in their portfolio (N. Stocks), and the number of stocks they trade (N. of Stocks Traded). On the contrary, investors who have more concentrated (Herfindahl) and riskier (Risk Idio) portfolios research fewer stocks. We also find that older investors tend to acquire information regarding a larger number of stocks. Overall, these results suggest that the investors that have broader portfolio allocations (and trade more often) are also the ones that monitor the stocks they own and explore new investment opportunities more actively. In terms of economic magnitudes, the effect of the various covariates is quite large. For example, the coefficient on Total Attention in panel A.I equals 0.423, indicating that a one-standard-deviation increase in the number of stocks held by investors is associated with a |$e^{0.423}-1= 52.6\%$| increase in the number of stocks researched. For the average investor that researches 31 stocks, this translates to an additional 16.3 stocks researched. Table 5 Investor attention allocation: cross-sectional regressions Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 This table reports regression results on the relation between attention allocation and account holder characteristics. In panel A we estimate the following cross-sectional regression: \begin{align*} N\_Stocks_{i}=\alpha+\boldsymbol{x}'_i \ \boldsymbol{\beta}+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$N\_Stocks$| is the log number of stocks researched by investor |$i$|⁠, |$\boldsymbol{x}_i$| is the vector of investor-specific covariates, and |$N$| is the total number of investors included in the analysis. The results in panel A.I use all the stocks researched by each account holder, and panel A.II (panel A.III) includes only the stocks inside (outside) each investor’s portfolio. In panel B we estimate the following cross-sectional regression: \begin{align*} Herfindahl_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} , \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$Herfindahl_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$|—computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention allocated to stock |$k$| and |$K$| is the total number of stocks searched. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises Total Attention, defined as the log total number of seconds spent by the investor on the brokerage account website over the full sample. The third category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; Herfindahl Stocks, the normalized Herfindahl index of investor |$i$|’s stock holdings—computed as |$\frac{\sum_{j=1}^{J} \omega_{j}^{2} - 1/J}{1-1/J}$| if |$J>1$| and 1 if |$J=1$|⁠, where |$w_{j}$| is the fraction of wealth allocated to stock |$j$| and |$J$| is the total number of stocks held; N. Stocks, the total number of stocks held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The fourth category comprises regressors related to the performance and risk of investors’ portfolios: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low and Momentum factors computed using the full sample available using daily returns; Risk Idio, the idiosyncratic risk of the investor’s portfolio. Finally, the last covariate is N. of Stocks Traded: the number of stocks traded over the period. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 5 Investor attention allocation: cross-sectional regressions Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 This table reports regression results on the relation between attention allocation and account holder characteristics. In panel A we estimate the following cross-sectional regression: \begin{align*} N\_Stocks_{i}=\alpha+\boldsymbol{x}'_i \ \boldsymbol{\beta}+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$N\_Stocks$| is the log number of stocks researched by investor |$i$|⁠, |$\boldsymbol{x}_i$| is the vector of investor-specific covariates, and |$N$| is the total number of investors included in the analysis. The results in panel A.I use all the stocks researched by each account holder, and panel A.II (panel A.III) includes only the stocks inside (outside) each investor’s portfolio. In panel B we estimate the following cross-sectional regression: \begin{align*} Herfindahl_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} , \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$Herfindahl_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$|—computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention allocated to stock |$k$| and |$K$| is the total number of stocks searched. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises Total Attention, defined as the log total number of seconds spent by the investor on the brokerage account website over the full sample. The third category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; Herfindahl Stocks, the normalized Herfindahl index of investor |$i$|’s stock holdings—computed as |$\frac{\sum_{j=1}^{J} \omega_{j}^{2} - 1/J}{1-1/J}$| if |$J>1$| and 1 if |$J=1$|⁠, where |$w_{j}$| is the fraction of wealth allocated to stock |$j$| and |$J$| is the total number of stocks held; N. Stocks, the total number of stocks held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The fourth category comprises regressors related to the performance and risk of investors’ portfolios: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low and Momentum factors computed using the full sample available using daily returns; Risk Idio, the idiosyncratic risk of the investor’s portfolio. Finally, the last covariate is N. of Stocks Traded: the number of stocks traded over the period. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Second, we find that certain covariates have differential impact on the number of stocks researched inside and outside the portfolio. For example, male account holders seek new investment opportunities more actively—compared with females—as exemplified by the coefficients on Male reported in panel A.II. We do not, however, observe any difference between males and females for the stocks inside the investment portfolios. Third, as shown in panel A.III, the value of the investment portfolio is positively related to the number of stocks in the portfolio monitored by the investors. Along the same lines, we find that the greater the fraction of wealth invested in cash, ETFs or mutual funds, the lower the number of stocks in the portfolio tracked by the investors. These results are in stark contrast with the ones reported for the same regressors in panel A.II, where the regression coefficients are positive—rather than negative— and statistically significant for the fraction of wealth invested in cash. Economically, these positive coefficients suggest that it is the investors with free cash who actively seek new investment opportunities. Finally, our results suggest that greater exposures to the Market, SMB, HML, and MOM factors are negatively related to the attention paid by investors to new investment opportunities. The same regressors have instead an insignificant—or positive—effect on the attention spent by investors monitoring their current holdings. The results reported in panel A of Table 5 compute coefficient estimates exploiting variation across investors. If we take the relation between the number of stocks held and Total Attention as an example, they show that those investors who pay more attention to their investment portfolio also track more stocks. We also compute panel regressions with time and account fixed effects, which exploit within-investor variation. Our findings, reported in panel A of Online Table 4, are that investors tend to increase the number of stocks they track as they pay more attention and as they trade more. 4.2.2 How do investors allocate attention among the stocks they follow? We address the second question, that is, how do account holders allocate their attention among the assets on their radar, using three distinct approaches. The first exploits only cross-sectional variation. We estimate a regression very similar to the one in Equation (2), but replace the dependent variable |$N\_Stocks\_Researched_{i}$| with |$Herfindahl\_Researched_{i}$|⁠: the Herfindahl index of the stocks researched by investor |$i$|⁠. This quantity is computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention of investor |$i$|—measured in seconds—allocated to stock |$k$| over the full sample and |$K$| is the total number of stocks searched. The measure is bounded between 0—attention allocated evenly across all assets—and 1—all attention concentrated in one asset. The results, reported in panel B of Table 5, indicate that older investors and investors with greater total attention have less concentrated attention patterns, as shown by the negative coefficients on Age and Total Attention. We also find a positive effect for Herfindahl Stocks, and a negative effect for N. Stocks, Fr. in Cash, and N. of Stocks Traded, meaning that investors who hold fewer stocks, have more concentrated portfolios, trade less, and have a smaller fraction of their portfolio in cash allocate their attention in a more concentrated fashion. Finally, we find that investors whose portfolios have higher idiosyncratic risk have more concentrated patterns of attention across the stocks they search. The second approach exploits within-investor variation in a panel regression setting. The results, reported in panel B of Online Table 4, indicate that the more investors pay attention to their investment portfolio and the more they trade, the more evenly they allocate attention. None of the other covariates related to portfolio characteristics and risk are related to how investors allocate their attention. In the third exercise, we study which stocks investors pay attention to whenever they decide to acquire stock-specific information. We follow Hartzmark (2014) and, for every investor, we focus only on those days when stock specific information is acquired. We then estimate the following regression: \begin{align} Att\_Dummy_{i,j,t}=\alpha_{i} + {\boldsymbol x}'_{i,j,t } \ \boldsymbol{\beta} + \epsilon_{i,j,t}, \end{align} (3) where |$Att\_Dummy_{i,j,t}$| is a dummy equal to 1 if investor |$i$| researches stock |$j$| on date |$t$| and is equal to 0 for all those stocks that are in investor |$i$|’s investment opportunity set on date |$t$|⁠, but are not researched. Because it is not possible for us to know what is the investment opportunity set of every account holder at any given point in time, we consider three alternatives. The first includes only the stocks held in the investment portfolio at time |$t$| (panel A). The second uses all the stocks researched by the investor over our sample (panel B). The third includes all the stocks researched by the investor as well as all the stocks in the S&P 500 index at time |$t$| (panel C). For each investment opportunity set, we estimate two specifications. The first uses only covariates motivated by the theoretical models in Van Nieuwerburgh and Veldkamp (2009, 2010), which predict that investors should pay more attention to the stocks that have a higher squared Sharpe ratio and market capitalization. Our vector |${\boldsymbol x}'_{i,j,t }$| therefore includes: Squared Sharpe Ratio, the squared realized Sharpe ratio for each stock in the investment opportunity set, computed over the previous 21 days,13 and Size, the market value of the stock—computed as the product of the price and shares outstanding. In the second specification, we additionally include Dummy Close, an indicator variable identifying whether the headquarters of stock |$j$| are located within a 250-mile radius from investor |$i$|⁠, as in Ivkovic and Weisbenner (2005).14 In panel A, we also include Portfolio Weight, the weight of stock |$j$| in investor |$i$|’s portfolio as of time |$t$|⁠. Finally, we include account holder fixed effects and we standardize the regressors to ease the interpretation of their coefficients. Depending on the definition of investment opportunity set, the coefficients have a different interpretation. If we take the coefficient on Squared Sharpe Ratio as an example, in panel A the coefficient measures the relation between a standard-deviation increase in the squared Sharpe ratio of a stock and the probability that an investor focuses on it rather than on another stock held in his or her investment portfolio. In panels B and C the interpretation is similar, but the comparison is with the stocks that the investor tracks over the sample (panel B) or all the stocks in the S&P 500 index in addition to the ones the investor tracks over the sample (panel C). In line with the predictions of Van Nieuwerburgh and Veldkamp (2009, 2010), the results—reported in Table 6—indicate that investors pay more attention to the stocks that have a higher squared Sharpe ratio as the coefficients on Squared Sharpe Ratio are positive and significant across all panels and specifications. Size also has a positive coefficient, but is significant only in panels B and C and marginally insignificant (⁠|$t$|-statistic of 1.45) in the first specification of panel A. Table 6 Stock characteristics and investor attention Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 This table reports regression results studying which stocks investors pay attention to, whenever they acquire stock-specific information. We focus only on days when stock-specific information is acquired and estimate the following regression: \begin{align} Att\_Dummy_{i,j,t}=\alpha_i+\boldsymbol{x'}_{i,j,t} \ \boldsymbol {\beta} +\epsilon_{i,j,t} \notag \end{align} where |$Att\_Dummy_{i,j,t}$| is a dummy equal to 1 if investor |$i$| researches stock |$j$| on date |$t$| and is equal to 0 for all the other stocks that are in investor |$i$|’s investment opportunity set, but are not researched. We construct three alternative information sets for each investor. The first includes only the stocks held in the investment portfolio at time |$t$| (panel A). The second uses all the stocks researched by the investor over our sample (panel B). The third includes all the stocks researched by the investor as well as all the stocks in the S&P 500 index at time |$t$| (panel C). In each panel, we estimate two specifications. The first isolates the regressors theoretically motivated by the models in Van Nieuwerburgh and Veldkamp (2009, 2010): Squared Sharpe Ratio, the squared realized Sharpe ratio for each stock in the investment opportunity set, computed over the previous 21 days; and Size, the market value of the stock—computed as the product of the price and shares outstanding. The second additionally includes: Dummy Close, an indicator variable identifying whether the headquarters of stock |$j$| are located within a 250-mile radius from investor |$i$|⁠, as in Ivkovic and Weisbenner (2005). We use the standard formula for computing the distance in miles between account holder |$i$| and firm |$j$|⁠, as follows: |$dist (i, j) = arccos\Big(cos(lat_{i})cos(long_{i})cos(lat_{j}) cos(long_{j}) + cos(lat_{i})sin(long_{i})cos(lat_{j})sin(long_{j})+ sin(lat_{i}) sin(lat_{j})\Big) r,$| where |$lat$|/|$long$| are the latitudes/longitudes of location |$i$| and location |$j$| (expressed in radians), respectively, and |$r$| denotes the radius of the earth (approximately 3,963 miles). In panel A, we also include Portfolio Weight, the weight of stock |$j$| in investor |$i$|’s portfolio as of time |$t$|⁠. The estimates include account holder fixed effects. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are clustered at the account level. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 6 Stock characteristics and investor attention Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 This table reports regression results studying which stocks investors pay attention to, whenever they acquire stock-specific information. We focus only on days when stock-specific information is acquired and estimate the following regression: \begin{align} Att\_Dummy_{i,j,t}=\alpha_i+\boldsymbol{x'}_{i,j,t} \ \boldsymbol {\beta} +\epsilon_{i,j,t} \notag \end{align} where |$Att\_Dummy_{i,j,t}$| is a dummy equal to 1 if investor |$i$| researches stock |$j$| on date |$t$| and is equal to 0 for all the other stocks that are in investor |$i$|’s investment opportunity set, but are not researched. We construct three alternative information sets for each investor. The first includes only the stocks held in the investment portfolio at time |$t$| (panel A). The second uses all the stocks researched by the investor over our sample (panel B). The third includes all the stocks researched by the investor as well as all the stocks in the S&P 500 index at time |$t$| (panel C). In each panel, we estimate two specifications. The first isolates the regressors theoretically motivated by the models in Van Nieuwerburgh and Veldkamp (2009, 2010): Squared Sharpe Ratio, the squared realized Sharpe ratio for each stock in the investment opportunity set, computed over the previous 21 days; and Size, the market value of the stock—computed as the product of the price and shares outstanding. The second additionally includes: Dummy Close, an indicator variable identifying whether the headquarters of stock |$j$| are located within a 250-mile radius from investor |$i$|⁠, as in Ivkovic and Weisbenner (2005). We use the standard formula for computing the distance in miles between account holder |$i$| and firm |$j$|⁠, as follows: |$dist (i, j) = arccos\Big(cos(lat_{i})cos(long_{i})cos(lat_{j}) cos(long_{j}) + cos(lat_{i})sin(long_{i})cos(lat_{j})sin(long_{j})+ sin(lat_{i}) sin(lat_{j})\Big) r,$| where |$lat$|/|$long$| are the latitudes/longitudes of location |$i$| and location |$j$| (expressed in radians), respectively, and |$r$| denotes the radius of the earth (approximately 3,963 miles). In panel A, we also include Portfolio Weight, the weight of stock |$j$| in investor |$i$|’s portfolio as of time |$t$|⁠. The estimates include account holder fixed effects. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are clustered at the account level. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The remaining regressors have coefficients that follow intuition. The coefficient on Dummy Close indicates that investors spend more attention researching the stocks that are located nearby. This finding is important, because it uncovers the potential mechanism associated with the proximity/familiarity results in Massa and Simonov (2006), Ivkovic and Weisbenner (2005), Huberman (2001), and Grinblatt and Keloharju (2001). While the literature has shown that investors hold a more than proportional amount of local stocks in their portfolio, our results suggest that proximity/familiarity operates at the attention level as well. This means that investors include local stocks in their portfolios because they pay attention to local stocks, as opposed to the alternative hypothesis that investors pay attention uniformly across all stocks (near and far) and include in their portfolio only the ones geographically close, because proximity gives them informational advantages. Finally, the coefficient on Portfolio Weight suggests that — among the assets they hold—investors pay more attention to the assets that have a higher weight in their portfolio. 4.3 Which stocks do investors pay attention to? While the results in Section 4.2 show that idiosyncratic factors such as geographic proximity affect investor attention to specific stocks, the summary statistics in Section 3 provide prima facie evidence that investor attention is correlated as investors tend to focus on large and well-known companies such as Facebook, Apple, Bank of America, and Ford—among others. In this section we dig deeper and analyze what stock characteristics are related to investor attention. We consider the sample October 1, 2013, to June 10, 2014, to construct the overall attention paid across all investors to each individual ticker, and estimate the following baseline specification: \begin{align} Minutes_j= \alpha+ \boldsymbol x'_j \ \boldsymbol \beta+\epsilon_{j}, \quad for \,\,\, j=1,\ldots,J \label{cross_reg_stocks}, \end{align} (4) where Minutes is the (log) number of minutes spent researching stock |$j$| over the eight-month period, and |$J$| is the total number of stocks for which we have both attention and conditioning information. We divide our conditioning variables in three groups. The first group comprises attention proxies: Analyst, the number of analysts covering the stock; News, the total number of news events associated with the stock;15Volume, the stock’s volume; and Turnover, computed as the stock’s volume divided by the shares outstanding. The second category comprises regressors related to stocks’ risk and performance. Alpha, Beta Mkt, Beta SMB, Beta HML, and Beta MOM are the intercept and the loadings on the Market, SMB, HML and MOM factors. The coefficients are estimated for each stock |$j$| using daily data over the sample. Return, Variance, Skewness, and Kurtosis are the realized returns, variance, skewness, and kurtosis for each stock |$j$|⁠, computed using daily returns. For the higher moments we follow Amaya et al. (2015) and use the following expressions: \begin{align} RVar_{j} =\sum_{i=1}^{N} r^2_{i,j}, \quad RSkew_{j} =\frac{\sqrt{N}\sum_{i=1}^{N} r_{i,j}^3}{RVar_{j}^{3/2}}, \quad RKurt_{j} =\frac{{N}\sum_{i=1}^{N} r_{i,j}^4}{RVar_{j}^{2}}, \notag \end{align} where |$r_{i,j}$| is the daily return of stock |$j$| on day |$i$| and |$N$| is the number of trading days in the sample. The third category comprises regressors related to stock characteristics: MktBook, the ratio of market value of assets and the book value of assets;16R&D, computed as the ratio of research and development expenses (Compustat item: XRDQ) and sales (Compustat item: SALEQ);17Profitability, the ratio between operating income before depreciation (Compustat item: OIBDPQ) and total assets (Compustat item: ATQ); Age, the number of years a company has been in the CRSP data set; Size, the market value of assets—computed as the product of the price and shares outstanding; Leverage, computed as the sum of long-term debt and debt in current liabilities, divided by the market value of assets; and Inst. Investors, the fraction of the shares outstanding owned by institutional investors. As in Section 4.1, we de-mean and standardize all the regressors so that they have unit variance. The economic interpretation of the coefficients in Table 7 therefore parallels the one of Table 4. The first column reports the coefficient of each regressor, the second its |$t$|-statistic, and the third its economic magnitude. Table 7 Characteristics of researched stocks Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 This table reports regression results on the relation between investor attention—computed across all account holders in our data—and stock characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{j}=\alpha+\boldsymbol{x}'_j \ \boldsymbol{\beta}+\epsilon_{j}, \quad for \,\,\, j=1,\ldots, J, \end{align*} where |$Attention_{j}$| is the log total number of minutes spent—across all account holders—on stock |$j$| and |$\boldsymbol{x}_j$| is a vector of stock-specific covariates. What follows is a description of the three groups of covariates included in the analysis. The first group comprises regressors we classify as attention proxies: Analyst, the number of analysts covering the stock; News, the total number of news events associated with the stock; Volume, the stock’s volume; Turnover, computed as the stock’s volume divided by the shares outstanding. The second category comprises regressors related to stocks’ risk and performance. Alpha, Beta Mkt, Beta SMB, Beta HML, and Beta MOM are the intercept and the loadings on the market, SMB, HML, and MOM factors. The loadings are estimated for each stock |$j$| using daily data over the full sample. Return, Variance, Skewness, and Kurtosis are the realized returns, variance, skewness and kurtosis for each stock |$j$|—computed using daily returns. The third category comprises regressors related to stock characteristics: MktBook, the ratio of market value of assets and the book value of assets; R&D, computed as the ratio of research and development expenses (Compustat item: XRDQ) and sales (Compustat item: SALEQ); Profitability, the ratio between operating income before depreciation (Compustat item: OIBDPQ) and total assets (Compustat item: ATQ); Age, the number of years a company has been in the CRSP data set; Size, the market value of assets computed as the product of the price and shares outstanding; Leverage, computed as the sum of long-term debt and debt in current liabilities, divided by the market value of assets; Inst. Investors, the fraction of the shares outstanding owned by institutional investors. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistic (⁠|$t$|-stat), and the economic magnitude (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 7 Characteristics of researched stocks Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 This table reports regression results on the relation between investor attention—computed across all account holders in our data—and stock characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{j}=\alpha+\boldsymbol{x}'_j \ \boldsymbol{\beta}+\epsilon_{j}, \quad for \,\,\, j=1,\ldots, J, \end{align*} where |$Attention_{j}$| is the log total number of minutes spent—across all account holders—on stock |$j$| and |$\boldsymbol{x}_j$| is a vector of stock-specific covariates. What follows is a description of the three groups of covariates included in the analysis. The first group comprises regressors we classify as attention proxies: Analyst, the number of analysts covering the stock; News, the total number of news events associated with the stock; Volume, the stock’s volume; Turnover, computed as the stock’s volume divided by the shares outstanding. The second category comprises regressors related to stocks’ risk and performance. Alpha, Beta Mkt, Beta SMB, Beta HML, and Beta MOM are the intercept and the loadings on the market, SMB, HML, and MOM factors. The loadings are estimated for each stock |$j$| using daily data over the full sample. Return, Variance, Skewness, and Kurtosis are the realized returns, variance, skewness and kurtosis for each stock |$j$|—computed using daily returns. The third category comprises regressors related to stock characteristics: MktBook, the ratio of market value of assets and the book value of assets; R&D, computed as the ratio of research and development expenses (Compustat item: XRDQ) and sales (Compustat item: SALEQ); Profitability, the ratio between operating income before depreciation (Compustat item: OIBDPQ) and total assets (Compustat item: ATQ); Age, the number of years a company has been in the CRSP data set; Size, the market value of assets computed as the product of the price and shares outstanding; Leverage, computed as the sum of long-term debt and debt in current liabilities, divided by the market value of assets; Inst. Investors, the fraction of the shares outstanding owned by institutional investors. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistic (⁠|$t$|-stat), and the economic magnitude (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The covariates that have been adopted as attention proxies in the literature are the ones that have the greatest economic impact. For example, starting from a baseline of 45.24 minutes, a standard deviation increase in Turnover is associated with a 59.07-minutes increase in attention. Also large are the magnitudes associated with Volume and Analyst. News are significant as well, but their economic impact is smaller.18 Among the second set of regressors, we note that investors pay more attention to stocks that have higher exposure to the market and momentum factors. This indicates that investors spend time researching stocks that have higher systematic risk and have a positive exposure to momentum. The coefficient on SMB is negative and marginally significant, while the coefficient on HML is not significant. Once exposures to market factors are controlled for, stock returns, variance, skewness, and kurtosis do not have a significant impact on attention. In terms of firm characteristics, investors pay attention to stocks that have high growth potentials—as evidenced by the positive coefficients on MktBook and R&D—and that are well established but relatively risky—as evidenced by the positive coefficient on Size and Leverage. We also find a very strong and negative relation between attention and Inst. Investors, suggesting that individual investors pay attention to stocks that are different, compared with the ones owned by institutional investors. Finally, Profitability and Age do not have a significant coefficient. Online Table 5 shows the results are robust when we use Pages and Visits as measures of attention. 5. Attention and Performance: Evidence from Portfolio Returns We now turn to the set of results relating investor attention and portfolio performance. Investors who spend more time acquiring information are likely to receive more trading signals. If these investors are able to correctly process these signals, we would expect them to achieve superior portfolio performance. On the other hand, if they systematically misinterpret these signals, we would expect them to perform poorly. To estimate what relation holds in the data, we propose two strategies. The first strategy uses cross-sectional regressions and addresses the question of whether investors who pay more attention perform better. The second strategy implements panel regressions and uses within-individual variation over time to assess whether changes in investor attention are associated with performance differentials. In both cases, we find a positive relation between attention and investment performance. 5.1 Cross-sectional regressions In Table 8 we evaluate the relation between investor attention and risk-adjusted performance, where the former is measured as overall attention and the latter is measured as the DGTW abnormal return of the investor portfolio.19 We estimate the following cross-sectional regression: \begin{align} AVG\_DGTW\_Ret_{i}=\alpha+\beta \ {Attention_i} + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N, \label{cross_spec_baseline} \end{align} (5) where |$AVG\_DGTW\_Ret_{i}$| is the annualized average percentage DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the total attention spent on the brokerage account website by account holder |$i$| over the sample, and |$\boldsymbol{x}_i$| is a subset of the covariates that explain the cross-section of investor attention—as shown in Section 4.1.20 All regressors are standardized so that they have zero mean and unit standard deviation. Table 8 Attention and portfolio performance: cross-sectional results Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 This table reports regression results on the relation between portfolio performance and investor attention. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta \ {Attention_i} + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$| and |$N$| is the total number of account holders included in the analysis. We include three specifications. The first specification uses attention as the sole covariate. The second specification includes a group of covariates that control for investor demographic characteristics: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The third specification includes portfolio holdings and trading activity variables: Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroskedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% level, respectively. Table 8 Attention and portfolio performance: cross-sectional results Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 This table reports regression results on the relation between portfolio performance and investor attention. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta \ {Attention_i} + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$| and |$N$| is the total number of account holders included in the analysis. We include three specifications. The first specification uses attention as the sole covariate. The second specification includes a group of covariates that control for investor demographic characteristics: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The third specification includes portfolio holdings and trading activity variables: Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroskedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% level, respectively. The results highlight a statistically and economically significant relation between attention and performance. The coefficient in the first specification equals 1.531% and is significant at the 1% level. The results imply that a one-standard-deviation increase in attention is associated with an increase in the investor’s DGTW abnormal return of 1.53%, which is economically rather large. However, while the coefficient on attention is positive and significant, the low |$R^2$| of 0.1% indicates there is a lot of cross-sectional heterogeneity in performance that attention does not explain. The corresponding coefficient estimates for research attention (Online Table 6, panel A) and balances and positions attention (Online Table 6, panel B) are 0.988% and 1.267%, respectively. A common feature of the results reported in this section—as well as the subsequent ones—is that balances and positions attention sometimes has a stronger impact on performance, compared with research attention. While surprising at first sight, the results may be driven by the fact that investors obtain important research information also on the Balances and Positions portion of the website. The information includes stocks’ past returns; historical and forecasted earnings and dividends; key accounting information such as price-to-book and price-to-earnings ratios; basic facts such as revenues and institutional ownership; and a summary of analysts’ opinions as well as recent news. Research pages, on the other hand, contain in-depth analyst reports and balance sheet information for each company. Also, because investors spend almost double the time on balances and positions pages compared with research pages—as shown in Table 2—it may be that the amount of time spent on the Balances and Positions pages is a more precise estimate of the process of information acquisition, particularly for those investors who also acquire information through other sources such as Yahoo Finance or Bloomberg, for example. To control for possible differences in information acquisition capabilities, Specification 2 in each panel includes demographic characteristics as control variables. Adding these regressors leaves the effect of attention on investment performance largely unchanged for all attention measures, indicating that the relation between these additional regressors and performance is largely orthogonal to that of attention. Among the newly added covariates, the ones significant at the 5% level are age and the brokerage dummy. The first has a negative coefficient—indicating that younger investors achieve a better risk-adjusted performance on their investment portfolio. The second shows instead that brokerage accounts seem to outperform IRA and other accounts, on average, possibly because investors in these accounts are actively seeking underpriced stocks rather than holding broad and diversified passive portfolios. Finally, Specification 3 adds—as further controls—regressors related to portfolio size, portfolio allocation, and trading activity. The inclusion of these additional covariates strengthens the effect of attention on performance by 35–50%, depending on the specification: the overall attention coefficient increases to 2.326%, the research attention coefficient increases to 1.776%, and the balances and positions coefficient increases to 2.007%. All coefficients remain statistically significant at the 1% level, indicating that, even after controlling for portfolio composition and trading frequency, the attention is significantly related to the performance of the investors in our sample. As for the effect of the additional controls, we find a negative relation between portfolio size and number of trades and performance. Interestingly, we find that including the number of trades as a control variable increases the effect of attention on performance, suggesting that paying attention attenuates the negative effects of excessive trading documented in the literature; see Barber and Odean (2000). Table 8 reports results in terms of annualized abnormal percentage returns. To compute the dollar gains per hour spent on the platform for the average investor, we combine the information contained in Tables 3 and 8. A one-standard-deviation increase in attention is associated with an increase in abnormal returns of 1.53%, 1.60%, or 2.33%, depending on the specification. From Table 3, we know that the average portfolio size in our data set is |${\$}94,000$|⁠. The standard deviation of overall attention is equal to 88 hours. Using this information, we can compute the dollar gains associated with each hour of attention. For the first specification, this equals: |${\$}94,000\times1.53\%/88={\$}16.3$|⁠. For Specifications 2 and 3, the dollar gains per hour equal |${\$}94,000\times1.60\%/88={\$}17$| and |${\$}94,000\times2.33\%/88={\$}24.9$|⁠. The dollar gains for each hour of attention therefore range from |${\$}$|16 to |${\$}$|25. The economic significance of these quantities depends on the opportunity cost of each individual investor. The average American has an annual income of |${\$}41,000$|⁠. This converts to |${\$}41,000/52/40 = {\$}19.7$| per hour. The average American who invests in the stock market, however, has an annual income of |${\$}52,000$|⁠, which translates to |${\$}52,000/52/40 = {\$}25$| per hour.21 As a first robustness exercise, we implement the calendar-time approach of Barber and Odean (2000). We first sort the investors into five groups according to attention (from Q1 to Q5). We then compute weekly value-weighted and equally weighted returns for each group and estimate Carhart four-factor regressions on these portfolios. We report the results in Table 9. Going from low- to high-attention and focusing on the equally weighted returns reported in panel A, the five groups have the following annualized alpha estimates 0.03, 0.02, 0.02, 0.03, and 0.06, with |$t$|-statistics equal to 0.96, 0.82, 0.96, 1.15, and 2.02. The alphas increase in significance as we move from the second to the fifth portfolio and only the top portfolio is significant at the 5% level, confirming that risk-adjusted performance is positively related to attention. Finally, we form a portfolio long in the most attentive investors (Q5) and short in the least attentive investors (Q1) and find it has an alpha of 0.04, statistically significant at the 5% level. The value-weighted returns in panel B are aligned with the equally weighted returns. The top two portfolios have significant outperformance—at the 10% significance for Q4 and the 5% significance for Q5. The long-short portfolio in the last column also displays an alpha of 0.04, statistically significant at the 1% level. Table 9 Calendar portfolio returns analysis at the weekly frequency Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 This table displays the results of calendar-time portfolio return regressions. We first sort account holders into five groups (Q1 through Q5) based on the number of seconds spent on the brokerage account website over the full sample. We then compute, for each group, equally weighted weekly returns in panel A and value-weighted weekly returns in panel B. Q1 are the least attentive investors while Q5 are the most attentive. Finally, we run the following time-series regression: \begin{align*} r_{q,t}-rf_{t}=\alpha_{q}+\beta_{1,q}\ (Mkt_{t}-rf_{t})+\beta_{2,q} \ HML_{t}+\beta_{3,q} \ SMB_{t}+\beta_{4,q}\ MOM_{t}+\epsilon_{t} \quad for \,\,\, t=1,\ldots,T \& q=1,\ldots,5, \end{align*} where |$r_{q,t}-rf_{t}$| is the annualized excess-return at time |$t$| of portfolio |$q$|⁠; |$Mkt_{t}-rf_{t}$| is the annualized excess return on the NYSE/AMEX/NASDAQ index, |$HML_{t}$|⁠, |$SMB_{t}$|⁠, and |$MOM_{t}$| are the annualized returns on the value, size, and momentum factors, respectively. The last column of each panel reports performance regressions for a portfolio long in the most attentive investors (Q5) and short in the least attentive investors (Q1). We display the ordinary least squares coefficient estimates and the associated |$t$|-statistics computed using Newey-West standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 9 Calendar portfolio returns analysis at the weekly frequency Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 This table displays the results of calendar-time portfolio return regressions. We first sort account holders into five groups (Q1 through Q5) based on the number of seconds spent on the brokerage account website over the full sample. We then compute, for each group, equally weighted weekly returns in panel A and value-weighted weekly returns in panel B. Q1 are the least attentive investors while Q5 are the most attentive. Finally, we run the following time-series regression: \begin{align*} r_{q,t}-rf_{t}=\alpha_{q}+\beta_{1,q}\ (Mkt_{t}-rf_{t})+\beta_{2,q} \ HML_{t}+\beta_{3,q} \ SMB_{t}+\beta_{4,q}\ MOM_{t}+\epsilon_{t} \quad for \,\,\, t=1,\ldots,T \& q=1,\ldots,5, \end{align*} where |$r_{q,t}-rf_{t}$| is the annualized excess-return at time |$t$| of portfolio |$q$|⁠; |$Mkt_{t}-rf_{t}$| is the annualized excess return on the NYSE/AMEX/NASDAQ index, |$HML_{t}$|⁠, |$SMB_{t}$|⁠, and |$MOM_{t}$| are the annualized returns on the value, size, and momentum factors, respectively. The last column of each panel reports performance regressions for a portfolio long in the most attentive investors (Q5) and short in the least attentive investors (Q1). We display the ordinary least squares coefficient estimates and the associated |$t$|-statistics computed using Newey-West standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. As a second robustness exercise, Online Table 7 shows the results that use number of pages (panel A) or logins (panel B) as measures of attention. While the results are qualitatively similar, they are economically smaller, suggesting that—compared with seconds—pages and logins may be poorer proxies for the process of information acquisition. As a third robustness exercise, we use the full sample Sharpe ratio as a measure of risk-adjusted performance. The results, reported in Online Table 8, highlight a statistically and economically significant relation between attention and performance. For overall attention, the coefficient in the first specification equals 0.090 and is significant at the 1% level. The corresponding coefficient estimates for research and balances and positions are 0.108 and 0.137, respectively. The results imply—taking balances and position attention as an example—that a one-standard-deviation increase in attention is associated with an increase in the investor’s Sharpe ratio of 0.137. Economically, this quantity is quite large, because the average Sharpe ratio across the investors in our sample is 1.316.22 The results reported so far show that investor attention is positively related to risk-adjusted performance, even after controlling for investor characteristics and investment style. As we detail in Section 4.1 and the associated Table 4, account holders with higher exposure to small capitalization stocks, growth stocks, momentum stocks, and the overall market are more attentive. Jointly, therefore, the results reported in Table 4 and Table 8 show that attention has both an indirect and a direct effect on investor performance. The indirect effect is the one related to the type of investment decisions attentive investors make compared with inattentive investors. The direct effect, on the other hand, is the effect of attention on performance, controlling for style and other characteristics. Taking the relation between momentum exposure, attention, and risk-adjusted performance as an example, our results in Table 4 uncover the indirect effect of attention on performance, in that more attentive investors tend to follow momentum strategies — buying (selling) stocks that have increased (decreased) in price over the previous twelve months. The results in Table 8, on the other hand, uncover the direct effect of attention on performance, in that attention retains a positive effect on performance even after controlling for momentum using the DGTW procedure. 5.2 Panel data regressions The results reported so far potentially suffer from a reverse-causality problem. It may not be that investors perform better because they pay more attention, but that investors pay more attention because they have been performing better. The cross-sectional regressions cannot distinguish between these two alternative hypotheses, because they compute performance and attention over the full sample. To separate the two effects, we next estimate panel regressions that identify the effect of attention on performance from time-series variations in attention—measured at the individual level. Our baseline specification is: \begin{align} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m +\gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k}, \label{panel_spec} \end{align} (6) where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$| and |$k$| equals 21, 42, and 63 days—depending on the specification;23|$Abn\_Attention_{i,t}$| is account holder |$i$|’s abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Following Da, Engelberg, and Gao (2011), we use abnormal attention—rather than attention—to capture time trends and other low-frequency seasonalities in investors’ attention. This is important, because investors tend to pay more attention to their investment portfolios right after opening their accounts and they tend to lose interest subsequently. Furthermore, even though time effects are included in the regressions, they are unlikely to capture a large part of the time variation in investors’ attention, because individual investors hold relatively few stocks. It may therefore be that certain investors pay a lot of attention in certain periods—because the stocks they own (or they want to purchase) are in the news—and other investors pay attention in other periods. Finally, to ease the comparison across the specifications, we standardize |$Abn\_Attention$| so that it has zero mean and unit standard deviation. To guarantee that our results are not specific to the type of clustering used in the computation of the standard errors, we report two sets of |$t$|-statistics. The first—in round brackets—are computed using standard errors that are double-clustered by account holder and time; see Petersen (2009). The second—in square brackets—are computed using Driscoll and Kraay (1998) standard errors. We base our discussion on the double-clustered standard errors, because they result in more conservative estimates in our data. Reported in Table 10 are the results based on the overall attention measure. Across all horizons, the estimates highlight a positive and significant relation between abnormal attention and performance. Statistically, the results are significant at the 5% level for the one month horizon and the 1% level at the two- and three-month horizons. Economically, the effect of overall abnormal attention is quite large. At the one-month horizon, we find that a one standard deviation increase in attention is associated with an annualized increase in portfolio DGTW-adjusted performance of |$0.032\times12=0.38\%$| per year. The effect increases to |$0.103\times6=0.62\%$| at the two-month horizon and decreases slightly to |$0.138\times4=0.55\%$| at the three-month horizon. This indicates that paying attention allows individual investors to improve their portfolio performance in the short term, suggesting that the improvement in performance hinges on attentive investors being able to purchase (sell) stocks that—in the short run—realize relatively large positive (negative) returns. Table 10 Attention and performance: panel regression results Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 This table reports panel regression results on the relation between portfolio performance and investor attention. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m+ \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the number of seconds spent on the brokerage account website. We report results for different horizons, that is, |$k=21$|⁠, |$42$|⁠, and |$63$| days. Displayed are the ordinary least squares coefficient estimates and two sets of |$t$|-statistics. The first—in round brackets—are computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). The second—in square brackets—are computed using the Driscoll and Kraay (1998) standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively, according to the double-clustered standard errors. Table 10 Attention and performance: panel regression results Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 This table reports panel regression results on the relation between portfolio performance and investor attention. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m+ \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the number of seconds spent on the brokerage account website. We report results for different horizons, that is, |$k=21$|⁠, |$42$|⁠, and |$63$| days. Displayed are the ordinary least squares coefficient estimates and two sets of |$t$|-statistics. The first—in round brackets—are computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). The second—in square brackets—are computed using the Driscoll and Kraay (1998) standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively, according to the double-clustered standard errors. Panels A and B of Online Table 9 report the results for research attention and balances and positions attention, respectively. The results show that, while the effect of balances and positions attention is economically stronger than overall attention, the effect of research attention is somewhat weaker. For example, the annualized effect for the two-month horizon specification equals |$0.135\times6=0.81\%$| for balances and positions attention and |$0.078\times6=0.47\%$| for research attention. In both cases the coefficients are statistically significant at the 5% level. Across all panels, the coefficients are always more significant if we use Driscoll and Kraay (1998) standard errors. The baseline results reported in Table 10 use a window of 21 days to compute abnormal attention. For robustness, in Online Table 10 and Online Table 11 we compute abnormal attention using windows of 42 and 63 days, respectively. In all cases, the results become stronger if we use a longer window to compute abnormal attention. Table 11 Trading performance and investor attention: baseline regression results 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 This table reports regression results on the relation between investor attention and trading performance. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panel A reports results for stock purchases. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 11 Trading performance and investor attention: baseline regression results 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 This table reports regression results on the relation between investor attention and trading performance. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panel A reports results for stock purchases. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The results reported so far use number of seconds as a measure of attention. Online Table 12 shows that the results reported above are generally less significant when we measure attention using number of pages or logins instead of seconds, suggesting—again—that the latter two measures may be poorer proxies for the process of information acquisition. Table 12 Trading performance and investor attention: portfolio results Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) This table reports portfolio results on the relation between investor attention and the performance of the stocks before (panel B) and after (panel A) they are traded. Each panel reports the average cumulative DGTW-adjusted returns at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panels A.I and B.I present the unconditional results and—for every horizon—report the difference in performance between the stocks purchased and sold as well as the |$p$|-value testing whether the difference in performance is equal to zero. Panels A.II and B.II condition on the overall attention paid by the account holders in the month preceding each trade. We divide the buys in two groups—low and high—based on overall attention and report the performance of the the low- and high-attention buys, respectively. We repeat the same procedure to compute low- and high-attention sells. For every horizon, we report the difference in performance between: (i) high-attention buys and high-attention sells; (ii) low-attention buys and low-attention sells; (iii) high-attention buys and low-attention buys; and (iv) high-attention sells and low-attention sells. In each case, we also report the |$p$|-value testing whether the difference in performance is equal to zero. All |$p$|-values are based on the bootstrap procedure suggested by Barber, Lyon, and Tsai (1999). Estimates marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 12 Trading performance and investor attention: portfolio results Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) This table reports portfolio results on the relation between investor attention and the performance of the stocks before (panel B) and after (panel A) they are traded. Each panel reports the average cumulative DGTW-adjusted returns at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panels A.I and B.I present the unconditional results and—for every horizon—report the difference in performance between the stocks purchased and sold as well as the |$p$|-value testing whether the difference in performance is equal to zero. Panels A.II and B.II condition on the overall attention paid by the account holders in the month preceding each trade. We divide the buys in two groups—low and high—based on overall attention and report the performance of the the low- and high-attention buys, respectively. We repeat the same procedure to compute low- and high-attention sells. For every horizon, we report the difference in performance between: (i) high-attention buys and high-attention sells; (ii) low-attention buys and low-attention sells; (iii) high-attention buys and low-attention buys; and (iv) high-attention sells and low-attention sells. In each case, we also report the |$p$|-value testing whether the difference in performance is equal to zero. All |$p$|-values are based on the bootstrap procedure suggested by Barber, Lyon, and Tsai (1999). Estimates marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The results in Table 10 do not account for trading fees. Barber and Odean (2000) show that transaction costs can substantially reduce the post-fee returns of individual investors, as the average total cost of a round-trip trade was approximately 4% in 1999. Thanks to technological progress and increased competition among brokers, trading fees are much lower nowadays: it is possible to trade many ETFs without incurring any fee, and the majority of the brokers charge less than |${\$}$|10 for online trades—a very small amount compared with the average trade size of |${\$}$|16,000 reported in Table 3. Furthermore, brokers often offer their customers promotions whereby trading fees are waived for a certain number of trades and/or a certain period of time. Because we do not observe these promotions in our data set, we assume that every trade is charged the maximum fee advertised by our broker and reestimate Equation (6) using abnormal DGTW returns adjusted for trading fees. Compared to Table 10 and Online Table 9, the coefficients in Online Table 13 are only slightly smaller, but still strongly significant, indicating that controlling for trading fees does not affect our results. Table 13 Attention, attention specialization, and portfolio performance: cross-sectional results Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 This table reports regression results on the relation between portfolio performance and investor attention specialization. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta_1 \ {Attention_i} +\beta_2 \ Herfindahl\_Researched_{i}+ \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the total attention spent on the brokerage account website by account holder |$i$| over the sample period, |$Herfindahl\_Researched_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. Attention is measured as the log of the total number of minutes spent on the brokerage account website in panel A, the log of the total number of minutes spent on the Research pages of the brokerage account website in panel B, and the log of the total number of minutes spent on the Balances and Positions pages of the brokerage account website in panel C. Each panel includes a group of covariates that control for investor demographic characteristics, portfolio holdings, and trading activity variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account; Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 13 Attention, attention specialization, and portfolio performance: cross-sectional results Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 This table reports regression results on the relation between portfolio performance and investor attention specialization. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta_1 \ {Attention_i} +\beta_2 \ Herfindahl\_Researched_{i}+ \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the total attention spent on the brokerage account website by account holder |$i$| over the sample period, |$Herfindahl\_Researched_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. Attention is measured as the log of the total number of minutes spent on the brokerage account website in panel A, the log of the total number of minutes spent on the Research pages of the brokerage account website in panel B, and the log of the total number of minutes spent on the Balances and Positions pages of the brokerage account website in panel C. Each panel includes a group of covariates that control for investor demographic characteristics, portfolio holdings, and trading activity variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account; Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Finally, while the results in Table 10 use monthly time effects, we find that the results are virtually unchanged when we use daily time effects—as we show in Online Table 14. Table 14 Attention and performance: panel regression results by groups Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 This table reports panel regression results on the relation between portfolio performance and investor attention for five groups of account holders (Columns |$Q1$| to |$Q5$|⁠) based on the total number of seconds spent on the brokerage account website. |$Q1$| denotes the bottom quintile, that is, the least attentive group of account holders, while |$Q5$| denotes the top quintile, that is, the most attentive group of account holders. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m + \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the seconds spent on the brokerage account website. Panels A, B, and C report results for |$k=21$|⁠, |$42$|⁠, and 63 days, respectively. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics, computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 14 Attention and performance: panel regression results by groups Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 This table reports panel regression results on the relation between portfolio performance and investor attention for five groups of account holders (Columns |$Q1$| to |$Q5$|⁠) based on the total number of seconds spent on the brokerage account website. |$Q1$| denotes the bottom quintile, that is, the least attentive group of account holders, while |$Q5$| denotes the top quintile, that is, the most attentive group of account holders. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m + \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the seconds spent on the brokerage account website. Panels A, B, and C report results for |$k=21$|⁠, |$42$|⁠, and 63 days, respectively. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics, computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. 6. Attention and Performance: Evidence from Investor Trades In an effort to understand the mechanism through which higher investor attention relates to superior portfolio performance, in this section we analyze how attention affects the profitability of investors’ active management decisions, that is, their trades. We first relate the performance of the stocks bought and sold by individual investors to the attention they pay to their investment portfolio in the month preceding each trade. We take each individual’s trade as the unit of observation, and we measure performance as the risk-adjusted returns of the stock over the one-, two-, all the way to twelve-month period after the trade has been placed. The adjustment for risk is performed using the DGTW model (see Daniel et al. 1997) but the results are similar if we use simple or market-adjusted returns. We then provide results showing that the positive effect of attention is—at least in part—due to the fact that high-attention individuals purchase attention-grabbing stocks that have appreciated in the recent past and whose positive performance persists for up to six months. 6.1 Baseline parametric results We start by presenting pooled regression estimates in Table 11, testing whether there is a positive relation between investor attention and stock performance. We separate the buys and the sells, and estimate the regressions: \begin{align} DGTW\_Ret\_Buys_{i,j,t:t+k}=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \label{performance_regression}\\ \end{align} (7) \begin{align} DGTW\_Ret\_Sells_{i,j,t:t+k}=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \label{performance_regression_2} \end{align} (8) where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Note that—to make the results more interpretable—we scale |$Attention_{i,j,t}$| so that it has zero mean and unit variance. Finally, cumulative abnormal returns are computed at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panel A focuses on the returns of the buys. We find a positive relation (statistically significant at the 5% level) between the total attention investors spend on the trading platform and the performance of their trades at the two-, three-, four-, and five-month horizons. The relation is also positive, but only statistically significant at the 10% level, at the one-month horizon. The results are also economically significant. At the three-month horizon, for example, a unit-standard-deviation increase in attention increases the average annualized adjusted returns of the stocks purchased by |$0.533\%\times 4=2.13\%$|⁠. The results for the two specialized measures of attention are very much in line with the overall attention results. For example, at the three-month horizon, the effect equals |$0.489\%\times 4=1.96\%$| and |$0.408\%\times 4=1.63\%$| for research and balances and positions attention, respectively. The results for research attention and balances and positions are reported in Online Table 15. Compared with overall attention, research attention seems to have a stronger effect at the one-month horizon and a slightly smaller effect at the five-month horizon. Finally, the results for balances and positions are somewhat weaker, as the results are significant at the two- and three-month horizons, but they just miss significance at the four- and five-month horizons. Table 15 Trading performance and investor attention: regression results conditioning on stock characteristics Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 This table reports regression results on the relation between investor attention and trading performance, conditioning on the characteristics of the stocks traded. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the three—month horizon. Panel A reports results for stock purchases, and measures attention as overall attention. The results are computed separately for stocks with low and high values of the conditioning variables. The conditioning variables used are (from left to right): Volume, the volume of the stock on the day of the trade; Size, computed as the log of the market price multiplied by the number of shares outstanding; Volatility, computed as the realized volatility of the stock over the previous month; Disagreement, the standard deviation of the analysts’ earnings-per-share forecasts; Num. Analysts, the log of the number of analysts covering the stock during the quarter the trade takes place; and News, the number of news from Capital IQ over the previous month. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 15 Trading performance and investor attention: regression results conditioning on stock characteristics Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 This table reports regression results on the relation between investor attention and trading performance, conditioning on the characteristics of the stocks traded. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the three—month horizon. Panel A reports results for stock purchases, and measures attention as overall attention. The results are computed separately for stocks with low and high values of the conditioning variables. The conditioning variables used are (from left to right): Volume, the volume of the stock on the day of the trade; Size, computed as the log of the market price multiplied by the number of shares outstanding; Volatility, computed as the realized volatility of the stock over the previous month; Disagreement, the standard deviation of the analysts’ earnings-per-share forecasts; Num. Analysts, the log of the number of analysts covering the stock during the quarter the trade takes place; and News, the number of news from Capital IQ over the previous month. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Panel B reports the performance of the sells. For overall attention, we find that none of the coefficients are significant at the 5% level, and only one coefficient is significant at the 10% level—indicating that there is virtually no relation between overall attention and the performance of the stocks sold by investors. Once again, the results for research attention and balances and positions attention (Online Table 15) are very much aligned. The only exception is the relation between research attention and returns at the one- and two-month horizons, which is positive and significant at the 5% level. Overall, the results in Table 11 show that there is a positive effect of attention on the stocks purchased, but not on the stocks sold. This suggests both that the time spent on the brokerage account website is dedicated to searching for new stocks and that the investors are able to translate the information they acquire into profitable trades.24 6.2 Nonparametric results To corroborate the parametric results reported above, we now report evidence based on portfolio sorts. Before analyzing the effect of attention on trading performance, we first confirm the findings in Odean (1999) that—unconditionally—the stocks purchased by individual investors systematically underperform the ones sold. Panel A.I of Table 12 reports the average cumulative abnormal returns at the one-, two-, three-, four-, five-, six-, and twelve-month horizons after stock purchases and sales. For every horizon, we also report the difference in performance between the two groups and a bootstrap |$p$|-value testing whether the difference is equal to zero.25 We find that, at every horizon, the stocks sold outperform the ones purchased and the difference is statistically significant for three out of seven horizons. For example, the average annualized three-month abnormal return of the stocks purchased equals |$0.37\%\times4=1.48\%$|⁠, the one for the stocks sold equals |$0.91\%\times4=3.64\%$|⁠, and their difference is statistically significant with a |$p$|-value of 0.01. While in line with Odean (1999), our results are somewhat weaker, possibly due to our sample covering the 2013–2014 bull market. Panel A.II conditions the trades on the overall attention paid by investors to their investment portfolio in the month preceding each trade. We divide the buys in two groups—low and high—and report their performance. We do the same for the sells. The results indicate that overall attention is strongly related to the performance of investor trades. For low-attention trades, we find that Odean (1999)’s bias is very strong. Low-attention buys strongly underperform the low-attention sells in a statistically significant fashion: the average annualized three-month abnormal return of the stocks purchased equals |$-0.07\%\times4=-0.28\%$|⁠, the one for the stocks sold equals |$0.77\%\times4=3.08\%$|⁠, and their difference is statistically significant, with a |$p$|-value of 0.00. On the other hand, even though the bias is not overturned for high-attention trades, high-attention buys do not underperform the high-attention sells in a statistically significant fashion: the average annualized three-month abnormal return of the stocks purchased equals |$0.79\%\times4=3.16\%$|⁠, the one for the stocks sold equals |$1.05\%\times4=4.2\%$|⁠, and their difference is statistically insignificant, with a |$p$|-value of 0.29. While these results indicate that the behavioral bias first discovered by Odean (1999) is smaller for high-attention trades, it is difficult to relate this behavioral bias to portfolio performance. This is because the returns that affect the performance of the investors are the ones of the stocks they hold in their portfolio. Even if the performance of the stock an investor sells is superior to the one he or she buys, it is not clear that he or she will underperform the market as a result. This is simply because both stocks may be delivering a return superior to that of the market in the subsequent period. To assess the relation between attention and performance, it is preferable to focus on overall portfolio performance, as we do in Tables 8, 9, and 10. We also find that the high-attention buys strongly outperform the low-attention buys up to four months and the difference becomes insignificant thereafter. Economically, the difference in performance is large: the average annualized three-month difference between high- and low-attention purchases equals |$3.16\%-(-0.28\%)=3.44\%$| and is statistically significant, with a |$p$|-value of 0.00. Finally, we find no significant impact of overall attention on the performance of the stocks sold as low- and high-attention sells have similar performance. The results reported above show that, the more time investors spend on their trading account, the higher the performance of the stocks they buy. In an effort to uncover the main drivers of the results, we show below that the more investors pay attention to their brokerage account, the more they purchase attention-grabbing stocks that have appreciated in the recent past and continue to appreciate for up to six months, and this seems to be the source of their outperformance. 6.3 Understanding the results While on the brokerage account website, investors may be looking at a wide range of information, such as firms’ accounting data, news, and analysts’ reports—for example. One obvious piece of information that may attract investors’ attention is the past performance of the securities they are considering buying or selling. Moskowitz, Ooi, and Pedersen (2012) document time-series momentum, that is, the positive predictability from a security’s own past returns that persists for up to twelve months. To test whether time-series momentum can explain our results, we conduct two complementary exercises. First, we estimate the relation between investors’ attention and the past performance of the stocks they trade using a nonparametric symmetric nearest neighbor approach. Second, we repeat the analysis contained in panel A of Table 12, but focus on the performance of the stocks before the trades occur, rather than after. The results of this analysis are reported in panel B of Table 12. Starting from the unconditional results, panel B.I shows that investors tend to both buy and sell stocks that have appreciated in the past, as shown in Odean (1999). Panel B.II sharpens the results, as it uncovers a very strong relation between the attention spent by investors and the risk-adjusted returns of the stocks they trade. For example, at the one-month horizon, panel B.II of Table 12 shows that the average past performance of the high-attention buys (sells) equals 3.13% (3.55%), the past performance of the low-attention buys (sells) equals 0.70% (1.19%), and the difference in performance between the high- and low-attention buys (sells) is statistically significant, with a |$p$|-value equal to 0.00 (0.00). For the same one-month horizon, panel A of Figure 3 displays a very strong positive and virtually monotonic relation between log attention and past performance.26 The remaining panels of Figure 3 and the remaining columns of Table 12, panel B.II, show that the pattern is very robust across horizons. Figure 3 View largeDownload slide Investor attention and past performance of stocks traded This figure reports the relation between investors’ attention and the past performance of the stocks they trade using a nonparametric symmetric nearest neighbor approach. Attention is computed as the log total number of seconds spent by each investor over the month preceding each trade. Stocks’ past performance is computed over the previous month in the top-left panel, the previous three months in the top-right panel, the previous six months in the bottom-left panel and the previous twelve months in the bottom-right panel. In each panel, the trades are divided into stock purchases and stock sales. The past performance of the stocks purchased is represented by a solid red line, while the past performance of the stocks sold is represented by a dashed black line. Figure 3 View largeDownload slide Investor attention and past performance of stocks traded This figure reports the relation between investors’ attention and the past performance of the stocks they trade using a nonparametric symmetric nearest neighbor approach. Attention is computed as the log total number of seconds spent by each investor over the month preceding each trade. Stocks’ past performance is computed over the previous month in the top-left panel, the previous three months in the top-right panel, the previous six months in the bottom-left panel and the previous twelve months in the bottom-right panel. In each panel, the trades are divided into stock purchases and stock sales. The past performance of the stocks purchased is represented by a solid red line, while the past performance of the stocks sold is represented by a dashed black line. Past returns are just one attention-grabbing proxy. Another proxy proposed in the literature is abnormal volume; see Gervais, Kaniel, and Mingelgrin (2001). Gervais, Kaniel, and Mingelgrin (2001) find evidence that stocks experiencing unusually high volume tend to appreciate for up to five to six months. Finally, we consider the number of news events. Past returns, abnormal volume, and number of news events are all attention-grabbing proxies, but have slightly different flavors. The first relates to the past returns of the stock. The second relates to how much the stock is traded. The third relates to how much the company is covered in the news. To assess which one better explains our attention results, we first estimate univariate regressions of the attention spent before any given trade on each proxy. We then include all proxies together and conduct a horse race between them. We report the results in Online Table 18 separately for buys (panel A) and sells (panel B). To ease the comparison among the estimated coefficients, we standardize the regressors so that they have zero mean and unit variance. The results in panel A show that, the higher the attention of a stock purchase, the more it relates to an attention-grabbing stock. The coefficient is positive and significant for all proxies. When we include all proxies in the same specification, we find that past abnormal returns is the most significant regressor, with a |$t$|-statistic of 4.53. The coefficient on abnormal volume has a |$t$|-statistic of 2.32 and a coefficient that is half the magnitude. Finally, past news is negative and barely significant. The results in panel B are similar, but abnormal returns and abnormal volume have a similar relation to the attention associated with the stocks sold. To test whether the relation between attention and performance remains significant after the inclusion of attention-grabbing proxies, we reestimate the results in Table 11 including abnormal volume and news events as additional controls. We find that the coefficient estimates and their significance remain unchanged. These results—in conjunction with the ones contained in Table 11 and panel A of Table 12—paint the following picture. Investors who pay a lot of attention to their trading accounts tend to trade attention-grabbing stocks that have appreciated greatly in the past. The good past performance persists into the future for the purchases, as we find that the high-attention buys significantly outperform the low-attention buys up to four to five months. The outperformance is relatively short-lived, however, as we find it disappears for horizons greater than five months. 7. Extensions and Additional Analyses In the previous sections we showed that higher attention is related to superior performance. We now provide a set of additional analyses to further understand our main findings. We start by providing extensions of the cross-sectional performance regressions and show a positive relation between specialization in information acquisition and investment performance. We continue by providing extensions of the baseline panel results and show that the positive relation between attention and performance is mainly driven by the positive effect of attention for those investors that, in general, are rather inattentive. Finally, we provide extensions of the baseline trading results and show that paying attention is particularly profitable when trading stocks that have high market capitalization, trading volume, volatility, number of analysts, dispersion of analyst forecasts, and news. 7.1 Specialization in information acquisition and investment performance The baseline cross-sectional performance regressions show there is a positive relation between attention and investment performance. In this section, we dig deeper and assess whether the degree of specialization in information acquisition also has an impact on investment performance. We focus only on those individuals who acquire stock specific information over the sample, and we measure the degree of specialization using the Herfindahl index of the stocks researched. For investor |$i$|⁠, this quantity is computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention of investor |$i$|—measured in seconds—allocated to stock |$k$| over the full sample and |$K$| is the total number of stocks researched. The measure is bounded between 0 —attention allocated evenly across all assets—and 1—all attention concentrated in one asset. We then add this regressor to the baseline specification in Equation (5) and estimate: \begin{align} AVG\_DGTW\_Ret_{i}&=\alpha +\beta_1 \ {Attention_i} + \beta_2 \ {Herfindahl\_Researched_i} \notag \\ &\quad + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N, \label{cross_spec_extension} \end{align} (9) where |$Herfindahl\_Researched_i$| is the normalized Herfindahl index of the stocks researched by investor |$i$| over the sample and the remaining regressors are the same as in Equation (5). The regression estimates, reported in Table 13, suggest two main findings. First, even when we estimate our results on the subset of investors that collect stock-specific information on the brokerage account website, the effect of the covariate |$Attention$| is positive and significant. This is true if we use Overall, Research or Balances and Positions attention. Second, the more investors specialize their attention, that is, the higher the Herfindahl index of the stocks researched, the superior the performance. The results highlight that, for a given level of attention, those investors that specialize and focus their research efforts on specific stocks perform better, compared with those who evenly allocate their attention across the stocks in their information set. Economically, the magnitudes are large. For overall attention (panel A), the |$\beta_2$| coefficient equals 1.861% and is significant at the 1% level. The corresponding coefficient estimates for research (panel B) and balances and positions (panel C) are 2.231% and 1.639%, respectively. As a final exercise, we estimate the effect of paying attention for those that display more and less specialization in information acquisition. To this end, we divide the investors for which we have stock-specific attention in two groups on the basis of the Herfindahl index of the stocks they research and reestimate Specification 5 separately for the two groups. The coefficient of interest |$\beta$| equals 2.40 for the less specialized individuals and 3.92 for the more specialized individuals. If we repeat the dollar gains per hour calculations associated with Table 8, we find that the effects are economically quite large. For the low-specialization investors, they equal |${\$}94,000 \times 2.40\%/88 = {\$}25.6$|⁠. For the high-specialization investors, they equal |${\$}94,000 \times 3.92\%/88 = {\$}41.66$|⁠.27 7.2 Panel portfolio results by investor type To assess whether the effects estimated in Table 10 are driven by those investors that—on average— pay a lot of attention or by those that rarely pay attention, we divide our account holders in five quintiles based on their overall total attention over the sample and—for each group—we reestimate Equation (6). Table 14 presents the results for the 21-, 42-, and 63-day horizons in panels A, B, and C, respectively. Moving from left to right, the results for Columns Q1 through Q5 report estimates for account-holders with greater degrees of overall attention. For the shortest horizon of 21 days (panel A), only the investors with low to medium attention (Q1 through Q3) seem to benefit from paying more attention. The effect is instead not statistically significant for high-attention investors (Q4 and Q5). Furthermore, the coefficients are monotonically decreasing as we move from low- attention to high-attention individuals. The results in panels B and C display similar patterns. The coefficients on abnormal attention decrease as average attention increases and become negative for the investors who pay the most attention. Also, the coefficients for the first four groups are all consistently significant at the 5% level. For the last group, on the other hand, we never find a significant effect of attention on performance. Taken together, the results in Table 14 uncover diminishing returns to abnormal attention. Abnormal attention for those investors who rarely pay attention to their account seems to improve their portfolio allocation. On the other hand, those who tend to spend a lot of time on their brokerage account do not seem to benefit from additional time researching stocks and worrying about their portfolio positions. 7.3 Investor trades results controlling for stock characteristics To sharpen the results in Table 11 and understand under what circumstances it pays the most to pay attention, we condition the performance of the trades on the characteristics of the stocks traded. We use the volume of the stock on the day of the trade, its volatility, and the disagreement between analysts’ earnings-per-share forecasts as alternative measures of valuation uncertainty. We use the market capitalization of the company, the number of analysts, and the number of news as proxies for the amount of public information available when placing a trade. For each conditioning variable we divide the trades in two groups: the low group contains the trades associated with firms whose characteristic is below the median; the high group contains the trades associated with firms whose characteristic is above the median. We then reestimate Equations (7) and (8) separately for each group. The results, displayed in Table 15, are based on the three-month horizon and are reported separately for stock purchases (panel A) and sales (panel B).28 A clear pattern emerges from panel A. Attention is associated with higher future returns when account holders trade companies with high valuation uncertainty, that is, stocks with high volume, volatility, and analysts’ disagreement. The same is true for companies with a larger amount of public information, that is, stocks with greater market capitalization, number of analysts, and number of news events. Statistically, all coefficients in the high group are significant at the 1% level, while only one coefficient is significant in the low group—the one associated with volatility. The two groups also differ in terms of economic significance. For example, a one-standard-deviation increase in attention leads to a |$0.764\% \times 4= 3.06\%$| increase in future adjusted returns for stocks with high market capitalization, while for stocks with low market capitalization the increase is |$0.261\% \times 4= 1.04\%$|⁠. While weaker than the results for the buys, attention seems to be related to the performance of the sells for stocks with high uncertainty and high levels of public information. For two conditioning variables out of five—market capitalization and number of analysts—the coefficients are significant at the 1% level for the high group. Furthermore, for high analysts’ disagreement and news, the coefficients on attention are significant at the 5% and 10% levels, respectively. The fact that higher attention is related to greater future performance after stock sales, combined with the known fact that investors rarely realize losses, possibly suggests that—in situations of high uncertainty —paying high attention leads investors to sell their stocks too early, while they are still appreciating. None of the coefficients for the low group are significant at the 1% level, and only one coefficient—the one for volatility—is significant at the 10% level. 8. Conclusions We use a novel brokerage account data set to study how individual investors pay attention to their investment portfolio and how investor attention relates to investment performance. We find a very large heterogeneity in attention across investors that can be explained by the size and risk of the investors’ portfolios, as well as by investor trading habits and demographic characteristics. We also uncover a large heterogeneity in the degree of specialization in information choice across investors. On average, investors research thirty-one individual stocks per year. The number of stocks they research is positively related to the overall amount of time they spend on the website, the number of stocks in their portfolio, and the number of stocks they trade. We also find that investors who hold fewer stocks, have more concentrated portfolios, trade less, and have a smaller fraction of their portfolio in cash allocate their attention in a more concentrated fashion. Finally, we find that investors pay more attention to local stocks, stocks with a higher weight in their portfolio, stocks that have a higher squared Sharpe ratio, and stocks of companies with a larger market capitalization—as predicted by Van Nieuwerburgh and Veldkamp (2009, 2010). We study the relation between investor attention and investment performance both at the portfolio returns level and at the individual trades level. We find a strong and positive cross-sectional relation between attention and performance, in that more attentive investors achieve higher portfolio risk-adjusted returns and Sharpe ratios, even after controlling for covariates related to investment style. Using panel regressions that control for investor skills using fixed effects, we also show that periods of higher attention are related to superior future portfolio performance. We find similar results when we focus on the performance of individual trades. Attention is positively related to the future performance of the stocks purchased up to four months after the trade is placed. We find—on the other hand—no discernible effect of attention on the performance of the stocks sold. To understand the economic mechanism relating attention and trading profitability, we conduct a number of auxiliary exercises. First, by analyzing the characteristics of the stocks before they are traded, we show that the superior performance of high-attention investors arises because they purchase attention-grabbing stocks that have appreciated in the recent past and whose positive performance persists for up to six months. Second, we show that attention is particularly profitable when investors trade stocks with high market capitalization, trading volume, volatility, number of analysts, dispersion of analyst forecasts, and news—indicating that it is for the stocks with high uncertainty, but for which a lot of public information is available, that it pays to pay attention. Our study contributes to the literature that studies the behavior of individual investors with information capacity constraints, in that we provide empirical evidence on how investors allocate their attention. We also contribute to the literature that studies the performance of individual investors. While the majority of the literature focuses on investor mistakes, we show that paying attention relates to better investment decisions. We would like to thank Stijn Van Nieuwerburgh (the editor) and two anonymous referees for their comments and suggestions. The paper has benefited from the comments made at presentations at the NBER 2016 behavioral meeting, the 2017 Conference on Financial Decisions and Asset Markets at Wharton, the 2017 CEPR European Summer Symposium in Financial Markets—Gerzensee, the 2017 WFA Annual Meeting, the 2017 Barcelona GSE Summer Forum, the 2017 Finance Down Under Conference at the University of Melbourne, the 2017 EFA Annual Meeting, the 2017 ITAM Finance Conference, the 2017 FSU SunTrust Beach Conference, the 2017 FMA Asia/Pacific Annual Meeting, the 2017 China International Conference in Finance, the 2016 HEC-McGill Winter Finance Workshop, Georgetown University, the R. H. Smith School of Business at the University of Maryland, the University of Melbourne, and the Johns Hopkins Carey Business School. We are grateful to Sumit Agarwal, Santosh Anagol, Daniel Andrei, James Ang, Ilona Babenko, Gurdip Bakshi, Turan Bali, Federico Bandi, Jonathan Berk, Brad Barber (the NBER discussant), Jules van Binsbergen, Peter Bossaerts, John Campbell, Maria Cecilia Bustamante, Yingmei Cheng, Carole Comerton-Forde, Max Croce, Henrik Cronqvist, Julien Cujean, Francesco D’Acunto, Zhi Da, Sandeep Dahiya, Alexander David, Douglas Diamond, Allan Eberhart, Joey Engelberg, Stephen Figlewski, Laurent Fresard, Nicola Fusari, Pengjie Gao (the FSU SunTrust Beach Conference discussant), Xiaohui Gao, Bruce Grundy, Jaehoon Hahn (the CICF discussant), Samuel Hartzmark, Michael Hasler, Xing Huang (the ITAM Finance Conference discussant), Irena Hutton, Ryan Israelsen (the WFA discussant), Pete Kyle, April Knill, Mattia Landoni (the CEPR discussant), Dmitry Livdan, Mark Loewenstein, Spencer Martin, Rich Mathews, Gonzalo Maturana, David McLean, Vladimir Mukharlyamov, Will Mullins, Carsten Murawski, Federico Nardari, Marina Niessner (the Conference on Financial Decisions and Asset Markets discussant), Greg Nini, Lubos Pastor, Elena Pikulina, Nagpurnanand Prabhala, Tarun Ramadorai, Rodney Ramcharan, Steven Riddiough, Nikolai Roussanov, Shrihari Santosh, Philipp Schnabl, Andrei Shleifer, Stephan Siegel, Kelly Shue, Eric So, David Solomon, Zhaogang Song, Juan Sotes-Paladino, Chester Spatt, Michael Ungeheuer (the EFA discussant), Stephen Utkus (the Conference on Financial Decisions and Asset Markets discussant), Laura Veldkamp, Katherine Waldock, Quan Wen, Joakim Westerholm, Russ Wermers, Fernando Zapatero, Yeqin Zeng (the FMA discussant) and Eric Zwick for comments and suggestions. Antonio Gargano acknowledges support from the Faculty Research Grant funded by the University of Melbourne. Supplementary data can be found on The Review of Financial Studies Web site. Appendix A. Data Construction In this appendix, we describe the auxiliary data sets we use in our study. As mentioned in Section 2.2, our data source gave us access to data structured as an SQL relational database. Besides the Web-activity table described at length in Section 2, this study uses four additional tables named Trades, Clients, Accounts, and Account Holdings, respectively. Finally, the study also uses information from a variety of standard data sources. Further details are provided below. Trades The Trades table includes the record of all the trades made by account holders over the period January 2010 through June 2014. This table contains information regarding 3,528,001 accounts. The Trades table has a total of 197,870,535 observations, and each observation contains the following information: Acct_id, a unique numeric account identifier; Client_id, a unique numeric client identifier; Ord_ts, the time stamp of the trade order; Exec_ts, the time stamp of the trade execution; Scrty_type_descr, denoting whether the security traded is a stock, a bond, an option, or a mutual fund; Cusip_id, the CUSIP code of the security traded; Action, denoting whether the action is a buy or a sell; Trd_prncpl_amt, the amount traded; Trd_qty, the number of stocks traded; Chnl, denoting whether the trade is web-based or phone based. In our sample, 99% of the trades are web-based, showing how much the broking business has changed compared with the Barber and Odean (2002) data. The Trades table also contains information on the type of account associated with a trade. This variable is named Acct_type and takes on 61 values such as 401K, 403B, IRA, and many others. In much of the analysis performed in the paper, we group individual brokerage accounts and Joint Tenants with Right of Survivorship (JTWROS) accounts as one broad category that comprises 57.6% of all accounts in our data. Clients The Clients table contains information on the characteristics of the clients. This file has 2,812,877 observations. Comparing the total number of accounts reported above with the total number of clients shows that many clients have more than one account. In fact, 83.9% of the clients have only one account, 2.5% of them have two accounts, and 3.5% of them have three or more accounts. Clients that have multiple accounts usually have an individual account and an IRA account. Out of the Clients table, we use the following variables: client_id, a unique numeric client identifier; and Client_age, the age of the account holder. Accounts The Accounts table includes data on the characteristics of the accounts. This file has 3,528,001 observations, and for each observation, we make use of the following variables: Acct_id, a unique numeric account identifier; Client_id, a unique numeric client identifier; Gender, the gender of the account holder; Stnd_pstl_cd, the zip code of the account holder; Acct_open_dt, the account opening date; and Acct_close_dt, the account closing date. Account Holdings The Account Holdings table includes quarterly holdings for every account in the data set. This file has 194,438,993 observations and the following variables: Acct_id, a unique numeric account identifier; mkt_close_dt, the date of the holdings snapshot; Cusip_id, the CUSIP code of the security held by the account holder; Scrty_type_descr, denoting whether the security held is a stock, a bond, an option, or a mutual fund; Qty, the quantity held; and Amt, the dollar value of the quantity held. The Account Holdings table also contains cash-holdings information. We construct account holdings at the daily frequency by merging the Account Holdings table with the Trades table. Additional Data Sources Stock market information such as prices, returns and trading volumes—among others—is obtained from CRSP, CRSP OTC, and CRSP Mutual Funds. Stocks’ accounting information is obtained from Compustat. Benchmark returns are obtained from the Fama-French website and by constructing DGTW returns at the daily frequency. News concerning stocks are obtained from Capital IQ. Analysts’ information is obtained from I/B/E/S. Finally, information regarding zip-codes’ latitude and longitude is obtained from Census. Construction of the Final Data Set The final data set is obtained in two steps. In the first, we merge the contents of the Web-activity, Trades, Clients, Accounts and Account Holdings tables using the acct_id identifier. In the second, we merge the resulting data set with the additional data sources using either the stocks’ Cusip_id or ticker as identifiers. Footnotes 1 As reported in Agnew, Balduzzi, and Sunden (2003), the distribution of allocations to stocks across 401K investors is strongly bimodal: 48% of the average annual equity allocations are zero, while 22% are 100%. 2 In compliance with the U.S. privacy law, no Personally Identifiable Information (PII) was provided by the brokerage house. For example, each account holder has been anonymized using a numeric account identifier. 3 The results for the total number of pages visited and the total number of logins are very similar. 4 The total number of news items pertaining to the stocks in the S&P 500 Index are obtained from Capital IQ, while we downloaded the S&P 500 trading volume from Yahoo! Finance. 5 These categories are the ones that the brokerage account website is divided into and allow us to categorize 99% of the URLs. 6 The sample is dictated by data availability, that is, the complete URLs that include ticker information are available from October 1, 2013, until the end of our sample. 7 Even though these accounts were randomly selected from the full set of accounts of the brokerage account house, we allay concerns regarding their representativeness by recomputing the statistics in Table 3 using the entire universe of accounts of the brokerage account house (approximately 3.5 million). The results, reported in Online Table 1, show that the biographic and portfolio characteristics statistics are very similar across the two samples. Because our brokerage house has millions of clients, our sample is likely to be representative of the population of U.S. stock investors. 8 All dollar figures have been rounded to the nearest thousand upon request of the data provider. 9 For ease of visualization, we winsorize the number of minutes at the 95th percentile, which results in the number of winsorized minutes to range from 0 to 150. The summary statistics reported in Tables 3 are computed using nonwinsorized data. 10 More precisely, |$Attention_i$| is computed as |$Attention_i=log(1+minutes_i)$|⁠. We use the same formula for all the logged attention measures in the paper. 11 We do not standardize the dummy variable regressors Male and Brokerage. 12 Because we are estimating a log-linear regression, the economic interpretation of each coefficient |$\beta_k$| is computed as |$e^{\beta_k}-1$|⁠, and is interpreted as the percentage change in the number of minutes spent on the brokerage account website when the |$k$|-th regressor increases by one standard deviation. Finally—because all regressors have been de-meaned— the number of minutes spent on the brokerage account website by non-male and non–brokerage account holders, that are average in terms of all the other conditioning variables, represent our “base-case investor.” The number of minutes the base-case investor spends on the brokerage account website can be computed as |$e^{\hat{\alpha}} \times N^{-1} \sum_{i=1}^N e^{\hat{\epsilon_{i}}}$|⁠, where |$\hat{\alpha}$| and |$\hat{\epsilon_{i}}$| are obtained from Equation (1); see Duan (1983). 13 Computing the squared Sharpe ratio over the previous 42 or 63 months leaves the results unchanged. 14 We use the standard formula for computing the distance in miles between account holder |$i$| and firm |$j$|⁠, as follows: |$dist (i, j) = arccos\Big(cos(lat_{i})cos(long_{i})cos(lat_{j}) cos(long_{j}) + cos(lat_{i})sin(long_{i})cos(lat_{j})sin(long_{j})+ sin(lat_{i}) sin(lat_{j})\Big) r,$| where |$lat$|/|$long$| are the latitudes/longitudes of location |$i$| and location |$j$| (expressed in radians), respectively, and |$r$| denotes the radius of the earth (approximately 3,963 miles). 15 We obtained the total number of news for each stock from Capital IQ. 16 The market value of assets is computed as the sum of the closing stock price (Compustat item: PRCCQ) multiplied by the common shares (Compustat item: CSHPRQ), debt in current liability (Compustat item: DLCQ), long-term debt (Compustat item: DLTTQ), and preferred stocks (Compustat item: PSTKQ), minus deferred taxes and investment tax credit (Compustat item: TXDITCQ). 17 Following Frank and Goyal (2003), we replace R&D with 0 if the entry related to research and development expense is missing. 18 This is mainly due to the fact that Turnover, Volume, Analyst, and News are correlated with each other. As expected, the exclusion of three out of four covariates dramatically boosts the economic and statistical significance of the fourth covariate. It is nonetheless interesting to note that News has a positive and statistically significant impact—even after controlling for the other three regressors. 19 The DGTW-adjusted portfolio return is computed as in Equation (1) of Wermers (2003) and in Equation (1) of Daniel et al. (1997). 20 Compared with the covariates in Table 4 of Section 4.1, we do not include the regressors related to risk-factor exposures, that is, the loadings on the market, small-minus-big, high-minus-low and momentum factors, because these exposures are directly controlled for by the DGTW procedure. 21 The annual income figures are taken from Khorunzhina (2013), and use 2007 PSID data. 22 While this average Sharpe ratio may seem rather large, note that—for the same period—the market Sharpe ratio (computed using returns and volatility on a NYSE/AMEX/NASDAQ value-weighted index) has been 2.62. 23 The DGTW-adjusted portfolio return is computed as in Equation (1) of Wermers (2003) and in Equation (1) of Daniel et al. (1997). The results are economically similar if we use simple returns or market-adjusted returns. 24 Online Table 16 shows that the results are similar, but less significant, when we measure attention using number of pages or logins instead of seconds. Online Table 17 shows that the results are economically and statistically stronger when we use market-adjusted, rather than DGTW-adjusted, abnormal returns. 25 All |$p$|-values in this section are computed using the bootstrap procedure suggested by Barber, Lyon, and Tsai (1999) with 10,000 bootstrap iterations. 26 These plots exclude the top five percentiles of log-attention, because the data is more sparse in that region of the support, and the nearest neighbor estimates more erratic. 27 The fact that both the coefficients on low- and high-specialization individuals are higher than the corresponding coefficient of 2.33 reported in Table 8 is an indication that investors that look at stock-specific information benefit more from paying attention compared with those that don’t. 28 The results are qualitatively the same when we use one-month to five-month return horizons. References Abel, A. B. , Eberly J. C. , and Panageas S . 2007 . Optimal inattention to the stock market. American Economic Review 92 : 244 – 49 . Google Scholar Crossref Search ADS Abel, A. B. , Eberly J. C. , and Panageas S . 2013 . Optimal inattention to the stock market with information costs and transactions costs. Econometrica 81 : 1455 – 81 . Google Scholar Crossref Search ADS Agnew, J. , Balduzzi P. , and Sunden A . 2003 . Portfolio choice and trading in a large 401(k) plan. American Economic Review 93 : 193 – 215 . Google Scholar Crossref Search ADS Alvarez, F. , Guiso L. , and Lippi F . 2012 . Durable consumption and asset management with transaction and observation costs. American Economic Review 102 : 2272 – 2300 . Google Scholar Crossref Search ADS Amaya, D. , Christoffersen P. , Jacobs K. , and Vasquez A . 2015 . Does realized skewness predict the cross-section of equity returns? Journal of Financial Economics 118 : 135 – 67 . Google Scholar Crossref Search ADS Andrei, D. , and Hasler M . 2015 . Investor attention and stock market volatility. Review of Financial Studies 28 : 34 – 72 . Google Scholar Crossref Search ADS Barber, B. M. , Lyon J. D. , and Tsai C.-L . 1999 . Holding size while improving power in tests of long-run abnormal stock returns. Journal of Finance 54 : 165 – 2020 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2000 . Trading is hazardous to your wealth: The common stock investment performance of individual investors. Journal of Finance 55 : 773 – 806 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2001 . Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics 116 : 261 – 92 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2002 . Online investors: Do the slow die first? Review of Financial Studies 15 : 455 – 87 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2007 . All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies 21 : 785 – 818 . Google Scholar Crossref Search ADS Calvet, L. E. , Campbell J. Y. , and Sodini P . 2007 . Down or out: Assessing the welfare costs of household investment mistakes. Journal of Political Economy 115 : 707 – 47 . Google Scholar Crossref Search ADS Da, Z. , Engelberg J. , and Gao P . 2011 . In search of attention. Journal of Finance 66 : 1461 – 99 . Google Scholar Crossref Search ADS Daniel, K. , Grinblatt M. , Titman S. , and Wermers R . 1997 . Measuring mutual fund performance with characteristic-based benchmarks. Journal of Finance 52 : 1035 – 58 . Google Scholar Crossref Search ADS Driscoll, J. C. , and Kraay A. C . 1998 . Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics 80 : 549 – 60 . Google Scholar Crossref Search ADS Duan, N. 1983 . Smearing estimate: A nonparametric retransformation method. Journal of the American Statistical Association 78 : 605 – 10 . Google Scholar Crossref Search ADS Frank, M. Z. , and Goyal V. K . 2003 . Testing the pecking order theory of capital structure. Journal of Financial Economics 67 : 217 – 48 . Google Scholar Crossref Search ADS Frydman, C. , Hartzmark S. M. , and Solomon D. H . 2017 . Rolling mental accounts. Review of Financial Studies 31 : 362 – 97 . Google Scholar Crossref Search ADS Gabaix, X. , and Laibson D . 2002 . The 6d bias and the equity-premium puzzle. In NBER Macroeconomics Annual 2001, Volume 16 , 257 – 330 . Cambridge, MA : MIT Press . Gervais, S. , Kaniel R. , and Mingelgrin D. H . 2001 . The high-volume return premium. Journal of Finance 56 : 877 – 919 . Google Scholar Crossref Search ADS Grinblatt, M. , and Keloharju M . 2001 . How distance, language, and culture influence stockholdings and trades. Journal of Finance 56 : 1053 – 73 . Google Scholar Crossref Search ADS Grinblatt, M. , Keloharju M. , and Linnainmaa J . 2012 . IQ, trading behavior, and performance. Journal of Financial Economics 104 : 339 – 92 . Google Scholar Crossref Search ADS Guiso, L. , and Jappelli T . 2006 . Information acquisition and portfolio performance. Working Paper . Hartzmark, S. M. 2014 . The worst, the best, ignoring all the rest: The rank effect and trading behavior. Review of Financial Studies 28 : 1024 – 59 . Google Scholar Crossref Search ADS Huang, L. , and Liu H . 2007 . Rational inattention and portfolio selection. Journal of Finance 62 : 1999 – 2040 . Google Scholar Crossref Search ADS Huberman, G. 2001 . Familiarity breeds investment. Review of Financial Studies 14 : 659 – 80 . Google Scholar Crossref Search ADS Ivkovic, Z. , Sialm C. , and Weisbenner S . 2008 . Portfolio concentration and the performance of individual investors. Journal of Financial and Quantitative Analysis 43 : 613 – 56 . Google Scholar Crossref Search ADS Ivkovic, Z. , and Weisbenner S . 2005 . Local does as local is: Information content of the geography of individual investors’ common stock investments. Journal of Finance 60 : 267 – 306 . Google Scholar Crossref Search ADS Kacperczyk, M. , Van Nieuwerburgh S. , and Veldkamp L . 2016 . A rational theory of mutual funds’ attention allocation. Econometrica 84 : 571 – 626 . Google Scholar Crossref Search ADS Karlsson, N. , Loewenstein G. , and Seppi D . 2009 . The ostrich effect: Selective attention to information. Journal of Risk and Uncertainty 38 : 95 – 115 . Google Scholar Crossref Search ADS Khorunzhina, N. 2013 . Structural estimation of stock market participation costs. Journal of Economic Dynamics and Control 37 : 2928 – 42 . Google Scholar Crossref Search ADS Korniotis, G. M. , and Kumar A . 2011 . Do portfolio distortions reflect superior information or psychological biases? Review of Economics and Statistics 93 : 244 – 65 . Google Scholar Crossref Search ADS Korniotis, G. M. , and Kumar A . 2013 . Do portfolio distortions reflect superior information or psychological biases? Journal of Financial and Quantitative Analysis 48 : 1 – 45 . Google Scholar Crossref Search ADS Li, J. , and Yu J . 2012 . Investor attention, psychological anchors, and stock return predictability. Journal of Financial Economics 104 : 401 – 19 . Google Scholar Crossref Search ADS Madrian, B. C. , and Shea D. F . 2001 . The power of suggestion: Inertia in 401(k) participation and savings behavior. Quarterly Journal of Economics 116 : 1149 – 87 . Google Scholar Crossref Search ADS Massa, M. , and Simonov A . 2006 . Hedging, familiarity and portfolio choice. Review of Financial Studies 19 : 633 – 85 . Google Scholar Crossref Search ADS Moskowitz, T. J. , Ooi H. Y. , and Pedersen L . 2012 . Time series momentum. Journal of Financial Economics 228 – 50 . Nicolosi, G. , Peng L. , and Zhu N . 2009 . Do individual investors learn from their trading experience? Journal of Financial Markets 317 – 38 . Odean, T. 1998a . Are investors reluctant to realize their losses? Journal of Finance 53 : 1775 – 98 . Google Scholar Crossref Search ADS Odean, T. 1998b . Volume, volatility, price, and profit when all traders are above average. Journal of Finance 53 : 1887 – 934 . Google Scholar Crossref Search ADS Odean, T. 1999 . Do investors trade too much? American Economic Review 89 : 1279 – 98 . Google Scholar Crossref Search ADS Peng, L. , and Xiong W . 2006 . Investor attention, overconfidence and category learning. Journal of Financial Economics 80 : 563 – 602 . Google Scholar Crossref Search ADS Peress, J. 2004 . Wealth, information acquisition, and portfolio choice. Review of Financial Studies 17 : 879 – 914 . Google Scholar Crossref Search ADS Petersen, M. A. 2009 . Estimating standard errors in finance panel data sets: Comparing approaches. Review of financial studies 22 : 435 – 80 . Google Scholar Crossref Search ADS Seasholes, M. S. , and Wu G . 2007 . Predictable behavior, profits, and attention. Journal of Empirical Finance 14 : 590 – 610 . Google Scholar Crossref Search ADS Sialm, C. , Starks L. T. , and Zhang H . 2015 . Defined contribution pension plans: Sticky or discerning money? Journal of Finance 70 : 805 – 38 . Google Scholar Crossref Search ADS Sicherman, N. , Loewenstein G. , Seppi D. , and Utkus S . 2016 . Financial attention. Review of Financial Studies 29 : 863 – 97 . Google Scholar Crossref Search ADS Van Nieuwerburgh, S. , and Veldkamp L . 2009 . Information immobility and the home bias puzzle. Journal of Finance 64 : 1187 – 215 . Google Scholar Crossref Search ADS Van Nieuwerburgh, S. , and Veldkamp L . 2010 . Information acquisition and under-diversification. The Review of Economic Studies 77 : 779 – 805 . Google Scholar Crossref Search ADS Vlastakis, N. , and Markellos N. R . 2012 . Information demand and stock market volatility. Journal of Banking & Finance 36 : 1808 – 21 . Google Scholar Crossref Search ADS Von Gaudecker, H.-M. 2015 . How does household portfolio diversification vary with financial literacy and financial advice? Journal of Finance 70 : 489 – 507 . Google Scholar Crossref Search ADS Wermers, R. 2003 . Is money really smart? New evidence on the relation between mutual fund flows, manager behavior, and performance persistence. Working Paper . Yuan, Y. 2015 . Market-wide attention, trading, and stock returns. Journal of Financial Economics 548 – 64 . © The Author(s) 2018. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Review of Financial Studies Oxford University Press

Does It Pay to Pay Attention?

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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0893-9454
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10.1093/rfs/hhy050
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Abstract

Abstract We employ a novel brokerage account data set to investigate which individual investors are the most attentive, how investors allocate their attention, and the relation between investor attention and performance. Attention is positively related to investment performance, at both the portfolio return level and the individual trades level. We provide evidence that the superior performance of high-attention investors arises because they purchase attention-grabbing stocks whose positive performance persists for up to six months. Finally, we show that paying attention is particularly profitable when trading stocks with high uncertainty, but for which a lot of public information is available. Received April 24, 2017; editorial decision February 14, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. Traditional asset pricing models assume that investors continuously incorporate all the available information into their investment decisions. In reality, however, attention is a scarce resource and individuals display limited attention. Rational models argue that attention-constrained investors should benefit from paying attention and should pay attention up to the point where the improvement in investment performance equals the costs of information acquisition (see, among others, Peress 2004; Peng and Xiong 2006; Van Nieuwerburgh and Veldkamp 2009, 2010; Kacperczyk, Van Nieuwerburgh, and Veldkamp 2016). The behavioral literature, on the other hand, shows that investors tend to be overconfident and are subject to numerous biases, such as the disposition effect, suggesting that paying more attention may harm rather than improve investor performance (see, among others, Odean 1998a, 1998b, 1999; Barber and Odean 2001; Guiso and Jappelli 2006). While the number of normative models on the behavior of attention-constrained investors has exploded in recent years, lack of data on individual attention has made it virtually impossible to test directly many of the theories, and the literature is still lacking satisfactory answers to seemingly simple questions, such as, how often do investors pay attention to their investment portfolio? What individual characteristics lead certain investors to pay more attention to their investment portfolios compared with others? How do investors allocate their attention? And, more importantly, does paying more attention lead to better or worse investment decisions? We answer these questions using a unique novel brokerage account data set. The data set is unique in that it contains—at the individual investor level—detailed information regarding both investor attention and trading behavior. For approximately 11,000 accounts, we observe the time stamp of investors’ login to the brokerage account website, what web pages they browse within the brokerage account domain, and how much time they spend on each web page. We use this information to construct various measures of attention, such as the number of minutes spent on the brokerage account website, the number of web pages browsed, and the number of logins. The extreme granularity of our data allows us to even identify what type of information and what stocks investors focus their attention on. For the same accounts, we also have detailed trading activity information. For every trade placed by each investor, we observe whether it is a buy or a sell, an identifier of the security traded, the time stamp of when the trade is ordered and executed, the quantity traded, and the price. Finally, the data set contains quarterly holdings information and clients’ biographical characteristics. We first explore the determinants of investor attention, that is, the relation between investor characteristics and investor attention. We find a very large heterogeneity in attention across investors that can be explained by the size and risk of the investors’ portfolios, as well as by investor trading habits and demographic characteristics. Account holders with higher invested wealth and higher exposure to small capitalization stocks, growth stocks, momentum stocks, and the overall market are more attentive. The same is true for investors who trade more frequently. On the other hand, investors who hold a higher fraction of their invested wealth in cash or exchange-traded funds (ETFs) are less attentive. Finally, we find that males pay more attention than females and attention is an increasing function of investors’ age. We then analyze how investors allocate their attention. We show the average annualized number of stocks researched by investors is equal to 31. Additionally, we find that the number of stocks researched by investors is positively related to the overall amount of time spent on the website, the number of stocks in their portfolio, and the number of stocks they trade. On the contrary, investors who have more concentrated and riskier portfolios tend to pay less attention to stock-specific information. In addition to studying the number of stocks researched, we analyze how investors concentrate their attention. We find that older investors and investors who pay more attention have less concentrated attention patterns. Investors who hold fewer stocks, have more concentrated portfolios, trade less, and have a smaller fraction of their portfolio in cash allocate their attention in a more concentrated fashion. Finally, we find that investors pay more attention to local stocks, stocks with a higher weight in their portfolio, stocks that have a higher squared Sharpe ratio, and stocks of companies with a larger market capitalization—as predicted by Van Nieuwerburgh and Veldkamp (2009, 2010). We complete the picture by analyzing what stock characteristics are related to investor attention. Investors pay more attention to companies with higher R&D expenditures, market-to-book ratios, market capitalization, and leverage, suggesting that investors prefer to research large companies that, while risky, have high growth potentials. Finally, our results confirm that many proxies used in the literature, such as stocks’ volume, turnover, news, and analyst coverage, are indeed positively related to investor attention—with turnover being the most economically and statistically significant. We then turn to studying the relation between attention and performance. We find a strong and positive cross-sectional relation between attention and performance, in that more attentive investors achieve higher risk-adjusted returns and portfolio Sharpe ratios—even after controlling for covariates related to investment style. Our baseline results show that a standard-deviation increase in overall attention is associated with a 1.5% (3.36 |$t$|-statistic) increase in annualized risk-adjusted returns and a 0.09 (7.08 |$t$|-statistic) increase in investors’ annualized Sharpe ratios. The results are consistent when we use other measures of attention—such as the amount of time spent on the research pages of the brokerage account website. Finally, the results that use the number of pages browsed or the number of logins as measures of attention are qualitatively similar, but economically smaller, suggesting that—compared with seconds—pages and logins may be poorer proxies for the process of information acquisition. We also find that, for a given level of attention, those investors who specialize and focus their research efforts on specific stocks perform better compared with those who allocate their attention evenly across all the stocks in their information set. Our cross-sectional analysis cannot distinguish whether investors perform well because they pay more attention from the alternative hypothesis that investors pay more attention because their portfolio has been performing well. To disentangle the two effects, we present results for panel regressions that relate each investor attention to future performance. The additional advantage is that panel regressions allow us to control for (average) investor skills using fixed effects and overall market conditions using time effects. At the one-month horizon, we find that a one-standard-deviation increase in attention is associated with an annualized increase in portfolio risk-adjusted performance of |$0.38\%$| (2.27 |$t$|-statistic). The effect increases to |$0.62\%$| (4.21 |$t$|-statistic) at the two-month horizon and decreases slightly to |$0.55\%$| (3.66 |$t$|-statistic) at the three-month horizon. This indicates that paying attention allows individual investors to improve their portfolio performance in the short term, suggesting that the improvement in performance hinges on attentive investors being able to purchase (sell) stocks that—in the short run—realize relatively large positive (negative) returns. Controlling for trading fees does not have a significant impact on our results. Finally, we find that the effect of attention is stronger for those investors who are—on average—less attentive. In particular, we divide our investors in five quintiles based on their unconditional attention and show that the relation between attention and future performance is monotonically decreasing across the five groups. Furthermore, while the coefficients on attention for the first four groups are positive and significant, the coefficient on attention for the last group is small, negative, and not statistically different from zero—indicating that there are diminishing marginal returns to attention. Because portfolio performance does not necessarily capture investors’ active management, we provide results on the relation between attention and the performance of individual trades. Attention is positively related to the future performance of the stocks purchased up to four months after the trade is placed. The economic magnitudes are large. At the three-month horizon, for example, a unit-standard-deviation increase in attention increases the average annualized adjusted returns of the stocks purchased by |$2.13\%$| (3.57 |$t$|-statistic). We find—on the other hand—no discernible effect of attention on the performance of the stocks sold. To understand the economic mechanism relating attention to the performance of the stocks traded, we conduct a number of auxiliary exercises. First, by analyzing the performance of the stocks before they are traded, we provide evidence that the superior performance of high-attention investors arises because they purchase attention-grabbing stocks that have appreciated in the recent past and whose positive performance persists for up to six months. However, the relation between attention and future stock returns remains significant when we include attention-grabbing proxies as controls in the performance regressions. Second, we show that attention is particularly profitable when investors trade stocks with high market capitalization, trading volume, volatility, number of analysts, dispersion of analyst forecasts, and news—indicating that it is for the stocks with high uncertainty, but for which a lot of public information is available, that it pays to pay attention. Third, we show that Odean (1999)’s result that the stocks sold by individuals outperform the ones purchased disappears for high-attention trades, but is very strong for low-attention trades. For high-attention trades, the average annualized three-month abnormal return of the stocks purchased equals 3.16%, the one for the stocks sold equals 4.20%, and their difference is statistically insignificant, with a |$p$|-value of 0.29. For low-attention trades, on the other hand, the average annualized three-month abnormal return of the stocks purchased equals |$-0.28$|%, the one for the stocks sold equals 3.08%, and their difference is statistically significant, with a |$p$|-value of 0.00. 1. Related Literature Our paper is related to the theoretical literature that studies the behavior of investors with information capacity constraints. A first strand of this literature focuses on the cross-sectional dimension of inattention—that is, what sources of information or assets should attention-constrained investors pay attention to when facing the problem of allocating their wealth across a number of assets. Peng and Xiong (2006) show that, theoretically, investors with limited attention capacity engage in category-learning, in that they focus not on stock-specific information, but on market- and sector-wide information. Van Nieuwerburgh and Veldkamp (2010) propose a model in which, depending on their preferences, investors behave as “specialized learners” who focus their attention on a single asset or “generalized learners” who focus their attention on multiple assets. Van Nieuwerburgh and Veldkamp (2009) predict that investors should pay more attention to the assets that have a higher squared Sharpe ratio and market capitalization. We contribute to this literature by providing evidence on how brokerage account investors allocate their attention—that is, how many individual stocks they focus their attention on, whether they spread their attention evenly among them, and what stock characteristics drive investor attention. A second strand of this literature focuses on the time-series dimension of attention and shows that, if information acquisition is costly, it is optimal to alternate long periods of inaction to brief spurs of attention, where information is acquired and investment decisions are made (see Gabaix and Laibson 2002; Abel, Eberly, and Panageas 2007, 2013; Huang and Liu 2007; and Alvarez, Guiso, and Lippi 2012). We contribute to this literature by providing empirical evidence on the patterns of investor attention and trading—that is, how often investors pay attention to their investment portfolio, whether they evaluate their portfolio allocations at equally spaced intervals (as many of the theoretical models predict) or they alternate periods of high attention to periods of low attention. Finally, we study what demographic and portfolio characteristics are associated with higher or lower degrees of attention. Our paper is also related to the literature that studies the performance of individual investors. For the most part, the literature has documented the mistakes of individual investors. For example, Odean (1999) and Barber and Odean (2000) show that—on average—individual investors trade too frequently and that trading is detrimental to their wealth. Subsequent studies have uncovered substantial cross-sectional variation among investors’ trading performance. Superior trading performance has been linked to investors’ IQ (Grinblatt, Keloharju, and Linnainmaa 2012; Korniotis and Kumar 2013), education (Von Gaudecker 2015), wealth (Calvet, Campbell, and Sodini 2007), experience (Korniotis and Kumar 2011; Nicolosi, Peng, and Zhu 2009), and portfolio concentration (Ivkovic, Sialm, and Weisbenner 2008). More recently, Frydman, Hartzmark, and Solomon (2017) document that investors make better investment decisions when they sell one asset and quickly buy another one. On reinvestment days, investors display no disposition effect and make better selling decisions. Our results provide novel empirical evidence on the relation between attention and trading performance. If investors acquire valuable information while spending time on the trading platform, we expect their trades to be more profitable as they pay more attention. If investors are incapable of processing the information they acquire, on the other hand, we expect to find no relation between attention and performance. Finally, if investors systematically misinterpret the information they acquire, we expect to find a negative relation between attention and performance. The fact that we find a positive relation between attention and performance, even in the presence of investor fixed-effects, is an indication that at least certain investors are systematically able to understand and exploit the information they acquire. We also contribute to the empirical literature that investigates investor attention, its determinants, and its impact on asset prices. Since its inception, this literature has faced significant challenges in measuring attention itself, leading researchers to resort to attention proxies such as trading volume (Gervais, Kaniel, and Mingelgrin 2001), price limits (Li and Yu 2012; Seasholes and Wu 2007), and news (Yuan 2015; Barber and Odean 2007), and making the implicit assumption that investors are likely to pay attention to stocks that are mentioned in the news or that have been heavily traded on a given day. More recently, Da, Engelberg, and Gao (2011) propose the use of Google searches as a direct measure of aggregate attention and—using Google searches—Vlastakis and Markellos (2012) and Andrei and Hasler (2015) show that aggregate attention varies as a function of stock market volatility. The advantage of using aggregate Google web searches over news is that they identify the information investors actively seek, rather than the information they are potentially exposed to. A key shortcoming, on the other hand, is that they are not specific to the individual investor and therefore cannot shed new light on how attention and the process of information acquisition relate to trading at the individual investor level. The only studies that obtain direct measures of investor attention at the individual level—and are therefore closest to ours—are Karlsson, Loewenstein, and Seppi (2009) and Sicherman et al. (2016). Using a large panel of investors’ logins to 401K accounts as a measure of attention, they show that investors pay less attention to their investment portfolio after stock market declines. They also show that investors’ attention varies as a function of portfolio holdings, wealth, and demographic characteristics such as age and gender. While our work confirms many of the findings in Sicherman et al. (2016), it is different along several dimensions. First, we have information on brokerage rather than 401K accounts. This is important, because 401K investors can only choose among a limited number of fixed income and equity funds, and are completely unable to purchase or sell individual stocks. Furthermore, as shown by Agnew, Balduzzi, and Sunden (2003), Madrian and Shea (2001), and Sialm, Starks, and Zhang (2015), 401K investors display very limited trading, a high degree of inertia, and extreme asset allocations.1 Second, our measures of attention are not limited to investors’ logins, because—for each investor—we observe what information he or she browses and how much time he or she spends doing it. This means that we are able to provide results regarding what information investors pay attention to and how much time they spend thinking about their portfolio decisions. Finally, we have detailed information regarding investors’ portfolio allocations and trades, meaning that we can relate investors’ attention to the type of stocks they own and to the performance of the stocks they purchase and sell. 2. Measuring Attention Using Investor Web Activity In this section, we discuss the measures of attention used in the paper. We start by presenting the data we have access to in terms of investors’ web activity, and we show that—once aggregated across all the investors in our data set—the information measures we construct have an information content similar to that of Google’s Search Volume Index. The advantage of our measures, however, is that they are available for each investor and therefore allow us to study the relation between attention and investment decisions at the individual level. 2.1 The web activity we observe Before describing the measures of attention we construct, we provide an example of the web activity we observe for each investor in our sample. Table 1 shows the web behavior of a sample account holder on January 28, 2014.2 To preserve the anonymity of the brokerage account house, we mask the URLs we have access to and, rather than reporting the full string characterizing each URL, we report only the content of the web page each URL is associated with. Table 1 Example of web activity within the brokerage account website Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 This table displays the web activity—within the brokerage account website—of an account holder on January 28, 2014. Timestamp includes the date, hour, minute, and second of the first click on the web page; Masked URL is the masked URL of the web page browsed by the investor. |$Duration$| is the number of seconds spent on the web page, and |$Session$| is the web-session number within the trading day. The URLs presented in the table have been masked to preserve the anonymity of the brokerage account house. Table 1 Example of web activity within the brokerage account website Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 Timestamp Masked URL Duration Session 28jan2014 07:58:05 Homepage 00:00:13 1 28jan2014 07:58:18 Balances and Positions 00:00:08 1 28jan2014 07:58:26 Watchlist 00:00:06 1 28jan2014 08:32:16 Research / Stocks Overview 00:00:02 2 28jan2014 08:32:18 Research / Ticker Symbol=SPX 00:05:33 2 28jan2014 08:37:51 Watchlist 00:00:06 2 28jan2014 08:37:57 Watchlist / Refresh 00:00:27 2 28jan2014 08:38:24 Research / Stocks Overview 00:00:01 2 28jan2014 08:38:25 Research / Ticker Symbol=VIX 00:00:47 2 28jan2014 08:39:12 Watchlist 00:00:04 2 28jan2014 08:39:16 Watchlist / Refresh 00:06:16 2 28jan2014 08:45:32 Balances and Positions 00:00:05 2 28jan2014 08:45:37 Watchlist 00:00:29 2 28jan2014 08:46:06 Watchlist / Refresh 00:26:00 2 28jan2014 09:12:06 Balances and Positions 00:00:04 2 28jan2014 09:12:10 Watchlist 00:02:38 2 28jan2014 12:59:46 Balances and Positions 00:00:18 3 28jan2014 13:00:04 Watchlist 00:17:53 3 28jan2014 14:28:54 Homepage 00:00:12 4 28jan2014 14:29:06 Balances and Positions 00:00:05 4 28jan2014 14:29:11 Watchlist 00:00:23 4 This table displays the web activity—within the brokerage account website—of an account holder on January 28, 2014. Timestamp includes the date, hour, minute, and second of the first click on the web page; Masked URL is the masked URL of the web page browsed by the investor. |$Duration$| is the number of seconds spent on the web page, and |$Session$| is the web-session number within the trading day. The URLs presented in the table have been masked to preserve the anonymity of the brokerage account house. The table shows that the account holder had a total of four sessions over the day. The first connection occurred at 7:58:05 am (Central Time) and, after logging to the Home page, the investor checked his or her Balances and Positions as well as his or her stocks Watchlist. The Balances and Positions page reports not only the amount of wealth invested in each stock, but also important stock information such as past returns, historical and forecasted earnings and dividends, key accounting information such as price-to-book and price-to-earnings ratios, basic facts such as revenues and institutional ownership, and a summary of analysts’ opinions as well as recent news. The Watchlist is a web page containing information on all the stocks and other assets the investor decides to pay attention to. The second session starts only thirty minutes later, two minutes after the markets open in New York. This second session is much longer, approximately forty minutes, and entails more actions. The investor first checks the Research page of the website detailing the latest news on the |$SPX$|⁠, the S&P 500 index. Right after, he or she connects to the page displaying the stocks Watchlist to quickly switch to the Research page relative to the |$VIX$|⁠. The rest of the session is dedicated to assessing Balances and Positions of his or her trading account and the Watchlist. The third session occurs right around lunch time, it lasts a little more than eighteen minutes, and it involves a look at the Watchlist as well as the Balances and Positions pages. Finally, the last session of the day occurs at 14:28:54 pm, thirty minutes before the markets close. It lasts only forty seconds and involves just a quick look at his or her Balances and Positions and the Watchlist. 2.2 From links to attention The web activity information presented in Table 1 is a small example of the web-activity data we were granted access to. In particular, as part of a large SQL relational database described in more details in Appendix A, the brokerage house gave us access to a web-activity table containing the web pages visited—within its website domain—for approximately 11,000 randomly chosen accounts over the period January 2013–June 2014. The data set is very granular and contains in excess of 17 million observations. Each observation contains a unique numeric account identifier, the URL of the web page visited within the brokerage account domain, the date and time of the first click on the page, the number of seconds spent on the page, and the number of the web sessions within the trading day. Next, we explain how we use this data to construct a variety of attention measures. 2.2.1 Overall attention measures The first set of attention measures is related to the overall attention paid by investors to their brokerage account. The first measure is the number of seconds spent on the brokerage account website over a given time interval. The second measure computes the total number of pages visited by the investor. Finally, the third measure is the number of investor logins to the brokerage account website. Using three measures is important for robustness purposes. One may prefer the number of pages visited rather than the number of seconds spent on the website, because it is possible for some individuals to stay logged into the brokerage account website and leave it in the background while performing other activities. On the other hand, one may prefer the number of seconds rather than the number of pages visited, because it is unlikely for someone to understand the content of a stock report if he or she spends only one or two seconds reading it. Considering the number of logins is also important, because it potentially allows us to distinguish between the extensive and intensive margin of investors’ attention. For example, an investor who connects multiple times a day, but stays connected for just a few seconds, is probably looking for very different information compared with an investor who connects once or twice but spends an hour or longer on the trading platform. To show that the attention measures we extract from our data are related to the ones that have been proposed in the literature, we present in Figure 1 the time-series—at the weekly frequency—of the total number of seconds aggregated across all investors, and four attention proxies:3 the Google Search Volume Index for “S&P 500” (panel A), the total number of news pertaining to stocks in the S&P 500 (panel B), the trading volume on the S&P 500 (panel C), and the State Street Investor Confidence Index (panel D).4 Each panel reports our measure of attention as a dashed red line and one of the alternative measures as a solid blue line. The plots clearly show that there is a very tight relation between our attention measure and both Google’s Search Volume Index for the S&P 500 (78.2% correlation, statistically significant at the 1% level) and the trading volume on the S&P 500 (66.1% correlation, statistically significant at the 1% level). The news variable is also quite related to our attention measure (43.1% correlation, statistically significant at the 1% level), while the correlation with the confidence index is relatively low—at only 27.7%—and is statistically insignificant. Figure 1 View largeDownload slide Comparison of alternative attention measures This figure compares the weekly total number of seconds spent on the brokerage account website—computed across all account holders—to four proxies of investor attention that have been proposed in the literature: the Google Search Volume Index for the word “S&P 500,” reported in the top-left panel; the total number of news pertaining to the stocks in the S&P 500, reported in the top-right panel; the trading volume on the S&P 500, reported in the bottom-left panel; and the State Street Investor Confidence Index, reported in the bottom-right panel. In each panel, our measure of attention is reported as a dashed red line, while the alternative measure of attention is reported as a solid blue line. Figure 1 View largeDownload slide Comparison of alternative attention measures This figure compares the weekly total number of seconds spent on the brokerage account website—computed across all account holders—to four proxies of investor attention that have been proposed in the literature: the Google Search Volume Index for the word “S&P 500,” reported in the top-left panel; the total number of news pertaining to the stocks in the S&P 500, reported in the top-right panel; the trading volume on the S&P 500, reported in the bottom-left panel; and the State Street Investor Confidence Index, reported in the bottom-right panel. In each panel, our measure of attention is reported as a dashed red line, while the alternative measure of attention is reported as a solid blue line. 2.2.2 Categorical attention measures The second set of attention measures also uses seconds, pages, and number of logins, but focuses on the various sections of the website visited by investors. The brokerage account website has a hierarchical structure, whereby—for example— all the web pages related to specific tickers like |$SPX$| and |$VIX$| fall under the “Research” category. By parsing all the URLs and categorizing them, we can obtain many other categories such as “Home Page,” “Balances and Positions,” and “Watchlist.” To maintain parsimony, we classify all the available URLs into fourteen categories: Balances and Positions, Research, Trading, Homepage, Account, Watchlist, History Statement, Bank, Mail, Tax, Help, Search, Retirement, and Client.5 Panel A of Table 2 reports the daily total number of hours spent across investors for the top six categories. The most viewed section of the website is—on average—Balances and Positions. This page contains not only information regarding investors’ performance and portfolio weights, but also important stock information such as past returns, historical and forecasted earnings and dividends, key accounting information such as price-to-book and price-to-earnings ratios, basic facts such as revenues and institutional ownership, and a summary of analysts’ opinions as well as recent news. The average time spent across all investors is 787 hours per day, but this aggregate measure has a large standard deviation equal to 412, so the total number of hours spent ranges from 516 to 930 hours for the 25th and 75th percentiles, respectively. Research is the second most popular category, with 483 hours per day, followed by Trading and Homepage. The remaining categories are much less visited by the investors. For example, the average number of hours spent on Watchlist is only 46 per day. Table 2 Summary statistics of investor attention Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 This table reports summary statistics of investor attention. Panel A reports summary statistics of the total daily number of hours spent across all account holders on various sections of the brokerage account website. For each section, we report the mean (⁠|$Mean$|⁠), the standard deviation (⁠|$St. \ dev.$|⁠), and the 25th, 50th, and 75th percentiles of the daily number of hours—all computed in the time-series dimension. Panel B reports the top 20 companies and ETFs researched by brokerage account holders. The three columns show the rank based on the number of minutes, pages, and visits, respectively. Table 2 Summary statistics of investor attention Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel A. Total daily number of hours spent across all accounts on the top six sections of the brokerage account website Website Section Mean St. dev. pct.25 pct.50 pct.75 Balance and Positions 787 412 516 669 930 Research 438 281 260 381 523 Trading 415 263 213 353 561 Homepage 370 143 271 346 438 Account 61 47 35 53 76 Watchlist 46 21 35 42 52 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 Panel B. Rank of the top 20 companies and ETFs researched by brokerage account holders Company or ETF Rank by minutes Rank by pages Rank by visits Facebook 1 2 2 Apple 2 1 1 Bank of America 3 6 6 Ford 4 5 5 SPDR S&P 500 ETF Trust 5 12 21 AT&T 6 4 4 Twitter 7 11 16 General Electric 8 3 3 3D System 9 10 10 Verizon 10 9 8 Tesla Motors 11 8 9 Gilead Sciences 12 19 27 JC Penney 13 15 19 Microsoft 14 7 7 Sirius XM 15 18 17 Amazon 16 14 15 SPDR Gold Trust 17 26 32 SPDR Dow Jones Industrial Average ETF 18 23 41 Netflix 19 13 11 Market Vectors ETF Trust 20 33 43 This table reports summary statistics of investor attention. Panel A reports summary statistics of the total daily number of hours spent across all account holders on various sections of the brokerage account website. For each section, we report the mean (⁠|$Mean$|⁠), the standard deviation (⁠|$St. \ dev.$|⁠), and the 25th, 50th, and 75th percentiles of the daily number of hours—all computed in the time-series dimension. Panel B reports the top 20 companies and ETFs researched by brokerage account holders. The three columns show the rank based on the number of minutes, pages, and visits, respectively. Focusing on the various subcategories is important because it allows us to discern whether looking at different types of information leads to different trading behavior and performance. 2.2.3 Stock attention measures The third and final set of attention measures uses only information associated with the Research URLs and focuses on the tickers researched by investors. Panel B of Table 2 reports the top 20 companies and exchange traded funds (ETFs) researched by the investors in our data set over the time period October 1, 2013–June 10, 2014.6 To compute the table, we first sum the number of minutes spent on each ticker across all the account holders in our data set and over the full sample. We then report the rank of each company (or ETF) according to the number of minutes (first column), pages (second column), and visits (third column). Starting from the first column, the table shows that individual investors focus on companies that belong to the consumer-tech space. Interestingly, while we find tech giants such as Facebook and Apple ranked first and second, respectively, we also find companies that have much smaller market capitalization, such as Twitter (7), AT&T (6), Verizon (10), Tesla (11), Sirius XM (15), and Netflix (19), ranked higher or similarly to much larger firms such as Microsoft (14). All in all, it is remarkable that 11 out of the top 20 stocks researched by investors are in the technology space. As expected, large conglomerates and banks also populate the list. For example, among the companies listed, we find Bank of America (3), Ford (4), and General Electric (9). Finally, we find four ETFs in the list: SPDR S&P 500 ETF (5), SPDR Gold Trust (17), SPDR Dow Jones ETF (18), and Market Vectors ETF (20). The results in the second and third columns are similar, indicating that number of minutes, pages, and visits capture similar patterns of behavior. 3. Summary Statistics The source of the proprietary data used in this study is a large U.S. discount broker. Unlike the discount brokers described in Odean (1999), today’s discount brokers operate online and—while providing access to analysts’ research, market news, and a large number of tools to help investors with their trading—they charge very small trading fees (less than |${\$}$|10 per trade). We relegate to Appendix A the detailed description of the various databases employed in our study, and proceed to present the key summary statistics of our final data set. Table 3 reports the summary statistics for the subset of accounts (approximately 11,000) for which we have web-activity information.7 Starting from the biographical traits, the first row of panel A shows that the average age of account holders in our data set is approximately 51, the second row shows that 73% of the account holders are males. While the average and median age are very much aligned with the ones of previous studies, our data set has a slightly higher percentage of women — 27% in our study, compared with 21% in Barber and Odean (2001), for example. The average number of accounts per client is 1.34. This occurs because although 80% of the clients have only one account, 20% of the clients have more than two accounts. Finally, as of June 2014, the average account age is 8.55, which suggests that the average account holder in our sample is quite experienced. Table 3 Summary Statistics Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 This table reports summary statistics of the biographic characteristics (panel A), the portfolio characteristics (panel B), and the trading (panel C) and attention behavior (panel D) of the brokerage account holders in our web-activity data set. For each variable in each panel, we report the sample mean (⁠|$Mean$|⁠), median (⁠|$Median$|⁠), and standard deviation (⁠|$St.\ dev.$|⁠). The statistics reported in panels A and B are computed across accounts. The statistics in panels C and D are computed first in the time-series dimension at the account-holder level, considering only days when the stock markets are open. They are then computed cross-sectionally across account holders. All dollar values were rounded to the nearest thousand, and the percentiles of “dollar value of trades” have been masked upon request of the data provider for confidentiality reasons. Table 3 Summary Statistics Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Panel A. Characteristics of clients Panel B. Portfolio characteristics Mean Median St. dev. Mean Median St. dev. Age 50.93 51 15.91 Portfolio value |${\$}$|94,000 |${\$}$|18,000 |${\$}$|368,000 Gender 0.73 1 0.44 Cash holdings |${\$}$|16,000 |${\$}$|1,000 |${\$}$|72,000 Number of accounts 1.34 1 0.68 Stock holdings |${\$}$|82,000 |${\$}$|15,000 |${\$}$|341,000 Account age 8.55 7.52 5.54 Number of stocks 6.51 4 8.77 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 Percentiles Mean St. dev. 1st 25th 50th 75th 99th Panel C. Trading behavior Fraction of days with trades 0.03 0.08 0 0 0 0.01 0.44 Days between trades 46.63 60.76 1.41 10 25.39 58.46 322 Number of trades per day 1.72 1.80 1 1 1.31 1.89 7.80 Dollar value of trades |${\$}$|16,000 |${\$}$|64,000 – – – – – Panel D. Attention behavior Fraction of days with logins 0.17 0.24 0 0.02 0.06 0.21 0.96 Days between logins 27.51 47.59 1.14 3.95 11.20 30.00 247 Number of logins per day 10.61 17.69 1.50 4.72 7.33 11.59 61.44 Number of minutes 28.74 288.69 0.30 3.77 8.00 17.45 366.35 This table reports summary statistics of the biographic characteristics (panel A), the portfolio characteristics (panel B), and the trading (panel C) and attention behavior (panel D) of the brokerage account holders in our web-activity data set. For each variable in each panel, we report the sample mean (⁠|$Mean$|⁠), median (⁠|$Median$|⁠), and standard deviation (⁠|$St.\ dev.$|⁠). The statistics reported in panels A and B are computed across accounts. The statistics in panels C and D are computed first in the time-series dimension at the account-holder level, considering only days when the stock markets are open. They are then computed cross-sectionally across account holders. All dollar values were rounded to the nearest thousand, and the percentiles of “dollar value of trades” have been masked upon request of the data provider for confidentiality reasons. Next, we turn to the portfolio characteristics of account holders as of March 31, 2014. As reported in panel B, the average (median) household has a portfolio value that equals |${\$}$|94,000 (⁠|${\$}$|18,000), indicating that the distribution is heavily skewed to the right. Cash holdings average |${\$}$|16,000, and their distribution is also heavily skewed to the right. Conditional on having at least one stock in the portfolio, the average account holds 6.51 stocks worth |${\$}$|82,000, and the median counterpart is four stocks for a total of |${\$}$|15,000.8 The median values for stock holdings are in line with those in Barber and Odean (2000), who report that the median household holds 2.61 stocks worth |${\$}$|16,210. Panel C of Table 3 reports summary statistics for the trading behavior of account holders. The results are computed as follows. In the first step, we compute the results for each account holder using his or her full time series, eliminating the days when the stock markets are closed—mainly weekends and holidays—as individuals tend to connect much less at those times. In the second step, we compute cross-sectional results across account holders. The first row shows that, on average, investors trade on 3% of the days, with 50% of the investors not trading at all, and 1% of the investors trading more than 44% of the days. For those investors placing at least two trades, the number of days between trades averages 47, resulting in approximately one trade every two months. The median counterpart is 25 days. Conditioning on placing a trade, the average investor places 1.72 trades per day, the median being 1.31. This indicates that investors tend to cluster their trades, consistent with the idea that—once they decide to reoptimize their investment positions—investors like to make multiple transactions on the same day. Finally, the average trade size is |${\$}$|16,000, in line with Barber and Odean (2000), who report an average trade size of |${\$}$|13,707 (⁠|${\$}$|11,205) for stock purchases (sales). Panel D reports summary statistics for the attention behavior of account holders. The average percentage of days with logins (across investors) equals 17%—which is almost six times larger than the trades’ frequency. The most active 1% of the investors log in 96% of the days, while the median investor logs in 6% of the days. The number of days between logins also shows that login and trading behaviors are quite different in terms of magnitude. The average number of days between logins averages 27.51 across account holders, while the median value is 11.20. Note that these numbers are not only much smaller than their trades counterparts, but they are also computed using information for twice as many account holders: that is, those accounts that do not trade at all over our sample. Conditional on logging in, investors revisit their trading account several times within a given day—the average is 10.61 and the median is 7.33. This indicates that there is a large degree of clustering in the visits we observe and that investors do not log in at regular intervals. Once they decide to pay attention, investors seem to spend a substantial amount of time on the trading platform. In particular, the average number of minutes spent on the website—conditional on logging in—equals 29, while the median value is 8. As expected, the shortest 1% of the sessions lasts only 18 seconds, while the longest 1% of the sessions can be as long as 366 minutes (more than 6 hours). To help the visualization of the cross-sectional variation in investor attention and to display how the within-investor attention varies over time, we use a heat-map graph in Figure 2. The figure is constructed as follows. For each account holder, we generate a time series from January 2013 through June 2014, eliminating the days when the stock markets are closed—mainly weekends and holidays—as individuals tend to connect much less frequently at those times. We then compute, for each investor, the daily number of minutes spent on the investment account.9 Finally, to ease the visualization, we sort the accounts by the total number of minutes over the full sample, so that the more active accounts are at the top of the figure. Figure 2 uncovers considerable heterogeneity in behavior across accounts. At the top, we find the more attentive investors that consistently log in and spend about one to two hours per day on their account. At the very bottom, on the other hand, we find those individuals that rarely log in. The figure also highlights some heterogeneity in individual accounts’ behavior over time. For example, the horizontal lines of “colder” colors—that appear in multiple parts of the figure—identify periods when a given investor pays more attention than usual to his or her investment portfolio. The opposite holds true for the horizontal lines of “warmer” colors, even though these are harder to discern. The first eight days of the sample are characterized by lighter colors for all clients. This is because the company was introducing and testing the tracking system over that week and the system was not fully functional. We do not include these observations in the computation of the results reported in the rest of the paper. Figure 2 View largeDownload slide Heat map of investors’ attention This figure reports the heat map of the daily number of minutes spent on the brokerage account website by each account holder. Each point on the |$x$|-axis represents a business day, and each point on the |$y$|-axis represents an account holder. By focusing on one value on the |$y$|-axis and moving from left to right, the reader can assess how the attention of an individual investor varies over time. Likewise, by focusing on one value on the |$x$|-axis and moving from top to bottom, the reader can observe how—for a given day—attention varies across account holders. The accounts are sorted by the total number of minutes spent on the brokerage account website over the full sample, so that the more active investors are at the top of the figure. The number of minutes is winsorized at the 95th percentile, so that the number of minutes spent on the brokerage account website ranges from 0 to 150. Figure 2 View largeDownload slide Heat map of investors’ attention This figure reports the heat map of the daily number of minutes spent on the brokerage account website by each account holder. Each point on the |$x$|-axis represents a business day, and each point on the |$y$|-axis represents an account holder. By focusing on one value on the |$y$|-axis and moving from left to right, the reader can assess how the attention of an individual investor varies over time. Likewise, by focusing on one value on the |$x$|-axis and moving from top to bottom, the reader can observe how—for a given day—attention varies across account holders. The accounts are sorted by the total number of minutes spent on the brokerage account website over the full sample, so that the more active investors are at the top of the figure. The number of minutes is winsorized at the 95th percentile, so that the number of minutes spent on the brokerage account website ranges from 0 to 150. Taken together, these findings represent a new benchmark for the models of optimal inattention, which often imply inattention intervals much longer than the ones we observe. For example, the model of Gabaix and Laibson (2002) implies an inattention interval of approximately twelve months, the one by Abel, Eberly, and Panageas (2007) an inattention interval of eight months. With values slightly greater than one month, the only model that predicts inattention intervals in line with the ones we find in the data is the one by Abel, Eberly, and Panageas (2013). Our findings also show that it is crucial for inattention models to capture the asynchronicity between attention and trading, as featured in Abel, Eberly, and Panageas (2013) and Alvarez, Guiso, and Lippi (2012). Finally, the clustering of attention, whereby investors pay attention to their portfolios for several days or weeks and then decide to be inattentive for months (or even years) is difficult to reconcile with standard models of optimal inattention—that predict instead regular inattention intervals. 4. Attention Allocation We start this section by analyzing the determinants of investor attention. That is, we explore the observable traits that characterize more and less attentive investors. The second part of this section is dedicated to providing new evidence on how investors allocate their attention. We first provide novel facts regarding the degree of investor specialization in information acquisition. We then relate the number of stocks investors pay attention to, both inside and outside their investment portfolio, to their characteristics. Finally, we explore how investors allocate their attention among the stocks they track and the characteristics of the stocks they research. 4.1 Who are the most attentive investors? In Van Nieuwerburgh and Veldkamp (2010), information acquisition involves two dimensions: how much attention to allocate, and how to allocate attention across the assets considered. Thanks to the richness of our data, we are able to study empirically the process of information acquisition at the individual investor level. In this subsection, we focus on the first dimension and study the cross-sectional determinants of investor attention. We use the number of minutes investors spend on the brokerage account website—and its various sections— as a measure of attention and relate it to investor holdings, trading, and demographic characteristics. We estimate the following cross-sectional regression at the account level: \begin{align} Attention_i= \alpha+ \boldsymbol x'_i \ \boldsymbol \beta+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N \label{cross_reg_acctid}, \end{align} (1) where |$Attention_i$| is the (log) total number of minutes spent over the 18-month period on the brokerage account website by account holder |$i$| and |$N$| is the total number of accounts in our data set.10 We divide the conditioning variables into three groups. The first group comprises demographic variables: Male, a male dummy variable; Brokerage, a brokerage account dummy; Age, the age of the investor; and Account Age, the age of the account. The second category comprises portfolio holdings variables as of December 31, 2013: Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of wealth held in cash, traded funds, and mutual funds, respectively. The third category comprises regressors related to portfolio risk and trading activity: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, small-minus-big, high-minus-low, and momentum factors—computed using daily returns over the full sample; and N. of Stocks Traded, the number of stocks traded over the period. To ease the economic interpretation of our estimates, we de-mean and standardize all regressors so that they have unit variance.11 The results for this cross-sectional regression are reported in Table 4. The first column reports the coefficient of each regressor, the second its |$t$|-statistics, and the third its economic magnitude. This last column is computed as follows. We first compute the number of minutes spent on the brokerage account website by the base-case investor, which equals 820.91 minutes, or 13.6 hours. We then multiply |$e^{\beta_k}-1$| to this base number for each coefficient |$\beta_k$| where |$k=1, \ldots , K$|⁠.12 For example, the economic magnitude of the |$Brokerage$| dummy is computed as |$820.91 \times (e^{0.590}-1)=660.11$|⁠, meaning that brokerage account holders spend 660.11 additional minutes—or 11.1 hours— compared with non–brokerage account holders, for a total of 820.91 + 660.11 = 1,481.02 minutes. This result is quite remarkable, as it shows that brokerage account investors spend twice as much time on their account compared with IRA and other account-type holders. Table 4 Characteristics of attentive investors Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 This table reports regression results on the relation between the time spent on the brokerage account website and account holder characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; N. of Assets, the total number of stocks, mutual funds, and exchange traded funds held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The third category comprises regressors related to portfolio risk and trading activity: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low, and Momentum factors computed using the full sample available using daily returns; and N. of Stocks Traded, the number of stocks traded over the period. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistics (⁠|$t$|-stat), and the economic magnitudes (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 4 Characteristics of attentive investors Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 Coeff |$t$|-stat Magnitude Brokerage 0.590*** (13.68) 660.109 Male 0.311*** (7.23) 299.143 Age 0.291*** (12.62) 277.522 Account age 0.033 (1.55) 27.720 Portfolio value 0.164*** (2.58) 145.975 Fr. in cash –0.101*** (–5.55) –78.669 Fr. in ETF –0.097*** (–4.85) –76.177 Fr. in mutual fund –0.005 (–0.25) –4.006 Beta Mkt 0.106*** (4.62) 92.019 Beta SMB 0.091*** (4.07) 78.478 Beta HML –0.062*** (–2.58) –48.974 Beta MOM 0.113*** (4.45) 98.496 Number of stocks traded 0.813*** (40.60) 1,030.815 Constant 4.749*** (121.35) 820.91 |$R^2$| 22.9% |$N$| 8,574 This table reports regression results on the relation between the time spent on the brokerage account website and account holder characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; N. of Assets, the total number of stocks, mutual funds, and exchange traded funds held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The third category comprises regressors related to portfolio risk and trading activity: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low, and Momentum factors computed using the full sample available using daily returns; and N. of Stocks Traded, the number of stocks traded over the period. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistics (⁠|$t$|-stat), and the economic magnitudes (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The remaining regressors associated with demographic characteristics show that, first, males pay more attention than women to their investment portfolios — by an average of 299.14 minutes over the period. Second, investors pay more attention to their investment portfolios as they get older. The effect is also quite strong, as a standard-deviation increase in age is associated with an increase in attention of 277.52 minutes. Third, investors’ attention does not increase with the amount of time investors have had their account open. Turning to the regressors associated with portfolio characteristics, we find that investors with higher wealth pay more attention, while attention is negatively related to the fraction of the portfolio invested in cash or in exchange-traded funds. Finally, the fraction of wealth invested in mutual funds does not seem to be related to attention in any significant manner. In terms of portfolio performance and risk, we find that investors that have greater exposure to the aggregate stock market, as measured by the beta of their portfolio with respect to the market factor, pay more attention. The same is true for those investors who are more exposed to the SMB and MOM factors, indicating that those investors that invest in small caps and momentum stocks are, overall, more attentive to their portfolios. The opposite holds for HML exposure, where we find that investors who focus on growth stocks, as opposed to value stocks, are more attentive. The final regressor in this category is the number of trades undertaken by the account holders over our sample. As expected, the more trades they place, the greater the degree of attention they pay to their investment portfolio. The coefficient for this regressor is both statistically and economically very significant: a standard-deviation increase in the number of trades is associated with an increase in attention of 1,030.81 minutes (17.2 hours) over the sample. Online Table 2 shows that the results are similar when using Research and Balances and Positions as measures of attention, and Online Table 3 shows that our results are robust to using number of pages and logins. 4.2 How do investors allocate their attention? The second dimension of information acquisition—as modeled by Van Nieuwerburgh and Veldkamp (2009, 2010)— involves allocating attention across the assets considered. In this respect, we provide novel empirical evidence along two dimensions. We first focus on the number of assets investors decide to pay attention to—that is, how many stocks they have on their “radar.” We then analyze how investors allocate their attention among the assets on their radar. To conduct this analysis, we focus on the stock-specific attention measures discussed in Section 2.2.3. 4.2.1 How many stocks do investors pay attention to? If investors did not have attention constraints, they would continuously acquire information regarding all the stocks potentially available. Because of their limited time and resources, however, investors generally collect information regarding a rather small number of assets, and some investors do not collect stock-specific information at all. For example, out of the 10,768 accounts in our data set, only 4,970 collect stock-specific information over the time period October 1, 2013–June 10, 2014. Among the investors that collect stock-specific information, the average investor acquires information for a total of 31 stocks annually. The distribution, however, is very skewed to the right, with the median investor looking at 8 stocks and the |$75$|th, |$95$|th, and |$99$|th percentiles of the distribution equal to 25, 128, and 376, respectively. To understand whether there is a systematic relation between investor characteristics and the number of stocks in their radar, we estimate the following baseline cross-sectional regression: \begin{align} N\_Stocks\_Researched_{i}=\alpha+\boldsymbol x'_i \ \boldsymbol \beta+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N, \label{stocks\_searched} \end{align} (2) where |$N\_Stocks\_Researched_{i}$| is the (log) number of stocks searched by investor |$i$|⁠, |$N$| is the total number of investors included in the analysis, and |$\boldsymbol {x}'_i$| is the same vector of investor characteristics contained in Equation (1), augmented with four regressors: Total Attention, the total amount of time spent by the investor on the website (see Section 2.2 for details) Stock Herfindahl, the Herfindahl index of each investor’s portfolio holdings as of December 31, 2013; N. Stocks, the total number of stocks held as of December 31, 2013; and N. of Stocks Traded, the number of stocks traded during the sample. We also estimate modifications of the baseline specification, where we focus, respectively, on the attention paid to the stocks inside or outside investors’ portfolios. As in Section 4.1, we de-mean and standardize all regressors so that they have unit variance. The results of the three specifications—reported in panel A of Table 5—suggest the following. First, across all specifications, we find that the number of stocks researched by investors is positively related to the overall amount of time spent on the website (Total Attention), the number of stocks in their portfolio (N. Stocks), and the number of stocks they trade (N. of Stocks Traded). On the contrary, investors who have more concentrated (Herfindahl) and riskier (Risk Idio) portfolios research fewer stocks. We also find that older investors tend to acquire information regarding a larger number of stocks. Overall, these results suggest that the investors that have broader portfolio allocations (and trade more often) are also the ones that monitor the stocks they own and explore new investment opportunities more actively. In terms of economic magnitudes, the effect of the various covariates is quite large. For example, the coefficient on Total Attention in panel A.I equals 0.423, indicating that a one-standard-deviation increase in the number of stocks held by investors is associated with a |$e^{0.423}-1= 52.6\%$| increase in the number of stocks researched. For the average investor that researches 31 stocks, this translates to an additional 16.3 stocks researched. Table 5 Investor attention allocation: cross-sectional regressions Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 This table reports regression results on the relation between attention allocation and account holder characteristics. In panel A we estimate the following cross-sectional regression: \begin{align*} N\_Stocks_{i}=\alpha+\boldsymbol{x}'_i \ \boldsymbol{\beta}+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$N\_Stocks$| is the log number of stocks researched by investor |$i$|⁠, |$\boldsymbol{x}_i$| is the vector of investor-specific covariates, and |$N$| is the total number of investors included in the analysis. The results in panel A.I use all the stocks researched by each account holder, and panel A.II (panel A.III) includes only the stocks inside (outside) each investor’s portfolio. In panel B we estimate the following cross-sectional regression: \begin{align*} Herfindahl_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} , \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$Herfindahl_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$|—computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention allocated to stock |$k$| and |$K$| is the total number of stocks searched. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises Total Attention, defined as the log total number of seconds spent by the investor on the brokerage account website over the full sample. The third category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; Herfindahl Stocks, the normalized Herfindahl index of investor |$i$|’s stock holdings—computed as |$\frac{\sum_{j=1}^{J} \omega_{j}^{2} - 1/J}{1-1/J}$| if |$J>1$| and 1 if |$J=1$|⁠, where |$w_{j}$| is the fraction of wealth allocated to stock |$j$| and |$J$| is the total number of stocks held; N. Stocks, the total number of stocks held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The fourth category comprises regressors related to the performance and risk of investors’ portfolios: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low and Momentum factors computed using the full sample available using daily returns; Risk Idio, the idiosyncratic risk of the investor’s portfolio. Finally, the last covariate is N. of Stocks Traded: the number of stocks traded over the period. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 5 Investor attention allocation: cross-sectional regressions Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 Panel A. Number of stocks Panel B. Herfindahl A.I. Overall A.II. Outside A.III. Inside Overall Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Coeff |$\boldsymbol t$|-stat Brokerage 0.027 (1.44) 0.033 (1.60) 0.002 (0.18) –0.008 (–1.16) Male 0.045** (2.54) 0.054*** (2.74) 0.014 (1.09) –0.002 (–0.29) Age 0.073*** (3.86) 0.065*** (3.12) 0.065*** (4.93) –0.012* (–1.76) Account age –0.022 (–1.16) –0.023 (–1.10) –0.014 (–0.99) 0.008 (1.31) Total attention 0.423*** (15.19) 0.433*** (14.39) 0.265*** (13.88) –0.057*** (–10.69) Portfolio value 0.002 (0.11) –0.027 (–1.16) 0.039** (2.13) –0.005 (–1.08) Herfindahl stocks –0.047** (–2.00) –0.037* (–1.71) –0.044** (–2.39) 0.020*** (2.86) N. stocks 0.116*** (4.43) 0.092*** (3.42) 0.135*** (5.88) –0.037*** (–5.38) Fr. in cash 0.064** (2.53) 0.092*** (3.31) –0.024 (–1.42) –0.028*** (–3.06) Fr. in ETF 0.002 (0.12) 0.030* (1.76) –0.051*** (–2.66) –0.004 (–0.74) Fr. in mutual fund –0.018 (–0.99) 0.008 (0.43) –0.050*** (–4.30) –0.001 (–0.16) Beta Mkt –0.047* (–1.69) –0.063** (–2.09) 0.002 (0.11) 0.015 (1.39) Beta SMB –0.047 (–1.62) –0.084** (–2.50) 0.043** (2.44) 0.012 (0.99) Beta HML –0.025 (–0.94) –0.033 (–1.13) –0.006 (–0.38) 0.003 (0.31) Beta MOM –0.030 (–1.42) –0.037 (–1.63) –0.004 (–0.33) 0.007 (0.81) Risk idio –0.084*** (–5.51) –0.091*** (–5.12) –0.033*** (–3.25) 0.032*** (5.39) N. of stocks traded 0.212*** (4.60) 0.181*** (4.21) 0.237*** (5.42) –0.030*** (–3.20) Constant 1.441*** (16.51) 1.180*** (12.31) 0.458*** (7.36) 0.482*** (14.80) |$R^2$| 23.0% 18.2% 30.7% 6.9% |$N$| 3,586 3,586 3,586 3,586 This table reports regression results on the relation between attention allocation and account holder characteristics. In panel A we estimate the following cross-sectional regression: \begin{align*} N\_Stocks_{i}=\alpha+\boldsymbol{x}'_i \ \boldsymbol{\beta}+\epsilon_{i}, \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$N\_Stocks$| is the log number of stocks researched by investor |$i$|⁠, |$\boldsymbol{x}_i$| is the vector of investor-specific covariates, and |$N$| is the total number of investors included in the analysis. The results in panel A.I use all the stocks researched by each account holder, and panel A.II (panel A.III) includes only the stocks inside (outside) each investor’s portfolio. In panel B we estimate the following cross-sectional regression: \begin{align*} Herfindahl_{i}=\alpha+\boldsymbol{x}'_i\ \boldsymbol{\beta}+\epsilon_{i} , \quad for \,\,\, i=1,\ldots,N, \end{align*} where |$Herfindahl_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$|—computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention allocated to stock |$k$| and |$K$| is the total number of stocks searched. What follows is a description of the covariates included. The first group comprises demographic variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The second category comprises Total Attention, defined as the log total number of seconds spent by the investor on the brokerage account website over the full sample. The third category comprises portfolio holdings variables: Portfolio Value, the total value of the invested portfolio; Herfindahl Stocks, the normalized Herfindahl index of investor |$i$|’s stock holdings—computed as |$\frac{\sum_{j=1}^{J} \omega_{j}^{2} - 1/J}{1-1/J}$| if |$J>1$| and 1 if |$J=1$|⁠, where |$w_{j}$| is the fraction of wealth allocated to stock |$j$| and |$J$| is the total number of stocks held; N. Stocks, the total number of stocks held; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively. The fourth category comprises regressors related to the performance and risk of investors’ portfolios: Beta Mkt, Beta SMB, Beta HML, and Beta MOM, the loadings on the market, Small-Minus-Big, High-Minus-Low and Momentum factors computed using the full sample available using daily returns; Risk Idio, the idiosyncratic risk of the investor’s portfolio. Finally, the last covariate is N. of Stocks Traded: the number of stocks traded over the period. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Second, we find that certain covariates have differential impact on the number of stocks researched inside and outside the portfolio. For example, male account holders seek new investment opportunities more actively—compared with females—as exemplified by the coefficients on Male reported in panel A.II. We do not, however, observe any difference between males and females for the stocks inside the investment portfolios. Third, as shown in panel A.III, the value of the investment portfolio is positively related to the number of stocks in the portfolio monitored by the investors. Along the same lines, we find that the greater the fraction of wealth invested in cash, ETFs or mutual funds, the lower the number of stocks in the portfolio tracked by the investors. These results are in stark contrast with the ones reported for the same regressors in panel A.II, where the regression coefficients are positive—rather than negative— and statistically significant for the fraction of wealth invested in cash. Economically, these positive coefficients suggest that it is the investors with free cash who actively seek new investment opportunities. Finally, our results suggest that greater exposures to the Market, SMB, HML, and MOM factors are negatively related to the attention paid by investors to new investment opportunities. The same regressors have instead an insignificant—or positive—effect on the attention spent by investors monitoring their current holdings. The results reported in panel A of Table 5 compute coefficient estimates exploiting variation across investors. If we take the relation between the number of stocks held and Total Attention as an example, they show that those investors who pay more attention to their investment portfolio also track more stocks. We also compute panel regressions with time and account fixed effects, which exploit within-investor variation. Our findings, reported in panel A of Online Table 4, are that investors tend to increase the number of stocks they track as they pay more attention and as they trade more. 4.2.2 How do investors allocate attention among the stocks they follow? We address the second question, that is, how do account holders allocate their attention among the assets on their radar, using three distinct approaches. The first exploits only cross-sectional variation. We estimate a regression very similar to the one in Equation (2), but replace the dependent variable |$N\_Stocks\_Researched_{i}$| with |$Herfindahl\_Researched_{i}$|⁠: the Herfindahl index of the stocks researched by investor |$i$|⁠. This quantity is computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention of investor |$i$|—measured in seconds—allocated to stock |$k$| over the full sample and |$K$| is the total number of stocks searched. The measure is bounded between 0—attention allocated evenly across all assets—and 1—all attention concentrated in one asset. The results, reported in panel B of Table 5, indicate that older investors and investors with greater total attention have less concentrated attention patterns, as shown by the negative coefficients on Age and Total Attention. We also find a positive effect for Herfindahl Stocks, and a negative effect for N. Stocks, Fr. in Cash, and N. of Stocks Traded, meaning that investors who hold fewer stocks, have more concentrated portfolios, trade less, and have a smaller fraction of their portfolio in cash allocate their attention in a more concentrated fashion. Finally, we find that investors whose portfolios have higher idiosyncratic risk have more concentrated patterns of attention across the stocks they search. The second approach exploits within-investor variation in a panel regression setting. The results, reported in panel B of Online Table 4, indicate that the more investors pay attention to their investment portfolio and the more they trade, the more evenly they allocate attention. None of the other covariates related to portfolio characteristics and risk are related to how investors allocate their attention. In the third exercise, we study which stocks investors pay attention to whenever they decide to acquire stock-specific information. We follow Hartzmark (2014) and, for every investor, we focus only on those days when stock specific information is acquired. We then estimate the following regression: \begin{align} Att\_Dummy_{i,j,t}=\alpha_{i} + {\boldsymbol x}'_{i,j,t } \ \boldsymbol{\beta} + \epsilon_{i,j,t}, \end{align} (3) where |$Att\_Dummy_{i,j,t}$| is a dummy equal to 1 if investor |$i$| researches stock |$j$| on date |$t$| and is equal to 0 for all those stocks that are in investor |$i$|’s investment opportunity set on date |$t$|⁠, but are not researched. Because it is not possible for us to know what is the investment opportunity set of every account holder at any given point in time, we consider three alternatives. The first includes only the stocks held in the investment portfolio at time |$t$| (panel A). The second uses all the stocks researched by the investor over our sample (panel B). The third includes all the stocks researched by the investor as well as all the stocks in the S&P 500 index at time |$t$| (panel C). For each investment opportunity set, we estimate two specifications. The first uses only covariates motivated by the theoretical models in Van Nieuwerburgh and Veldkamp (2009, 2010), which predict that investors should pay more attention to the stocks that have a higher squared Sharpe ratio and market capitalization. Our vector |${\boldsymbol x}'_{i,j,t }$| therefore includes: Squared Sharpe Ratio, the squared realized Sharpe ratio for each stock in the investment opportunity set, computed over the previous 21 days,13 and Size, the market value of the stock—computed as the product of the price and shares outstanding. In the second specification, we additionally include Dummy Close, an indicator variable identifying whether the headquarters of stock |$j$| are located within a 250-mile radius from investor |$i$|⁠, as in Ivkovic and Weisbenner (2005).14 In panel A, we also include Portfolio Weight, the weight of stock |$j$| in investor |$i$|’s portfolio as of time |$t$|⁠. Finally, we include account holder fixed effects and we standardize the regressors to ease the interpretation of their coefficients. Depending on the definition of investment opportunity set, the coefficients have a different interpretation. If we take the coefficient on Squared Sharpe Ratio as an example, in panel A the coefficient measures the relation between a standard-deviation increase in the squared Sharpe ratio of a stock and the probability that an investor focuses on it rather than on another stock held in his or her investment portfolio. In panels B and C the interpretation is similar, but the comparison is with the stocks that the investor tracks over the sample (panel B) or all the stocks in the S&P 500 index in addition to the ones the investor tracks over the sample (panel C). In line with the predictions of Van Nieuwerburgh and Veldkamp (2009, 2010), the results—reported in Table 6—indicate that investors pay more attention to the stocks that have a higher squared Sharpe ratio as the coefficients on Squared Sharpe Ratio are positive and significant across all panels and specifications. Size also has a positive coefficient, but is significant only in panels B and C and marginally insignificant (⁠|$t$|-statistic of 1.45) in the first specification of panel A. Table 6 Stock characteristics and investor attention Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 This table reports regression results studying which stocks investors pay attention to, whenever they acquire stock-specific information. We focus only on days when stock-specific information is acquired and estimate the following regression: \begin{align} Att\_Dummy_{i,j,t}=\alpha_i+\boldsymbol{x'}_{i,j,t} \ \boldsymbol {\beta} +\epsilon_{i,j,t} \notag \end{align} where |$Att\_Dummy_{i,j,t}$| is a dummy equal to 1 if investor |$i$| researches stock |$j$| on date |$t$| and is equal to 0 for all the other stocks that are in investor |$i$|’s investment opportunity set, but are not researched. We construct three alternative information sets for each investor. The first includes only the stocks held in the investment portfolio at time |$t$| (panel A). The second uses all the stocks researched by the investor over our sample (panel B). The third includes all the stocks researched by the investor as well as all the stocks in the S&P 500 index at time |$t$| (panel C). In each panel, we estimate two specifications. The first isolates the regressors theoretically motivated by the models in Van Nieuwerburgh and Veldkamp (2009, 2010): Squared Sharpe Ratio, the squared realized Sharpe ratio for each stock in the investment opportunity set, computed over the previous 21 days; and Size, the market value of the stock—computed as the product of the price and shares outstanding. The second additionally includes: Dummy Close, an indicator variable identifying whether the headquarters of stock |$j$| are located within a 250-mile radius from investor |$i$|⁠, as in Ivkovic and Weisbenner (2005). We use the standard formula for computing the distance in miles between account holder |$i$| and firm |$j$|⁠, as follows: |$dist (i, j) = arccos\Big(cos(lat_{i})cos(long_{i})cos(lat_{j}) cos(long_{j}) + cos(lat_{i})sin(long_{i})cos(lat_{j})sin(long_{j})+ sin(lat_{i}) sin(lat_{j})\Big) r,$| where |$lat$|/|$long$| are the latitudes/longitudes of location |$i$| and location |$j$| (expressed in radians), respectively, and |$r$| denotes the radius of the earth (approximately 3,963 miles). In panel A, we also include Portfolio Weight, the weight of stock |$j$| in investor |$i$|’s portfolio as of time |$t$|⁠. The estimates include account holder fixed effects. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are clustered at the account level. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 6 Stock characteristics and investor attention Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 Panel A. Stocks in the portfolio Panel B. Stocks searched over the sample Panel C. Stocks searched and S&P 500 stocks Squared sharpe ratio 0.0085*** 0.0085*** 0.0031*** 0.0031*** 0.0001*** 0.0001*** (8.11) (8.12) (10.06) (10.09) (3.39) (3.44) Size 0.0046 0.0026 0.0045*** 0.0045*** 0.0029*** 0.0029*** (1.45) (0.80) (3.83) (3.82) (15.24) (15.23) Dummy close 0.0263*** 0.0084*** 0.0009*** (3.94) (2.92) (4.30) Portfolio weight 0.0204*** (3.97) Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.1% 0.5% 0.1% 0.1% 0.3% 0.3% |$N$| 442,833 442,833 3,526,595 3,526,595 30,295,439 30,295,439 This table reports regression results studying which stocks investors pay attention to, whenever they acquire stock-specific information. We focus only on days when stock-specific information is acquired and estimate the following regression: \begin{align} Att\_Dummy_{i,j,t}=\alpha_i+\boldsymbol{x'}_{i,j,t} \ \boldsymbol {\beta} +\epsilon_{i,j,t} \notag \end{align} where |$Att\_Dummy_{i,j,t}$| is a dummy equal to 1 if investor |$i$| researches stock |$j$| on date |$t$| and is equal to 0 for all the other stocks that are in investor |$i$|’s investment opportunity set, but are not researched. We construct three alternative information sets for each investor. The first includes only the stocks held in the investment portfolio at time |$t$| (panel A). The second uses all the stocks researched by the investor over our sample (panel B). The third includes all the stocks researched by the investor as well as all the stocks in the S&P 500 index at time |$t$| (panel C). In each panel, we estimate two specifications. The first isolates the regressors theoretically motivated by the models in Van Nieuwerburgh and Veldkamp (2009, 2010): Squared Sharpe Ratio, the squared realized Sharpe ratio for each stock in the investment opportunity set, computed over the previous 21 days; and Size, the market value of the stock—computed as the product of the price and shares outstanding. The second additionally includes: Dummy Close, an indicator variable identifying whether the headquarters of stock |$j$| are located within a 250-mile radius from investor |$i$|⁠, as in Ivkovic and Weisbenner (2005). We use the standard formula for computing the distance in miles between account holder |$i$| and firm |$j$|⁠, as follows: |$dist (i, j) = arccos\Big(cos(lat_{i})cos(long_{i})cos(lat_{j}) cos(long_{j}) + cos(lat_{i})sin(long_{i})cos(lat_{j})sin(long_{j})+ sin(lat_{i}) sin(lat_{j})\Big) r,$| where |$lat$|/|$long$| are the latitudes/longitudes of location |$i$| and location |$j$| (expressed in radians), respectively, and |$r$| denotes the radius of the earth (approximately 3,963 miles). In panel A, we also include Portfolio Weight, the weight of stock |$j$| in investor |$i$|’s portfolio as of time |$t$|⁠. The estimates include account holder fixed effects. Within each panel we display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠) and the associated |$t$|-statistic (⁠|$t$|-stat). Standard errors are clustered at the account level. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The remaining regressors have coefficients that follow intuition. The coefficient on Dummy Close indicates that investors spend more attention researching the stocks that are located nearby. This finding is important, because it uncovers the potential mechanism associated with the proximity/familiarity results in Massa and Simonov (2006), Ivkovic and Weisbenner (2005), Huberman (2001), and Grinblatt and Keloharju (2001). While the literature has shown that investors hold a more than proportional amount of local stocks in their portfolio, our results suggest that proximity/familiarity operates at the attention level as well. This means that investors include local stocks in their portfolios because they pay attention to local stocks, as opposed to the alternative hypothesis that investors pay attention uniformly across all stocks (near and far) and include in their portfolio only the ones geographically close, because proximity gives them informational advantages. Finally, the coefficient on Portfolio Weight suggests that — among the assets they hold—investors pay more attention to the assets that have a higher weight in their portfolio. 4.3 Which stocks do investors pay attention to? While the results in Section 4.2 show that idiosyncratic factors such as geographic proximity affect investor attention to specific stocks, the summary statistics in Section 3 provide prima facie evidence that investor attention is correlated as investors tend to focus on large and well-known companies such as Facebook, Apple, Bank of America, and Ford—among others. In this section we dig deeper and analyze what stock characteristics are related to investor attention. We consider the sample October 1, 2013, to June 10, 2014, to construct the overall attention paid across all investors to each individual ticker, and estimate the following baseline specification: \begin{align} Minutes_j= \alpha+ \boldsymbol x'_j \ \boldsymbol \beta+\epsilon_{j}, \quad for \,\,\, j=1,\ldots,J \label{cross_reg_stocks}, \end{align} (4) where Minutes is the (log) number of minutes spent researching stock |$j$| over the eight-month period, and |$J$| is the total number of stocks for which we have both attention and conditioning information. We divide our conditioning variables in three groups. The first group comprises attention proxies: Analyst, the number of analysts covering the stock; News, the total number of news events associated with the stock;15Volume, the stock’s volume; and Turnover, computed as the stock’s volume divided by the shares outstanding. The second category comprises regressors related to stocks’ risk and performance. Alpha, Beta Mkt, Beta SMB, Beta HML, and Beta MOM are the intercept and the loadings on the Market, SMB, HML and MOM factors. The coefficients are estimated for each stock |$j$| using daily data over the sample. Return, Variance, Skewness, and Kurtosis are the realized returns, variance, skewness, and kurtosis for each stock |$j$|⁠, computed using daily returns. For the higher moments we follow Amaya et al. (2015) and use the following expressions: \begin{align} RVar_{j} =\sum_{i=1}^{N} r^2_{i,j}, \quad RSkew_{j} =\frac{\sqrt{N}\sum_{i=1}^{N} r_{i,j}^3}{RVar_{j}^{3/2}}, \quad RKurt_{j} =\frac{{N}\sum_{i=1}^{N} r_{i,j}^4}{RVar_{j}^{2}}, \notag \end{align} where |$r_{i,j}$| is the daily return of stock |$j$| on day |$i$| and |$N$| is the number of trading days in the sample. The third category comprises regressors related to stock characteristics: MktBook, the ratio of market value of assets and the book value of assets;16R&D, computed as the ratio of research and development expenses (Compustat item: XRDQ) and sales (Compustat item: SALEQ);17Profitability, the ratio between operating income before depreciation (Compustat item: OIBDPQ) and total assets (Compustat item: ATQ); Age, the number of years a company has been in the CRSP data set; Size, the market value of assets—computed as the product of the price and shares outstanding; Leverage, computed as the sum of long-term debt and debt in current liabilities, divided by the market value of assets; and Inst. Investors, the fraction of the shares outstanding owned by institutional investors. As in Section 4.1, we de-mean and standardize all the regressors so that they have unit variance. The economic interpretation of the coefficients in Table 7 therefore parallels the one of Table 4. The first column reports the coefficient of each regressor, the second its |$t$|-statistic, and the third its economic magnitude. Table 7 Characteristics of researched stocks Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 This table reports regression results on the relation between investor attention—computed across all account holders in our data—and stock characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{j}=\alpha+\boldsymbol{x}'_j \ \boldsymbol{\beta}+\epsilon_{j}, \quad for \,\,\, j=1,\ldots, J, \end{align*} where |$Attention_{j}$| is the log total number of minutes spent—across all account holders—on stock |$j$| and |$\boldsymbol{x}_j$| is a vector of stock-specific covariates. What follows is a description of the three groups of covariates included in the analysis. The first group comprises regressors we classify as attention proxies: Analyst, the number of analysts covering the stock; News, the total number of news events associated with the stock; Volume, the stock’s volume; Turnover, computed as the stock’s volume divided by the shares outstanding. The second category comprises regressors related to stocks’ risk and performance. Alpha, Beta Mkt, Beta SMB, Beta HML, and Beta MOM are the intercept and the loadings on the market, SMB, HML, and MOM factors. The loadings are estimated for each stock |$j$| using daily data over the full sample. Return, Variance, Skewness, and Kurtosis are the realized returns, variance, skewness and kurtosis for each stock |$j$|—computed using daily returns. The third category comprises regressors related to stock characteristics: MktBook, the ratio of market value of assets and the book value of assets; R&D, computed as the ratio of research and development expenses (Compustat item: XRDQ) and sales (Compustat item: SALEQ); Profitability, the ratio between operating income before depreciation (Compustat item: OIBDPQ) and total assets (Compustat item: ATQ); Age, the number of years a company has been in the CRSP data set; Size, the market value of assets computed as the product of the price and shares outstanding; Leverage, computed as the sum of long-term debt and debt in current liabilities, divided by the market value of assets; Inst. Investors, the fraction of the shares outstanding owned by institutional investors. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistic (⁠|$t$|-stat), and the economic magnitude (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 7 Characteristics of researched stocks Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 Coeff |$\boldsymbol t$|-stat Magnitude Analyst 0.441*** (12.45) 25.052 News 0.064* (1.78) 2.981 Turnover 0.835*** (4.45) 59.070 Volume 0.295*** (6.82) 15.509 Alpha 0.027 (0.70) 1.228 Beta Mkt 0.254*** (5.04) 13.088 Beta SMB –0.050* (–1.68) –2.218 Beta HML –0.043 (–1.47) –1.917 Beta MOM 0.073*** (2.65) 3.411 Return 0.022 (0.72) 1.027 Variance 0.080 (1.62) 3.759 Skewness –0.043 (–1.54) –1.923 Kurtosis 0.047 (1.55) 2.159 MktBook 0.113*** (2.96) 5.393 R&D 0.101** (2.44) 4.788 Profitability 0.031 (0.68) 1.422 Age –0.007 (–0.30) –0.296 Size 0.201*** (8.14) 10.062 Leverage 0.155*** (4.72) 7.584 Inst. investors –0.364*** (–8.34) –13.805 Constant 3.206*** (68.05) 45.242 |$R^2$| 45.4% |$N$| 2,572 This table reports regression results on the relation between investor attention—computed across all account holders in our data—and stock characteristics. We estimate the following cross-sectional regression: \begin{align*} Attention_{j}=\alpha+\boldsymbol{x}'_j \ \boldsymbol{\beta}+\epsilon_{j}, \quad for \,\,\, j=1,\ldots, J, \end{align*} where |$Attention_{j}$| is the log total number of minutes spent—across all account holders—on stock |$j$| and |$\boldsymbol{x}_j$| is a vector of stock-specific covariates. What follows is a description of the three groups of covariates included in the analysis. The first group comprises regressors we classify as attention proxies: Analyst, the number of analysts covering the stock; News, the total number of news events associated with the stock; Volume, the stock’s volume; Turnover, computed as the stock’s volume divided by the shares outstanding. The second category comprises regressors related to stocks’ risk and performance. Alpha, Beta Mkt, Beta SMB, Beta HML, and Beta MOM are the intercept and the loadings on the market, SMB, HML, and MOM factors. The loadings are estimated for each stock |$j$| using daily data over the full sample. Return, Variance, Skewness, and Kurtosis are the realized returns, variance, skewness and kurtosis for each stock |$j$|—computed using daily returns. The third category comprises regressors related to stock characteristics: MktBook, the ratio of market value of assets and the book value of assets; R&D, computed as the ratio of research and development expenses (Compustat item: XRDQ) and sales (Compustat item: SALEQ); Profitability, the ratio between operating income before depreciation (Compustat item: OIBDPQ) and total assets (Compustat item: ATQ); Age, the number of years a company has been in the CRSP data set; Size, the market value of assets computed as the product of the price and shares outstanding; Leverage, computed as the sum of long-term debt and debt in current liabilities, divided by the market value of assets; Inst. Investors, the fraction of the shares outstanding owned by institutional investors. We display the ordinary least squares coefficient estimates (⁠|$Coeff$|⁠), the associated |$t$|-statistic (⁠|$t$|-stat), and the economic magnitude (⁠|$Magnitude$|⁠)—computed as described in Section 4.1. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The covariates that have been adopted as attention proxies in the literature are the ones that have the greatest economic impact. For example, starting from a baseline of 45.24 minutes, a standard deviation increase in Turnover is associated with a 59.07-minutes increase in attention. Also large are the magnitudes associated with Volume and Analyst. News are significant as well, but their economic impact is smaller.18 Among the second set of regressors, we note that investors pay more attention to stocks that have higher exposure to the market and momentum factors. This indicates that investors spend time researching stocks that have higher systematic risk and have a positive exposure to momentum. The coefficient on SMB is negative and marginally significant, while the coefficient on HML is not significant. Once exposures to market factors are controlled for, stock returns, variance, skewness, and kurtosis do not have a significant impact on attention. In terms of firm characteristics, investors pay attention to stocks that have high growth potentials—as evidenced by the positive coefficients on MktBook and R&D—and that are well established but relatively risky—as evidenced by the positive coefficient on Size and Leverage. We also find a very strong and negative relation between attention and Inst. Investors, suggesting that individual investors pay attention to stocks that are different, compared with the ones owned by institutional investors. Finally, Profitability and Age do not have a significant coefficient. Online Table 5 shows the results are robust when we use Pages and Visits as measures of attention. 5. Attention and Performance: Evidence from Portfolio Returns We now turn to the set of results relating investor attention and portfolio performance. Investors who spend more time acquiring information are likely to receive more trading signals. If these investors are able to correctly process these signals, we would expect them to achieve superior portfolio performance. On the other hand, if they systematically misinterpret these signals, we would expect them to perform poorly. To estimate what relation holds in the data, we propose two strategies. The first strategy uses cross-sectional regressions and addresses the question of whether investors who pay more attention perform better. The second strategy implements panel regressions and uses within-individual variation over time to assess whether changes in investor attention are associated with performance differentials. In both cases, we find a positive relation between attention and investment performance. 5.1 Cross-sectional regressions In Table 8 we evaluate the relation between investor attention and risk-adjusted performance, where the former is measured as overall attention and the latter is measured as the DGTW abnormal return of the investor portfolio.19 We estimate the following cross-sectional regression: \begin{align} AVG\_DGTW\_Ret_{i}=\alpha+\beta \ {Attention_i} + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N, \label{cross_spec_baseline} \end{align} (5) where |$AVG\_DGTW\_Ret_{i}$| is the annualized average percentage DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the total attention spent on the brokerage account website by account holder |$i$| over the sample, and |$\boldsymbol{x}_i$| is a subset of the covariates that explain the cross-section of investor attention—as shown in Section 4.1.20 All regressors are standardized so that they have zero mean and unit standard deviation. Table 8 Attention and portfolio performance: cross-sectional results Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 This table reports regression results on the relation between portfolio performance and investor attention. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta \ {Attention_i} + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$| and |$N$| is the total number of account holders included in the analysis. We include three specifications. The first specification uses attention as the sole covariate. The second specification includes a group of covariates that control for investor demographic characteristics: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The third specification includes portfolio holdings and trading activity variables: Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroskedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% level, respectively. Table 8 Attention and portfolio performance: cross-sectional results Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 Spec 1 Spec 2 Spec 3 Attention 1.531*** 1.600*** 2.326*** (3.36) (3.46) (4.48) Brokerage 1.991** 1.905** (2.29) (2.17) Male 0.631 0.605 (0.71) (0.68) Age –1.038*** –0.964** (–2.59) (–2.38) Account age 0.043 0.056 (0.10) (0.13) Portfolio value –0.553** (–2.29) Fr. in cash 0.136 Fr. in ETF 0.438 Fr. in mutual fund 0.580 (1.64) N. of stocks traded –1.320*** (–4.26) Constant 1.920*** 0.710 0.935 (4.25) (0.92) (1.18) |$R^2$| 0.1% 0.3% 0.4% |$N$| 8,340 7,476 7,468 This table reports regression results on the relation between portfolio performance and investor attention. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta \ {Attention_i} + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the log of the total number of minutes spent on the brokerage account website by account holder |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$| and |$N$| is the total number of account holders included in the analysis. We include three specifications. The first specification uses attention as the sole covariate. The second specification includes a group of covariates that control for investor demographic characteristics: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account. The third specification includes portfolio holdings and trading activity variables: Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroskedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% level, respectively. The results highlight a statistically and economically significant relation between attention and performance. The coefficient in the first specification equals 1.531% and is significant at the 1% level. The results imply that a one-standard-deviation increase in attention is associated with an increase in the investor’s DGTW abnormal return of 1.53%, which is economically rather large. However, while the coefficient on attention is positive and significant, the low |$R^2$| of 0.1% indicates there is a lot of cross-sectional heterogeneity in performance that attention does not explain. The corresponding coefficient estimates for research attention (Online Table 6, panel A) and balances and positions attention (Online Table 6, panel B) are 0.988% and 1.267%, respectively. A common feature of the results reported in this section—as well as the subsequent ones—is that balances and positions attention sometimes has a stronger impact on performance, compared with research attention. While surprising at first sight, the results may be driven by the fact that investors obtain important research information also on the Balances and Positions portion of the website. The information includes stocks’ past returns; historical and forecasted earnings and dividends; key accounting information such as price-to-book and price-to-earnings ratios; basic facts such as revenues and institutional ownership; and a summary of analysts’ opinions as well as recent news. Research pages, on the other hand, contain in-depth analyst reports and balance sheet information for each company. Also, because investors spend almost double the time on balances and positions pages compared with research pages—as shown in Table 2—it may be that the amount of time spent on the Balances and Positions pages is a more precise estimate of the process of information acquisition, particularly for those investors who also acquire information through other sources such as Yahoo Finance or Bloomberg, for example. To control for possible differences in information acquisition capabilities, Specification 2 in each panel includes demographic characteristics as control variables. Adding these regressors leaves the effect of attention on investment performance largely unchanged for all attention measures, indicating that the relation between these additional regressors and performance is largely orthogonal to that of attention. Among the newly added covariates, the ones significant at the 5% level are age and the brokerage dummy. The first has a negative coefficient—indicating that younger investors achieve a better risk-adjusted performance on their investment portfolio. The second shows instead that brokerage accounts seem to outperform IRA and other accounts, on average, possibly because investors in these accounts are actively seeking underpriced stocks rather than holding broad and diversified passive portfolios. Finally, Specification 3 adds—as further controls—regressors related to portfolio size, portfolio allocation, and trading activity. The inclusion of these additional covariates strengthens the effect of attention on performance by 35–50%, depending on the specification: the overall attention coefficient increases to 2.326%, the research attention coefficient increases to 1.776%, and the balances and positions coefficient increases to 2.007%. All coefficients remain statistically significant at the 1% level, indicating that, even after controlling for portfolio composition and trading frequency, the attention is significantly related to the performance of the investors in our sample. As for the effect of the additional controls, we find a negative relation between portfolio size and number of trades and performance. Interestingly, we find that including the number of trades as a control variable increases the effect of attention on performance, suggesting that paying attention attenuates the negative effects of excessive trading documented in the literature; see Barber and Odean (2000). Table 8 reports results in terms of annualized abnormal percentage returns. To compute the dollar gains per hour spent on the platform for the average investor, we combine the information contained in Tables 3 and 8. A one-standard-deviation increase in attention is associated with an increase in abnormal returns of 1.53%, 1.60%, or 2.33%, depending on the specification. From Table 3, we know that the average portfolio size in our data set is |${\$}94,000$|⁠. The standard deviation of overall attention is equal to 88 hours. Using this information, we can compute the dollar gains associated with each hour of attention. For the first specification, this equals: |${\$}94,000\times1.53\%/88={\$}16.3$|⁠. For Specifications 2 and 3, the dollar gains per hour equal |${\$}94,000\times1.60\%/88={\$}17$| and |${\$}94,000\times2.33\%/88={\$}24.9$|⁠. The dollar gains for each hour of attention therefore range from |${\$}$|16 to |${\$}$|25. The economic significance of these quantities depends on the opportunity cost of each individual investor. The average American has an annual income of |${\$}41,000$|⁠. This converts to |${\$}41,000/52/40 = {\$}19.7$| per hour. The average American who invests in the stock market, however, has an annual income of |${\$}52,000$|⁠, which translates to |${\$}52,000/52/40 = {\$}25$| per hour.21 As a first robustness exercise, we implement the calendar-time approach of Barber and Odean (2000). We first sort the investors into five groups according to attention (from Q1 to Q5). We then compute weekly value-weighted and equally weighted returns for each group and estimate Carhart four-factor regressions on these portfolios. We report the results in Table 9. Going from low- to high-attention and focusing on the equally weighted returns reported in panel A, the five groups have the following annualized alpha estimates 0.03, 0.02, 0.02, 0.03, and 0.06, with |$t$|-statistics equal to 0.96, 0.82, 0.96, 1.15, and 2.02. The alphas increase in significance as we move from the second to the fifth portfolio and only the top portfolio is significant at the 5% level, confirming that risk-adjusted performance is positively related to attention. Finally, we form a portfolio long in the most attentive investors (Q5) and short in the least attentive investors (Q1) and find it has an alpha of 0.04, statistically significant at the 5% level. The value-weighted returns in panel B are aligned with the equally weighted returns. The top two portfolios have significant outperformance—at the 10% significance for Q4 and the 5% significance for Q5. The long-short portfolio in the last column also displays an alpha of 0.04, statistically significant at the 1% level. Table 9 Calendar portfolio returns analysis at the weekly frequency Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 This table displays the results of calendar-time portfolio return regressions. We first sort account holders into five groups (Q1 through Q5) based on the number of seconds spent on the brokerage account website over the full sample. We then compute, for each group, equally weighted weekly returns in panel A and value-weighted weekly returns in panel B. Q1 are the least attentive investors while Q5 are the most attentive. Finally, we run the following time-series regression: \begin{align*} r_{q,t}-rf_{t}=\alpha_{q}+\beta_{1,q}\ (Mkt_{t}-rf_{t})+\beta_{2,q} \ HML_{t}+\beta_{3,q} \ SMB_{t}+\beta_{4,q}\ MOM_{t}+\epsilon_{t} \quad for \,\,\, t=1,\ldots,T \& q=1,\ldots,5, \end{align*} where |$r_{q,t}-rf_{t}$| is the annualized excess-return at time |$t$| of portfolio |$q$|⁠; |$Mkt_{t}-rf_{t}$| is the annualized excess return on the NYSE/AMEX/NASDAQ index, |$HML_{t}$|⁠, |$SMB_{t}$|⁠, and |$MOM_{t}$| are the annualized returns on the value, size, and momentum factors, respectively. The last column of each panel reports performance regressions for a portfolio long in the most attentive investors (Q5) and short in the least attentive investors (Q1). We display the ordinary least squares coefficient estimates and the associated |$t$|-statistics computed using Newey-West standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 9 Calendar portfolio returns analysis at the weekly frequency Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 Panel A. Equally weighted returns Q1 Q2 Q3 Q4 Q5 Q5-Q1 Alpha 0.03 0.02 0.02 0.03 0.06** 0.04** (0.96) (0.82) (0.96) (1.15) (2.02) (2.63) MKT 0.84*** 0.89*** 0.94*** 0.89*** 0.85*** 0.01 (17.33) (18.71) (25.03) (21.73) (15.67) (0.62) SMB 0.03 0.01 0.02 0.06 0.16** 0.13*** (0.46) (0.21) (0.34) (1.06) (2.43) (4.02) HML –0.07 –0.02 –0.01 –0.03 –0.03 0.03 (–1.10) (–0.42) (–0.17) (–0.59) (–0.48) (0.88) UMD –0.15*** –0.10* –0.10** –0.10** –0.07 0.09*** (–2.77) (–1.79) (–2.61) (–2.05) (–1.07) (2.77) |$R^2$| 90% 92% 95% 94% 90% 42% |$N$| 74 74 74 74 74 74 Panel B. Value-weighted returns Alpha 0.06 0.06 0.05 0.06* 0.10** 0.04*** (1.61) (1.52) (1.41) (1.73) (2.30) (2.76) MKT 0.76*** 0.82*** 0.86*** 0.84*** 0.83*** 0.08*** (14.62) (15.38) (16.03) (17.37) (14.11) (3.11) SMB 0.17** 0.16** 0.18*** 0.21*** 0.31*** 0.14*** (2.61) (2.23) (3.20) (3.19) (4.14) (3.29) HML 0.02 0.01 0.05 –0.02 0.01 –0.02 (0.42) (0.22) (1.04) (–0.32) (0.10) (–0.40) UMD –0.11** –0.13** –0.08 –0.07 –0.03 0.09** (–2.07) (–2.26) (–1.59) (–1.21) (–0.42) (2.28) |$R^2$| 87% 89% 92% 91% 87% 56% |$N$| 74 74 74 74 74 74 This table displays the results of calendar-time portfolio return regressions. We first sort account holders into five groups (Q1 through Q5) based on the number of seconds spent on the brokerage account website over the full sample. We then compute, for each group, equally weighted weekly returns in panel A and value-weighted weekly returns in panel B. Q1 are the least attentive investors while Q5 are the most attentive. Finally, we run the following time-series regression: \begin{align*} r_{q,t}-rf_{t}=\alpha_{q}+\beta_{1,q}\ (Mkt_{t}-rf_{t})+\beta_{2,q} \ HML_{t}+\beta_{3,q} \ SMB_{t}+\beta_{4,q}\ MOM_{t}+\epsilon_{t} \quad for \,\,\, t=1,\ldots,T \& q=1,\ldots,5, \end{align*} where |$r_{q,t}-rf_{t}$| is the annualized excess-return at time |$t$| of portfolio |$q$|⁠; |$Mkt_{t}-rf_{t}$| is the annualized excess return on the NYSE/AMEX/NASDAQ index, |$HML_{t}$|⁠, |$SMB_{t}$|⁠, and |$MOM_{t}$| are the annualized returns on the value, size, and momentum factors, respectively. The last column of each panel reports performance regressions for a portfolio long in the most attentive investors (Q5) and short in the least attentive investors (Q1). We display the ordinary least squares coefficient estimates and the associated |$t$|-statistics computed using Newey-West standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. As a second robustness exercise, Online Table 7 shows the results that use number of pages (panel A) or logins (panel B) as measures of attention. While the results are qualitatively similar, they are economically smaller, suggesting that—compared with seconds—pages and logins may be poorer proxies for the process of information acquisition. As a third robustness exercise, we use the full sample Sharpe ratio as a measure of risk-adjusted performance. The results, reported in Online Table 8, highlight a statistically and economically significant relation between attention and performance. For overall attention, the coefficient in the first specification equals 0.090 and is significant at the 1% level. The corresponding coefficient estimates for research and balances and positions are 0.108 and 0.137, respectively. The results imply—taking balances and position attention as an example—that a one-standard-deviation increase in attention is associated with an increase in the investor’s Sharpe ratio of 0.137. Economically, this quantity is quite large, because the average Sharpe ratio across the investors in our sample is 1.316.22 The results reported so far show that investor attention is positively related to risk-adjusted performance, even after controlling for investor characteristics and investment style. As we detail in Section 4.1 and the associated Table 4, account holders with higher exposure to small capitalization stocks, growth stocks, momentum stocks, and the overall market are more attentive. Jointly, therefore, the results reported in Table 4 and Table 8 show that attention has both an indirect and a direct effect on investor performance. The indirect effect is the one related to the type of investment decisions attentive investors make compared with inattentive investors. The direct effect, on the other hand, is the effect of attention on performance, controlling for style and other characteristics. Taking the relation between momentum exposure, attention, and risk-adjusted performance as an example, our results in Table 4 uncover the indirect effect of attention on performance, in that more attentive investors tend to follow momentum strategies — buying (selling) stocks that have increased (decreased) in price over the previous twelve months. The results in Table 8, on the other hand, uncover the direct effect of attention on performance, in that attention retains a positive effect on performance even after controlling for momentum using the DGTW procedure. 5.2 Panel data regressions The results reported so far potentially suffer from a reverse-causality problem. It may not be that investors perform better because they pay more attention, but that investors pay more attention because they have been performing better. The cross-sectional regressions cannot distinguish between these two alternative hypotheses, because they compute performance and attention over the full sample. To separate the two effects, we next estimate panel regressions that identify the effect of attention on performance from time-series variations in attention—measured at the individual level. Our baseline specification is: \begin{align} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m +\gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k}, \label{panel_spec} \end{align} (6) where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$| and |$k$| equals 21, 42, and 63 days—depending on the specification;23|$Abn\_Attention_{i,t}$| is account holder |$i$|’s abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Following Da, Engelberg, and Gao (2011), we use abnormal attention—rather than attention—to capture time trends and other low-frequency seasonalities in investors’ attention. This is important, because investors tend to pay more attention to their investment portfolios right after opening their accounts and they tend to lose interest subsequently. Furthermore, even though time effects are included in the regressions, they are unlikely to capture a large part of the time variation in investors’ attention, because individual investors hold relatively few stocks. It may therefore be that certain investors pay a lot of attention in certain periods—because the stocks they own (or they want to purchase) are in the news—and other investors pay attention in other periods. Finally, to ease the comparison across the specifications, we standardize |$Abn\_Attention$| so that it has zero mean and unit standard deviation. To guarantee that our results are not specific to the type of clustering used in the computation of the standard errors, we report two sets of |$t$|-statistics. The first—in round brackets—are computed using standard errors that are double-clustered by account holder and time; see Petersen (2009). The second—in square brackets—are computed using Driscoll and Kraay (1998) standard errors. We base our discussion on the double-clustered standard errors, because they result in more conservative estimates in our data. Reported in Table 10 are the results based on the overall attention measure. Across all horizons, the estimates highlight a positive and significant relation between abnormal attention and performance. Statistically, the results are significant at the 5% level for the one month horizon and the 1% level at the two- and three-month horizons. Economically, the effect of overall abnormal attention is quite large. At the one-month horizon, we find that a one standard deviation increase in attention is associated with an annualized increase in portfolio DGTW-adjusted performance of |$0.032\times12=0.38\%$| per year. The effect increases to |$0.103\times6=0.62\%$| at the two-month horizon and decreases slightly to |$0.138\times4=0.55\%$| at the three-month horizon. This indicates that paying attention allows individual investors to improve their portfolio performance in the short term, suggesting that the improvement in performance hinges on attentive investors being able to purchase (sell) stocks that—in the short run—realize relatively large positive (negative) returns. Table 10 Attention and performance: panel regression results Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 This table reports panel regression results on the relation between portfolio performance and investor attention. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m+ \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the number of seconds spent on the brokerage account website. We report results for different horizons, that is, |$k=21$|⁠, |$42$|⁠, and |$63$| days. Displayed are the ordinary least squares coefficient estimates and two sets of |$t$|-statistics. The first—in round brackets—are computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). The second—in square brackets—are computed using the Driscoll and Kraay (1998) standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively, according to the double-clustered standard errors. Table 10 Attention and performance: panel regression results Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 Panel A. Overall 21 Days 42 Days 63 Days Abn. Attention 0.032** 0.103*** 0.138*** (⁠|$t$|-statistic) (2.27) (4.21) (3.66) |$[t-statistic]$| [3.58] [7.67] [9.06] Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| Account holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.14% 0.27% 0.38% |$N$| 2,573,951 2,377,766 2,187,222 This table reports panel regression results on the relation between portfolio performance and investor attention. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m+ \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the number of seconds spent on the brokerage account website. We report results for different horizons, that is, |$k=21$|⁠, |$42$|⁠, and |$63$| days. Displayed are the ordinary least squares coefficient estimates and two sets of |$t$|-statistics. The first—in round brackets—are computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). The second—in square brackets—are computed using the Driscoll and Kraay (1998) standard errors. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively, according to the double-clustered standard errors. Panels A and B of Online Table 9 report the results for research attention and balances and positions attention, respectively. The results show that, while the effect of balances and positions attention is economically stronger than overall attention, the effect of research attention is somewhat weaker. For example, the annualized effect for the two-month horizon specification equals |$0.135\times6=0.81\%$| for balances and positions attention and |$0.078\times6=0.47\%$| for research attention. In both cases the coefficients are statistically significant at the 5% level. Across all panels, the coefficients are always more significant if we use Driscoll and Kraay (1998) standard errors. The baseline results reported in Table 10 use a window of 21 days to compute abnormal attention. For robustness, in Online Table 10 and Online Table 11 we compute abnormal attention using windows of 42 and 63 days, respectively. In all cases, the results become stronger if we use a longer window to compute abnormal attention. Table 11 Trading performance and investor attention: baseline regression results 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 This table reports regression results on the relation between investor attention and trading performance. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panel A reports results for stock purchases. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 11 Trading performance and investor attention: baseline regression results 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A. Performance of buys Attention 0.147* 0.342*** 0.533*** 0.383** 0.441** 0.197 0.009 (1.81) (3.03) (3.57) (2.18) (2.28) (0.93) (0.03) Constant –0.139* 0.152 0.349** 0.479*** 0.622*** 0.691*** 0.502 (–1.70) (1.31) (2.31) (2.58) (3.22) (3.30) (1.66) |$R^2$| 0.01% 0.03% 0.05% 0.02% 0.02% 0.00% 0.00% |$N$| 24,139 24,103 24,064 24,001 23,947 23,887 23,436 Panel B. Performance of sells Attention 0.103 0.130 0.287* 0.119 0.118 –0.027 –0.425 (1.23) (1.04) (1.81) (0.66) (0.58) (–0.13) (–1.13) Constant –0.023 0.376*** 0.914*** 1.008*** 1.090*** 1.182*** 1.366*** (–0.27) (2.97) (5.61) (5.42) (5.15) (5.11) (3.88) |$R^2$| 0.01% 0.01% 0.02% 0.00% 0.00% 0.00% 0.01% |$N$| 19,435 19,373 19,320 19,257 19,200 19,142 18,777 This table reports regression results on the relation between investor attention and trading performance. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panel A reports results for stock purchases. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The results reported so far use number of seconds as a measure of attention. Online Table 12 shows that the results reported above are generally less significant when we measure attention using number of pages or logins instead of seconds, suggesting—again—that the latter two measures may be poorer proxies for the process of information acquisition. Table 12 Trading performance and investor attention: portfolio results Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) This table reports portfolio results on the relation between investor attention and the performance of the stocks before (panel B) and after (panel A) they are traded. Each panel reports the average cumulative DGTW-adjusted returns at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panels A.I and B.I present the unconditional results and—for every horizon—report the difference in performance between the stocks purchased and sold as well as the |$p$|-value testing whether the difference in performance is equal to zero. Panels A.II and B.II condition on the overall attention paid by the account holders in the month preceding each trade. We divide the buys in two groups—low and high—based on overall attention and report the performance of the the low- and high-attention buys, respectively. We repeat the same procedure to compute low- and high-attention sells. For every horizon, we report the difference in performance between: (i) high-attention buys and high-attention sells; (ii) low-attention buys and low-attention sells; (iii) high-attention buys and low-attention buys; and (iv) high-attention sells and low-attention sells. In each case, we also report the |$p$|-value testing whether the difference in performance is equal to zero. All |$p$|-values are based on the bootstrap procedure suggested by Barber, Lyon, and Tsai (1999). Estimates marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 12 Trading performance and investor attention: portfolio results Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Return horizon 1 Month 2 Months 3 Months 4 Months 5 Months 6 Months 12 Months Panel A: Future returns Panel A.I. Unconditional results Buy –0.13% 0.17% 0.37% 0.50% 0.64% 0.70% 0.50% Sell –0.02% 0.38% 0.91% 1.01% 1.09% 1.18% 1.37% Buy-Minus-Sell –0.11% –0.21% –0.54%*** –0.51%* –0.45% –0.48% –0.86%** p-val (0.33) (0.19) (0.01) (0.05) (0.17) (0.20) (0.04) Panel A.II. Results conditional on overall attention Buy-High 0.00% 0.42% 0.79% 0.85% 0.98% 0.88% 0.35% Buy-Low –0.28% –0.10% –0.07% 0.12% 0.28% 0.51% 0.67% Sell-High 0.06% 0.38% 1.05% 0.92% 1.00% 0.92% 0.69% Sell-Low –0.11% 0.37% 0.77% 1.10% 1.18% 1.45% 2.05% Buy-High minus Sell-High –0.06% 0.04% –0.26% –0.07% –0.02% –0.04% –0.34% p-val (0.68) (0.84) (0.29) (0.83) (0.96) (0.94) (0.54) Buy-Low minus Sell-Low –0.17% –0.48%** –0.84%*** –0.98%*** –0.89%** –0.94%** –1.39%*** p-val (0.31) (0.05) (0.00) (0.00) (0.01) (0.02) (0.01) Buy-High minus Buy-Low 0.28%** 0.53%*** 0.86%*** 0.73%** 0.70% 0.36% –0.32% p-val (0.03) (0.00) (0.00) (0.03) (0.24) (0.73) (0.56) Sell-High minus Sell-Low 0.17% 0.01% 0.28% –0.18% –0.17% –0.53% –1.36%** p-val (0.30) (0.97) (0.46) (0.73) (0.83) (0.45) (0.03) Panel B: Past returns Panel B.I. Unconditional results Buy 1.95% 3.42% 5.03% 7.14% 8.33% 10.30% 26.75% Sell 2.38% 3.93% 5.21% 6.97% 8.12% 9.76% 22.08% Buy-Minus-Sell –0.43%*** –0.51%*** –0.18% 0.17% 0.21% 0.53%* 4.67%*** p-val (0.00) (0.00) (0.48) (0.47) (0.44) (0.08) (0.00) Panel B.II. Results conditional on overall attention Buy-High 3.13% 4.83% 6.98% 9.99% 11.56% 14.09% 34.50% Buy-Low 0.70% 1.92% 2.96% 4.11% 4.91% 6.27% 18.51% Sell-High 3.55% 5.40% 6.92% 9.37% 10.88% 13.17% 30.57% Sell-Low 1.19% 2.43% 3.46% 4.51% 5.30% 6.29% 13.43% Buy-High minus Sell-High –0.42%*** –0.57%** 0.06% 0.62%* 0.67%* 0.93%** 3.94%*** p-val (0.01) (0.01) (0.85) (0.05) (0.07) (0.03) (0.00) Buy-Low minus Sell-Low –0.49%*** –0.51%*** –0.50%** –0.40% –0.39% –0.02% 5.08%*** p-val (0.00) (0.00) (0.05) (0.17) (0.26) (0.96) (0.00) Buy-High minus Buy-Low 2.43%*** 2.91%*** 4.02%*** 5.88%*** 6.65%*** 7.82%*** 15.99%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Sell-High minus Sell-Low 2.36%*** 2.97%*** 3.46%*** 4.85%*** 5.58%*** 6.88%*** 17.14%*** p-val (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) This table reports portfolio results on the relation between investor attention and the performance of the stocks before (panel B) and after (panel A) they are traded. Each panel reports the average cumulative DGTW-adjusted returns at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panels A.I and B.I present the unconditional results and—for every horizon—report the difference in performance between the stocks purchased and sold as well as the |$p$|-value testing whether the difference in performance is equal to zero. Panels A.II and B.II condition on the overall attention paid by the account holders in the month preceding each trade. We divide the buys in two groups—low and high—based on overall attention and report the performance of the the low- and high-attention buys, respectively. We repeat the same procedure to compute low- and high-attention sells. For every horizon, we report the difference in performance between: (i) high-attention buys and high-attention sells; (ii) low-attention buys and low-attention sells; (iii) high-attention buys and low-attention buys; and (iv) high-attention sells and low-attention sells. In each case, we also report the |$p$|-value testing whether the difference in performance is equal to zero. All |$p$|-values are based on the bootstrap procedure suggested by Barber, Lyon, and Tsai (1999). Estimates marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. The results in Table 10 do not account for trading fees. Barber and Odean (2000) show that transaction costs can substantially reduce the post-fee returns of individual investors, as the average total cost of a round-trip trade was approximately 4% in 1999. Thanks to technological progress and increased competition among brokers, trading fees are much lower nowadays: it is possible to trade many ETFs without incurring any fee, and the majority of the brokers charge less than |${\$}$|10 for online trades—a very small amount compared with the average trade size of |${\$}$|16,000 reported in Table 3. Furthermore, brokers often offer their customers promotions whereby trading fees are waived for a certain number of trades and/or a certain period of time. Because we do not observe these promotions in our data set, we assume that every trade is charged the maximum fee advertised by our broker and reestimate Equation (6) using abnormal DGTW returns adjusted for trading fees. Compared to Table 10 and Online Table 9, the coefficients in Online Table 13 are only slightly smaller, but still strongly significant, indicating that controlling for trading fees does not affect our results. Table 13 Attention, attention specialization, and portfolio performance: cross-sectional results Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 This table reports regression results on the relation between portfolio performance and investor attention specialization. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta_1 \ {Attention_i} +\beta_2 \ Herfindahl\_Researched_{i}+ \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the total attention spent on the brokerage account website by account holder |$i$| over the sample period, |$Herfindahl\_Researched_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. Attention is measured as the log of the total number of minutes spent on the brokerage account website in panel A, the log of the total number of minutes spent on the Research pages of the brokerage account website in panel B, and the log of the total number of minutes spent on the Balances and Positions pages of the brokerage account website in panel C. Each panel includes a group of covariates that control for investor demographic characteristics, portfolio holdings, and trading activity variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account; Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 13 Attention, attention specialization, and portfolio performance: cross-sectional results Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 Panel A. Overall Panel B. Research Panel C. Balances and Positions Attention 3.391*** 2.965*** 2.415** (0.00) (3.38) (2.26) Herfindahl researched 1.861*** 2.231*** 1.639** (2.64) (2.77) (2.49) Brokerage 2.773** 3.161** 3.279** (2.14) (2.53) (2.51) Male 0.703 0.716 0.908 (0.55) (0.57) (0.71) Age –0.929* –0.796 –0.804 (–1.81) (–1.57) (–1.56) Account age –0.519 –0.592 –0.515 (–0.86) (–0.98) (–0.85) Portfolio value –0.817*** –0.642** –0.772*** (–2.84) (–2.42) (–2.64) Fr. in cash 1.324 1.292 1.397 (1.04) (1.01) (1.11) Fr. in ETF 0.556 0.512 0.572 (0.95) (0.87) (1.01) Fr. in mutual fund 0.219 0.159 0.150 (0.53) (0.39) (0.37) Number of stocks traded –1.181*** –0.889*** –1.047*** (–3.29) (–2.64) (–2.66) Constant –0.034 –0.716 –0.040 (–0.03) (–0.62) (–0.03) |$R^2$| 0.6% 0.5% 0.5% |$N$| 3,677 3,677 3,677 This table reports regression results on the relation between portfolio performance and investor attention specialization. We estimate the following baseline cross-sectional regression: \begin{align*} AVG\_DGTW\_Ret_{i}=\alpha+\beta_1 \ {Attention_i} +\beta_2 \ Herfindahl\_Researched_{i}+ \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N \end{align*} where |$AVG\_DGTW\_Ret_{i}$| is the annualized percentage average DGTW abnormal return of investor |$i$| over the sample, |$Attention_{i}$| is the total attention spent on the brokerage account website by account holder |$i$| over the sample period, |$Herfindahl\_Researched_{i}$| is the normalized Herfindahl index of the stocks researched by investor |$i$| over the sample period, |$\boldsymbol{x}_i$| is a vector of covariates associated with account holder |$i$|⁠, and |$N$| is the total number of account holders included in the analysis. Attention is measured as the log of the total number of minutes spent on the brokerage account website in panel A, the log of the total number of minutes spent on the Research pages of the brokerage account website in panel B, and the log of the total number of minutes spent on the Balances and Positions pages of the brokerage account website in panel C. Each panel includes a group of covariates that control for investor demographic characteristics, portfolio holdings, and trading activity variables: Brokerage, a brokerage account dummy; Male, a male dummy variable; Age, the age of the investor; Account Age, the age of the account; Portfolio Value, the total value of the invested portfolio; Fr. in Cash, Fr. in ETF, and Fr. in Mutual Fund, the fraction of the total wealth in the brokerage account held in cash, traded funds, and mutual funds, respectively; and N. of Stocks Traded, the number of stocks traded over the period. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Finally, while the results in Table 10 use monthly time effects, we find that the results are virtually unchanged when we use daily time effects—as we show in Online Table 14. Table 14 Attention and performance: panel regression results by groups Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 This table reports panel regression results on the relation between portfolio performance and investor attention for five groups of account holders (Columns |$Q1$| to |$Q5$|⁠) based on the total number of seconds spent on the brokerage account website. |$Q1$| denotes the bottom quintile, that is, the least attentive group of account holders, while |$Q5$| denotes the top quintile, that is, the most attentive group of account holders. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m + \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the seconds spent on the brokerage account website. Panels A, B, and C report results for |$k=21$|⁠, |$42$|⁠, and 63 days, respectively. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics, computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 14 Attention and performance: panel regression results by groups Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 Q1 Q2 Q3 Q4 Q5 Panel A. 21 days Abn. attention 0.241** 0.086* 0.070* 0.039 –0.023 (⁠|$t$|-statistic) (2.24) (1.76) (1.95) (1.55) (–1.09) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.13% 0.13% 0.14% 0.13% 0.18% |$N$| 421,489 510,043 531,510 537,771 573,138 Panel B. 42 days Abn. attention 0.412** 0.206** 0.222*** 0.113*** –0.012 (⁠|$t$|-statistic) (2.41) (2.53) (3.06) (2.82) (–0.35) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$R^2$| 0.25% 0.25% 0.27% 0.23% 0.32% |$N$| 390,144 471,115 490,610 496,060 529,837 Panel C. 63 days Abn. attention 0.588** 0.271** 0.415*** 0.161** –0.066 (⁠|$t$|-statistic) (2.41) (2.49) (3.04) (2.80) (–1.31) Time FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| Acct Holder FE |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| |$\checkmark$| R-Squared 0.36% 0.36% 0.36% 0.33% 0.44% N 359,282 433,371 451,065 455,663 487,830 This table reports panel regression results on the relation between portfolio performance and investor attention for five groups of account holders (Columns |$Q1$| to |$Q5$|⁠) based on the total number of seconds spent on the brokerage account website. |$Q1$| denotes the bottom quintile, that is, the least attentive group of account holders, while |$Q5$| denotes the top quintile, that is, the most attentive group of account holders. We estimate the following baseline panel regression: \begin{align*} DGTW\_Ret_{i,t:t+k}=\alpha_i+ \beta_m + \gamma \ {Abn\_Attention_{i,t}} + \epsilon_{i,t:t+k} \quad for \,\,\, i=1,\ldots,N \ \ \& \ \ \ t=1,\ldots,T, \end{align*} where |$DGTW\_Ret_{i,t:t+k}$| is the DGTW-adjusted portfolio return of account holder |$i$| over the time interval |$t:t+k$|⁠; |$Abn\_Attention_{i,t}$| is account holder |$i$| abnormal attention at time |$t$|⁠, computed as the difference between the (log) attention on day |$t$| and the (log) average attention over the previous 21 business days; finally, |$\alpha_i$| and |$\beta_m$| represent account holder fixed effects and monthly time effects, respectively. Attention is measured as the seconds spent on the brokerage account website. Panels A, B, and C report results for |$k=21$|⁠, |$42$|⁠, and 63 days, respectively. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics, computed using standard errors that are double-clustered by account holder and time, see Petersen (2009). Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. 6. Attention and Performance: Evidence from Investor Trades In an effort to understand the mechanism through which higher investor attention relates to superior portfolio performance, in this section we analyze how attention affects the profitability of investors’ active management decisions, that is, their trades. We first relate the performance of the stocks bought and sold by individual investors to the attention they pay to their investment portfolio in the month preceding each trade. We take each individual’s trade as the unit of observation, and we measure performance as the risk-adjusted returns of the stock over the one-, two-, all the way to twelve-month period after the trade has been placed. The adjustment for risk is performed using the DGTW model (see Daniel et al. 1997) but the results are similar if we use simple or market-adjusted returns. We then provide results showing that the positive effect of attention is—at least in part—due to the fact that high-attention individuals purchase attention-grabbing stocks that have appreciated in the recent past and whose positive performance persists for up to six months. 6.1 Baseline parametric results We start by presenting pooled regression estimates in Table 11, testing whether there is a positive relation between investor attention and stock performance. We separate the buys and the sells, and estimate the regressions: \begin{align} DGTW\_Ret\_Buys_{i,j,t:t+k}=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \label{performance_regression}\\ \end{align} (7) \begin{align} DGTW\_Ret\_Sells_{i,j,t:t+k}=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \label{performance_regression_2} \end{align} (8) where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Note that—to make the results more interpretable—we scale |$Attention_{i,j,t}$| so that it has zero mean and unit variance. Finally, cumulative abnormal returns are computed at the one-, two-, three-, four-, five-, six-, and twelve-month horizons. Panel A focuses on the returns of the buys. We find a positive relation (statistically significant at the 5% level) between the total attention investors spend on the trading platform and the performance of their trades at the two-, three-, four-, and five-month horizons. The relation is also positive, but only statistically significant at the 10% level, at the one-month horizon. The results are also economically significant. At the three-month horizon, for example, a unit-standard-deviation increase in attention increases the average annualized adjusted returns of the stocks purchased by |$0.533\%\times 4=2.13\%$|⁠. The results for the two specialized measures of attention are very much in line with the overall attention results. For example, at the three-month horizon, the effect equals |$0.489\%\times 4=1.96\%$| and |$0.408\%\times 4=1.63\%$| for research and balances and positions attention, respectively. The results for research attention and balances and positions are reported in Online Table 15. Compared with overall attention, research attention seems to have a stronger effect at the one-month horizon and a slightly smaller effect at the five-month horizon. Finally, the results for balances and positions are somewhat weaker, as the results are significant at the two- and three-month horizons, but they just miss significance at the four- and five-month horizons. Table 15 Trading performance and investor attention: regression results conditioning on stock characteristics Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 This table reports regression results on the relation between investor attention and trading performance, conditioning on the characteristics of the stocks traded. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the three—month horizon. Panel A reports results for stock purchases, and measures attention as overall attention. The results are computed separately for stocks with low and high values of the conditioning variables. The conditioning variables used are (from left to right): Volume, the volume of the stock on the day of the trade; Size, computed as the log of the market price multiplied by the number of shares outstanding; Volatility, computed as the realized volatility of the stock over the previous month; Disagreement, the standard deviation of the analysts’ earnings-per-share forecasts; Num. Analysts, the log of the number of analysts covering the stock during the quarter the trade takes place; and News, the number of news from Capital IQ over the previous month. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Table 15 Trading performance and investor attention: regression results conditioning on stock characteristics Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 Volume Size Volatility Disagreement Num. Analysts News Low High Low High Low High Low High Low High Low High Panel A. Performance of buys Attention 0.346 0.706*** 0.261 0.764*** 0.330*** 0.773*** 0.233 0.824*** 0.318 0.711*** 0.091 0.859*** (1.37) (3.98) (0.84) (7.46) (3.18) (2.98) (1.23) (3.60) (1.20) (5.23) (0.32) (5.74) Constant 0.140 0.535*** 0.261 0.416*** 0.178* 0.610** 0.793*** 0.435* 0.527* 0.387*** 0.288 0.423*** (0.55) (2.95) (0.82) (4.32) (1.81) (2.17) (4.40) (1.70) (1.96) (2.95) (0.99) (2.58) |$R^2$| 0.02% 0.11% 0.01% 0.45% 0.10% 0.06% 0.01% 0.09% 0.01% 0.24% 0.00% 0.22% |$N$| 11,450 12,614 10,886 13,178 10,892 12,309 12,266 11,178 12,223 11,648 10,431 13,633 Panel B. Performance of sells Attention 0.390 0.253 0.074 0.427*** 0.205* 0.219 0.049 0.515** 0.182 0.392*** 0.261 0.306* (1.55) (1.25) (0.23) (4.10) (1.88) (0.76) (0.27) (1.96) (0.66) (2.57) (0.86) (1.81) Constant 0.291 1.499*** 1.098*** 0.767*** 0.023 1.892*** 0.852*** 1.382*** 1.305*** 0.712*** 0.915*** 0.915*** (1.15) (7.11) (3.33) (7.05) (0.20) (6.28) (4.49) (5.00) (4.52) (4.88) (3.07) (5.27) |$R^2$| 0.03% 0.02% 0.00% 0.15% 0.04% 0.01% 0.00% 0.04% 0.00% 0.08% 0.01% 0.03% |$N$| 9,401 9,919 8,711 10,609 9,057 9,685 9,928 8,896 9,744 9,427 8,290 11,030 This table reports regression results on the relation between investor attention and trading performance, conditioning on the characteristics of the stocks traded. We separate the buys (i.e., stock purchases) and the sells (i.e., stock sales) and estimate the following pooled regressions: \begin{align*} DGTW\_Ret\_Buys_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k},\\ DGTW\_Ret\_Sells_{i,j,t:t+k}&=\alpha+\beta \ Attention_{i,j,t}+\epsilon_{i,j,t:t+k}, \end{align*} where |$DGTW\_Ret\_Buys_{i,j,t:t+k}$| (⁠|$DGTW\_Ret\_Sells_{i,j,t:t+k}$|⁠) are the cumulative abnormal returns of security |$j$| bought (sold) by investor |$i$| over the time interval |$t : t + k$|⁠, computed using the DGTW model; |$Attention_{i,j,t}$| is the (log) number of seconds spent on the brokerage account website by investor |$i$| over the month preceding the trade in stock |$j$| that occurs at time |$t$|⁠. Cumulative abnormal returns are computed at the three—month horizon. Panel A reports results for stock purchases, and measures attention as overall attention. The results are computed separately for stocks with low and high values of the conditioning variables. The conditioning variables used are (from left to right): Volume, the volume of the stock on the day of the trade; Size, computed as the log of the market price multiplied by the number of shares outstanding; Volatility, computed as the realized volatility of the stock over the previous month; Disagreement, the standard deviation of the analysts’ earnings-per-share forecasts; Num. Analysts, the log of the number of analysts covering the stock during the quarter the trade takes place; and News, the number of news from Capital IQ over the previous month. Panel B repeats the exercise for stock sales. Displayed are the ordinary least squares coefficient estimates and associated |$t$|-statistics. Standard errors are adjusted for heteroscedasticity. Coefficients marked with ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively. Panel B reports the performance of the sells. For overall attention, we find that none of the coefficients are significant at the 5% level, and only one coefficient is significant at the 10% level—indicating that there is virtually no relation between overall attention and the performance of the stocks sold by investors. Once again, the results for research attention and balances and positions attention (Online Table 15) are very much aligned. The only exception is the relation between research attention and returns at the one- and two-month horizons, which is positive and significant at the 5% level. Overall, the results in Table 11 show that there is a positive effect of attention on the stocks purchased, but not on the stocks sold. This suggests both that the time spent on the brokerage account website is dedicated to searching for new stocks and that the investors are able to translate the information they acquire into profitable trades.24 6.2 Nonparametric results To corroborate the parametric results reported above, we now report evidence based on portfolio sorts. Before analyzing the effect of attention on trading performance, we first confirm the findings in Odean (1999) that—unconditionally—the stocks purchased by individual investors systematically underperform the ones sold. Panel A.I of Table 12 reports the average cumulative abnormal returns at the one-, two-, three-, four-, five-, six-, and twelve-month horizons after stock purchases and sales. For every horizon, we also report the difference in performance between the two groups and a bootstrap |$p$|-value testing whether the difference is equal to zero.25 We find that, at every horizon, the stocks sold outperform the ones purchased and the difference is statistically significant for three out of seven horizons. For example, the average annualized three-month abnormal return of the stocks purchased equals |$0.37\%\times4=1.48\%$|⁠, the one for the stocks sold equals |$0.91\%\times4=3.64\%$|⁠, and their difference is statistically significant with a |$p$|-value of 0.01. While in line with Odean (1999), our results are somewhat weaker, possibly due to our sample covering the 2013–2014 bull market. Panel A.II conditions the trades on the overall attention paid by investors to their investment portfolio in the month preceding each trade. We divide the buys in two groups—low and high—and report their performance. We do the same for the sells. The results indicate that overall attention is strongly related to the performance of investor trades. For low-attention trades, we find that Odean (1999)’s bias is very strong. Low-attention buys strongly underperform the low-attention sells in a statistically significant fashion: the average annualized three-month abnormal return of the stocks purchased equals |$-0.07\%\times4=-0.28\%$|⁠, the one for the stocks sold equals |$0.77\%\times4=3.08\%$|⁠, and their difference is statistically significant, with a |$p$|-value of 0.00. On the other hand, even though the bias is not overturned for high-attention trades, high-attention buys do not underperform the high-attention sells in a statistically significant fashion: the average annualized three-month abnormal return of the stocks purchased equals |$0.79\%\times4=3.16\%$|⁠, the one for the stocks sold equals |$1.05\%\times4=4.2\%$|⁠, and their difference is statistically insignificant, with a |$p$|-value of 0.29. While these results indicate that the behavioral bias first discovered by Odean (1999) is smaller for high-attention trades, it is difficult to relate this behavioral bias to portfolio performance. This is because the returns that affect the performance of the investors are the ones of the stocks they hold in their portfolio. Even if the performance of the stock an investor sells is superior to the one he or she buys, it is not clear that he or she will underperform the market as a result. This is simply because both stocks may be delivering a return superior to that of the market in the subsequent period. To assess the relation between attention and performance, it is preferable to focus on overall portfolio performance, as we do in Tables 8, 9, and 10. We also find that the high-attention buys strongly outperform the low-attention buys up to four months and the difference becomes insignificant thereafter. Economically, the difference in performance is large: the average annualized three-month difference between high- and low-attention purchases equals |$3.16\%-(-0.28\%)=3.44\%$| and is statistically significant, with a |$p$|-value of 0.00. Finally, we find no significant impact of overall attention on the performance of the stocks sold as low- and high-attention sells have similar performance. The results reported above show that, the more time investors spend on their trading account, the higher the performance of the stocks they buy. In an effort to uncover the main drivers of the results, we show below that the more investors pay attention to their brokerage account, the more they purchase attention-grabbing stocks that have appreciated in the recent past and continue to appreciate for up to six months, and this seems to be the source of their outperformance. 6.3 Understanding the results While on the brokerage account website, investors may be looking at a wide range of information, such as firms’ accounting data, news, and analysts’ reports—for example. One obvious piece of information that may attract investors’ attention is the past performance of the securities they are considering buying or selling. Moskowitz, Ooi, and Pedersen (2012) document time-series momentum, that is, the positive predictability from a security’s own past returns that persists for up to twelve months. To test whether time-series momentum can explain our results, we conduct two complementary exercises. First, we estimate the relation between investors’ attention and the past performance of the stocks they trade using a nonparametric symmetric nearest neighbor approach. Second, we repeat the analysis contained in panel A of Table 12, but focus on the performance of the stocks before the trades occur, rather than after. The results of this analysis are reported in panel B of Table 12. Starting from the unconditional results, panel B.I shows that investors tend to both buy and sell stocks that have appreciated in the past, as shown in Odean (1999). Panel B.II sharpens the results, as it uncovers a very strong relation between the attention spent by investors and the risk-adjusted returns of the stocks they trade. For example, at the one-month horizon, panel B.II of Table 12 shows that the average past performance of the high-attention buys (sells) equals 3.13% (3.55%), the past performance of the low-attention buys (sells) equals 0.70% (1.19%), and the difference in performance between the high- and low-attention buys (sells) is statistically significant, with a |$p$|-value equal to 0.00 (0.00). For the same one-month horizon, panel A of Figure 3 displays a very strong positive and virtually monotonic relation between log attention and past performance.26 The remaining panels of Figure 3 and the remaining columns of Table 12, panel B.II, show that the pattern is very robust across horizons. Figure 3 View largeDownload slide Investor attention and past performance of stocks traded This figure reports the relation between investors’ attention and the past performance of the stocks they trade using a nonparametric symmetric nearest neighbor approach. Attention is computed as the log total number of seconds spent by each investor over the month preceding each trade. Stocks’ past performance is computed over the previous month in the top-left panel, the previous three months in the top-right panel, the previous six months in the bottom-left panel and the previous twelve months in the bottom-right panel. In each panel, the trades are divided into stock purchases and stock sales. The past performance of the stocks purchased is represented by a solid red line, while the past performance of the stocks sold is represented by a dashed black line. Figure 3 View largeDownload slide Investor attention and past performance of stocks traded This figure reports the relation between investors’ attention and the past performance of the stocks they trade using a nonparametric symmetric nearest neighbor approach. Attention is computed as the log total number of seconds spent by each investor over the month preceding each trade. Stocks’ past performance is computed over the previous month in the top-left panel, the previous three months in the top-right panel, the previous six months in the bottom-left panel and the previous twelve months in the bottom-right panel. In each panel, the trades are divided into stock purchases and stock sales. The past performance of the stocks purchased is represented by a solid red line, while the past performance of the stocks sold is represented by a dashed black line. Past returns are just one attention-grabbing proxy. Another proxy proposed in the literature is abnormal volume; see Gervais, Kaniel, and Mingelgrin (2001). Gervais, Kaniel, and Mingelgrin (2001) find evidence that stocks experiencing unusually high volume tend to appreciate for up to five to six months. Finally, we consider the number of news events. Past returns, abnormal volume, and number of news events are all attention-grabbing proxies, but have slightly different flavors. The first relates to the past returns of the stock. The second relates to how much the stock is traded. The third relates to how much the company is covered in the news. To assess which one better explains our attention results, we first estimate univariate regressions of the attention spent before any given trade on each proxy. We then include all proxies together and conduct a horse race between them. We report the results in Online Table 18 separately for buys (panel A) and sells (panel B). To ease the comparison among the estimated coefficients, we standardize the regressors so that they have zero mean and unit variance. The results in panel A show that, the higher the attention of a stock purchase, the more it relates to an attention-grabbing stock. The coefficient is positive and significant for all proxies. When we include all proxies in the same specification, we find that past abnormal returns is the most significant regressor, with a |$t$|-statistic of 4.53. The coefficient on abnormal volume has a |$t$|-statistic of 2.32 and a coefficient that is half the magnitude. Finally, past news is negative and barely significant. The results in panel B are similar, but abnormal returns and abnormal volume have a similar relation to the attention associated with the stocks sold. To test whether the relation between attention and performance remains significant after the inclusion of attention-grabbing proxies, we reestimate the results in Table 11 including abnormal volume and news events as additional controls. We find that the coefficient estimates and their significance remain unchanged. These results—in conjunction with the ones contained in Table 11 and panel A of Table 12—paint the following picture. Investors who pay a lot of attention to their trading accounts tend to trade attention-grabbing stocks that have appreciated greatly in the past. The good past performance persists into the future for the purchases, as we find that the high-attention buys significantly outperform the low-attention buys up to four to five months. The outperformance is relatively short-lived, however, as we find it disappears for horizons greater than five months. 7. Extensions and Additional Analyses In the previous sections we showed that higher attention is related to superior performance. We now provide a set of additional analyses to further understand our main findings. We start by providing extensions of the cross-sectional performance regressions and show a positive relation between specialization in information acquisition and investment performance. We continue by providing extensions of the baseline panel results and show that the positive relation between attention and performance is mainly driven by the positive effect of attention for those investors that, in general, are rather inattentive. Finally, we provide extensions of the baseline trading results and show that paying attention is particularly profitable when trading stocks that have high market capitalization, trading volume, volatility, number of analysts, dispersion of analyst forecasts, and news. 7.1 Specialization in information acquisition and investment performance The baseline cross-sectional performance regressions show there is a positive relation between attention and investment performance. In this section, we dig deeper and assess whether the degree of specialization in information acquisition also has an impact on investment performance. We focus only on those individuals who acquire stock specific information over the sample, and we measure the degree of specialization using the Herfindahl index of the stocks researched. For investor |$i$|⁠, this quantity is computed as |$\frac{\sum_{k=1}^{K} \omega_{k}^{2} - 1/K}{1-1/K}$| if |$K>1$| and 1 if |$K=1$|⁠, where |$\omega_{k}$| is the fraction of the total stock-specific attention of investor |$i$|—measured in seconds—allocated to stock |$k$| over the full sample and |$K$| is the total number of stocks researched. The measure is bounded between 0 —attention allocated evenly across all assets—and 1—all attention concentrated in one asset. We then add this regressor to the baseline specification in Equation (5) and estimate: \begin{align} AVG\_DGTW\_Ret_{i}&=\alpha +\beta_1 \ {Attention_i} + \beta_2 \ {Herfindahl\_Researched_i} \notag \\ &\quad + \boldsymbol{x}'_i\ \boldsymbol{\gamma}+\epsilon_{i} \quad for \,\,\, i=1,\ldots,N, \label{cross_spec_extension} \end{align} (9) where |$Herfindahl\_Researched_i$| is the normalized Herfindahl index of the stocks researched by investor |$i$| over the sample and the remaining regressors are the same as in Equation (5). The regression estimates, reported in Table 13, suggest two main findings. First, even when we estimate our results on the subset of investors that collect stock-specific information on the brokerage account website, the effect of the covariate |$Attention$| is positive and significant. This is true if we use Overall, Research or Balances and Positions attention. Second, the more investors specialize their attention, that is, the higher the Herfindahl index of the stocks researched, the superior the performance. The results highlight that, for a given level of attention, those investors that specialize and focus their research efforts on specific stocks perform better, compared with those who evenly allocate their attention across the stocks in their information set. Economically, the magnitudes are large. For overall attention (panel A), the |$\beta_2$| coefficient equals 1.861% and is significant at the 1% level. The corresponding coefficient estimates for research (panel B) and balances and positions (panel C) are 2.231% and 1.639%, respectively. As a final exercise, we estimate the effect of paying attention for those that display more and less specialization in information acquisition. To this end, we divide the investors for which we have stock-specific attention in two groups on the basis of the Herfindahl index of the stocks they research and reestimate Specification 5 separately for the two groups. The coefficient of interest |$\beta$| equals 2.40 for the less specialized individuals and 3.92 for the more specialized individuals. If we repeat the dollar gains per hour calculations associated with Table 8, we find that the effects are economically quite large. For the low-specialization investors, they equal |${\$}94,000 \times 2.40\%/88 = {\$}25.6$|⁠. For the high-specialization investors, they equal |${\$}94,000 \times 3.92\%/88 = {\$}41.66$|⁠.27 7.2 Panel portfolio results by investor type To assess whether the effects estimated in Table 10 are driven by those investors that—on average— pay a lot of attention or by those that rarely pay attention, we divide our account holders in five quintiles based on their overall total attention over the sample and—for each group—we reestimate Equation (6). Table 14 presents the results for the 21-, 42-, and 63-day horizons in panels A, B, and C, respectively. Moving from left to right, the results for Columns Q1 through Q5 report estimates for account-holders with greater degrees of overall attention. For the shortest horizon of 21 days (panel A), only the investors with low to medium attention (Q1 through Q3) seem to benefit from paying more attention. The effect is instead not statistically significant for high-attention investors (Q4 and Q5). Furthermore, the coefficients are monotonically decreasing as we move from low- attention to high-attention individuals. The results in panels B and C display similar patterns. The coefficients on abnormal attention decrease as average attention increases and become negative for the investors who pay the most attention. Also, the coefficients for the first four groups are all consistently significant at the 5% level. For the last group, on the other hand, we never find a significant effect of attention on performance. Taken together, the results in Table 14 uncover diminishing returns to abnormal attention. Abnormal attention for those investors who rarely pay attention to their account seems to improve their portfolio allocation. On the other hand, those who tend to spend a lot of time on their brokerage account do not seem to benefit from additional time researching stocks and worrying about their portfolio positions. 7.3 Investor trades results controlling for stock characteristics To sharpen the results in Table 11 and understand under what circumstances it pays the most to pay attention, we condition the performance of the trades on the characteristics of the stocks traded. We use the volume of the stock on the day of the trade, its volatility, and the disagreement between analysts’ earnings-per-share forecasts as alternative measures of valuation uncertainty. We use the market capitalization of the company, the number of analysts, and the number of news as proxies for the amount of public information available when placing a trade. For each conditioning variable we divide the trades in two groups: the low group contains the trades associated with firms whose characteristic is below the median; the high group contains the trades associated with firms whose characteristic is above the median. We then reestimate Equations (7) and (8) separately for each group. The results, displayed in Table 15, are based on the three-month horizon and are reported separately for stock purchases (panel A) and sales (panel B).28 A clear pattern emerges from panel A. Attention is associated with higher future returns when account holders trade companies with high valuation uncertainty, that is, stocks with high volume, volatility, and analysts’ disagreement. The same is true for companies with a larger amount of public information, that is, stocks with greater market capitalization, number of analysts, and number of news events. Statistically, all coefficients in the high group are significant at the 1% level, while only one coefficient is significant in the low group—the one associated with volatility. The two groups also differ in terms of economic significance. For example, a one-standard-deviation increase in attention leads to a |$0.764\% \times 4= 3.06\%$| increase in future adjusted returns for stocks with high market capitalization, while for stocks with low market capitalization the increase is |$0.261\% \times 4= 1.04\%$|⁠. While weaker than the results for the buys, attention seems to be related to the performance of the sells for stocks with high uncertainty and high levels of public information. For two conditioning variables out of five—market capitalization and number of analysts—the coefficients are significant at the 1% level for the high group. Furthermore, for high analysts’ disagreement and news, the coefficients on attention are significant at the 5% and 10% levels, respectively. The fact that higher attention is related to greater future performance after stock sales, combined with the known fact that investors rarely realize losses, possibly suggests that—in situations of high uncertainty —paying high attention leads investors to sell their stocks too early, while they are still appreciating. None of the coefficients for the low group are significant at the 1% level, and only one coefficient—the one for volatility—is significant at the 10% level. 8. Conclusions We use a novel brokerage account data set to study how individual investors pay attention to their investment portfolio and how investor attention relates to investment performance. We find a very large heterogeneity in attention across investors that can be explained by the size and risk of the investors’ portfolios, as well as by investor trading habits and demographic characteristics. We also uncover a large heterogeneity in the degree of specialization in information choice across investors. On average, investors research thirty-one individual stocks per year. The number of stocks they research is positively related to the overall amount of time they spend on the website, the number of stocks in their portfolio, and the number of stocks they trade. We also find that investors who hold fewer stocks, have more concentrated portfolios, trade less, and have a smaller fraction of their portfolio in cash allocate their attention in a more concentrated fashion. Finally, we find that investors pay more attention to local stocks, stocks with a higher weight in their portfolio, stocks that have a higher squared Sharpe ratio, and stocks of companies with a larger market capitalization—as predicted by Van Nieuwerburgh and Veldkamp (2009, 2010). We study the relation between investor attention and investment performance both at the portfolio returns level and at the individual trades level. We find a strong and positive cross-sectional relation between attention and performance, in that more attentive investors achieve higher portfolio risk-adjusted returns and Sharpe ratios, even after controlling for covariates related to investment style. Using panel regressions that control for investor skills using fixed effects, we also show that periods of higher attention are related to superior future portfolio performance. We find similar results when we focus on the performance of individual trades. Attention is positively related to the future performance of the stocks purchased up to four months after the trade is placed. We find—on the other hand—no discernible effect of attention on the performance of the stocks sold. To understand the economic mechanism relating attention and trading profitability, we conduct a number of auxiliary exercises. First, by analyzing the characteristics of the stocks before they are traded, we show that the superior performance of high-attention investors arises because they purchase attention-grabbing stocks that have appreciated in the recent past and whose positive performance persists for up to six months. Second, we show that attention is particularly profitable when investors trade stocks with high market capitalization, trading volume, volatility, number of analysts, dispersion of analyst forecasts, and news—indicating that it is for the stocks with high uncertainty, but for which a lot of public information is available, that it pays to pay attention. Our study contributes to the literature that studies the behavior of individual investors with information capacity constraints, in that we provide empirical evidence on how investors allocate their attention. We also contribute to the literature that studies the performance of individual investors. While the majority of the literature focuses on investor mistakes, we show that paying attention relates to better investment decisions. We would like to thank Stijn Van Nieuwerburgh (the editor) and two anonymous referees for their comments and suggestions. The paper has benefited from the comments made at presentations at the NBER 2016 behavioral meeting, the 2017 Conference on Financial Decisions and Asset Markets at Wharton, the 2017 CEPR European Summer Symposium in Financial Markets—Gerzensee, the 2017 WFA Annual Meeting, the 2017 Barcelona GSE Summer Forum, the 2017 Finance Down Under Conference at the University of Melbourne, the 2017 EFA Annual Meeting, the 2017 ITAM Finance Conference, the 2017 FSU SunTrust Beach Conference, the 2017 FMA Asia/Pacific Annual Meeting, the 2017 China International Conference in Finance, the 2016 HEC-McGill Winter Finance Workshop, Georgetown University, the R. H. Smith School of Business at the University of Maryland, the University of Melbourne, and the Johns Hopkins Carey Business School. We are grateful to Sumit Agarwal, Santosh Anagol, Daniel Andrei, James Ang, Ilona Babenko, Gurdip Bakshi, Turan Bali, Federico Bandi, Jonathan Berk, Brad Barber (the NBER discussant), Jules van Binsbergen, Peter Bossaerts, John Campbell, Maria Cecilia Bustamante, Yingmei Cheng, Carole Comerton-Forde, Max Croce, Henrik Cronqvist, Julien Cujean, Francesco D’Acunto, Zhi Da, Sandeep Dahiya, Alexander David, Douglas Diamond, Allan Eberhart, Joey Engelberg, Stephen Figlewski, Laurent Fresard, Nicola Fusari, Pengjie Gao (the FSU SunTrust Beach Conference discussant), Xiaohui Gao, Bruce Grundy, Jaehoon Hahn (the CICF discussant), Samuel Hartzmark, Michael Hasler, Xing Huang (the ITAM Finance Conference discussant), Irena Hutton, Ryan Israelsen (the WFA discussant), Pete Kyle, April Knill, Mattia Landoni (the CEPR discussant), Dmitry Livdan, Mark Loewenstein, Spencer Martin, Rich Mathews, Gonzalo Maturana, David McLean, Vladimir Mukharlyamov, Will Mullins, Carsten Murawski, Federico Nardari, Marina Niessner (the Conference on Financial Decisions and Asset Markets discussant), Greg Nini, Lubos Pastor, Elena Pikulina, Nagpurnanand Prabhala, Tarun Ramadorai, Rodney Ramcharan, Steven Riddiough, Nikolai Roussanov, Shrihari Santosh, Philipp Schnabl, Andrei Shleifer, Stephan Siegel, Kelly Shue, Eric So, David Solomon, Zhaogang Song, Juan Sotes-Paladino, Chester Spatt, Michael Ungeheuer (the EFA discussant), Stephen Utkus (the Conference on Financial Decisions and Asset Markets discussant), Laura Veldkamp, Katherine Waldock, Quan Wen, Joakim Westerholm, Russ Wermers, Fernando Zapatero, Yeqin Zeng (the FMA discussant) and Eric Zwick for comments and suggestions. Antonio Gargano acknowledges support from the Faculty Research Grant funded by the University of Melbourne. Supplementary data can be found on The Review of Financial Studies Web site. Appendix A. Data Construction In this appendix, we describe the auxiliary data sets we use in our study. As mentioned in Section 2.2, our data source gave us access to data structured as an SQL relational database. Besides the Web-activity table described at length in Section 2, this study uses four additional tables named Trades, Clients, Accounts, and Account Holdings, respectively. Finally, the study also uses information from a variety of standard data sources. Further details are provided below. Trades The Trades table includes the record of all the trades made by account holders over the period January 2010 through June 2014. This table contains information regarding 3,528,001 accounts. The Trades table has a total of 197,870,535 observations, and each observation contains the following information: Acct_id, a unique numeric account identifier; Client_id, a unique numeric client identifier; Ord_ts, the time stamp of the trade order; Exec_ts, the time stamp of the trade execution; Scrty_type_descr, denoting whether the security traded is a stock, a bond, an option, or a mutual fund; Cusip_id, the CUSIP code of the security traded; Action, denoting whether the action is a buy or a sell; Trd_prncpl_amt, the amount traded; Trd_qty, the number of stocks traded; Chnl, denoting whether the trade is web-based or phone based. In our sample, 99% of the trades are web-based, showing how much the broking business has changed compared with the Barber and Odean (2002) data. The Trades table also contains information on the type of account associated with a trade. This variable is named Acct_type and takes on 61 values such as 401K, 403B, IRA, and many others. In much of the analysis performed in the paper, we group individual brokerage accounts and Joint Tenants with Right of Survivorship (JTWROS) accounts as one broad category that comprises 57.6% of all accounts in our data. Clients The Clients table contains information on the characteristics of the clients. This file has 2,812,877 observations. Comparing the total number of accounts reported above with the total number of clients shows that many clients have more than one account. In fact, 83.9% of the clients have only one account, 2.5% of them have two accounts, and 3.5% of them have three or more accounts. Clients that have multiple accounts usually have an individual account and an IRA account. Out of the Clients table, we use the following variables: client_id, a unique numeric client identifier; and Client_age, the age of the account holder. Accounts The Accounts table includes data on the characteristics of the accounts. This file has 3,528,001 observations, and for each observation, we make use of the following variables: Acct_id, a unique numeric account identifier; Client_id, a unique numeric client identifier; Gender, the gender of the account holder; Stnd_pstl_cd, the zip code of the account holder; Acct_open_dt, the account opening date; and Acct_close_dt, the account closing date. Account Holdings The Account Holdings table includes quarterly holdings for every account in the data set. This file has 194,438,993 observations and the following variables: Acct_id, a unique numeric account identifier; mkt_close_dt, the date of the holdings snapshot; Cusip_id, the CUSIP code of the security held by the account holder; Scrty_type_descr, denoting whether the security held is a stock, a bond, an option, or a mutual fund; Qty, the quantity held; and Amt, the dollar value of the quantity held. The Account Holdings table also contains cash-holdings information. We construct account holdings at the daily frequency by merging the Account Holdings table with the Trades table. Additional Data Sources Stock market information such as prices, returns and trading volumes—among others—is obtained from CRSP, CRSP OTC, and CRSP Mutual Funds. Stocks’ accounting information is obtained from Compustat. Benchmark returns are obtained from the Fama-French website and by constructing DGTW returns at the daily frequency. News concerning stocks are obtained from Capital IQ. Analysts’ information is obtained from I/B/E/S. Finally, information regarding zip-codes’ latitude and longitude is obtained from Census. Construction of the Final Data Set The final data set is obtained in two steps. In the first, we merge the contents of the Web-activity, Trades, Clients, Accounts and Account Holdings tables using the acct_id identifier. In the second, we merge the resulting data set with the additional data sources using either the stocks’ Cusip_id or ticker as identifiers. Footnotes 1 As reported in Agnew, Balduzzi, and Sunden (2003), the distribution of allocations to stocks across 401K investors is strongly bimodal: 48% of the average annual equity allocations are zero, while 22% are 100%. 2 In compliance with the U.S. privacy law, no Personally Identifiable Information (PII) was provided by the brokerage house. For example, each account holder has been anonymized using a numeric account identifier. 3 The results for the total number of pages visited and the total number of logins are very similar. 4 The total number of news items pertaining to the stocks in the S&P 500 Index are obtained from Capital IQ, while we downloaded the S&P 500 trading volume from Yahoo! Finance. 5 These categories are the ones that the brokerage account website is divided into and allow us to categorize 99% of the URLs. 6 The sample is dictated by data availability, that is, the complete URLs that include ticker information are available from October 1, 2013, until the end of our sample. 7 Even though these accounts were randomly selected from the full set of accounts of the brokerage account house, we allay concerns regarding their representativeness by recomputing the statistics in Table 3 using the entire universe of accounts of the brokerage account house (approximately 3.5 million). The results, reported in Online Table 1, show that the biographic and portfolio characteristics statistics are very similar across the two samples. Because our brokerage house has millions of clients, our sample is likely to be representative of the population of U.S. stock investors. 8 All dollar figures have been rounded to the nearest thousand upon request of the data provider. 9 For ease of visualization, we winsorize the number of minutes at the 95th percentile, which results in the number of winsorized minutes to range from 0 to 150. The summary statistics reported in Tables 3 are computed using nonwinsorized data. 10 More precisely, |$Attention_i$| is computed as |$Attention_i=log(1+minutes_i)$|⁠. We use the same formula for all the logged attention measures in the paper. 11 We do not standardize the dummy variable regressors Male and Brokerage. 12 Because we are estimating a log-linear regression, the economic interpretation of each coefficient |$\beta_k$| is computed as |$e^{\beta_k}-1$|⁠, and is interpreted as the percentage change in the number of minutes spent on the brokerage account website when the |$k$|-th regressor increases by one standard deviation. Finally—because all regressors have been de-meaned— the number of minutes spent on the brokerage account website by non-male and non–brokerage account holders, that are average in terms of all the other conditioning variables, represent our “base-case investor.” The number of minutes the base-case investor spends on the brokerage account website can be computed as |$e^{\hat{\alpha}} \times N^{-1} \sum_{i=1}^N e^{\hat{\epsilon_{i}}}$|⁠, where |$\hat{\alpha}$| and |$\hat{\epsilon_{i}}$| are obtained from Equation (1); see Duan (1983). 13 Computing the squared Sharpe ratio over the previous 42 or 63 months leaves the results unchanged. 14 We use the standard formula for computing the distance in miles between account holder |$i$| and firm |$j$|⁠, as follows: |$dist (i, j) = arccos\Big(cos(lat_{i})cos(long_{i})cos(lat_{j}) cos(long_{j}) + cos(lat_{i})sin(long_{i})cos(lat_{j})sin(long_{j})+ sin(lat_{i}) sin(lat_{j})\Big) r,$| where |$lat$|/|$long$| are the latitudes/longitudes of location |$i$| and location |$j$| (expressed in radians), respectively, and |$r$| denotes the radius of the earth (approximately 3,963 miles). 15 We obtained the total number of news for each stock from Capital IQ. 16 The market value of assets is computed as the sum of the closing stock price (Compustat item: PRCCQ) multiplied by the common shares (Compustat item: CSHPRQ), debt in current liability (Compustat item: DLCQ), long-term debt (Compustat item: DLTTQ), and preferred stocks (Compustat item: PSTKQ), minus deferred taxes and investment tax credit (Compustat item: TXDITCQ). 17 Following Frank and Goyal (2003), we replace R&D with 0 if the entry related to research and development expense is missing. 18 This is mainly due to the fact that Turnover, Volume, Analyst, and News are correlated with each other. As expected, the exclusion of three out of four covariates dramatically boosts the economic and statistical significance of the fourth covariate. It is nonetheless interesting to note that News has a positive and statistically significant impact—even after controlling for the other three regressors. 19 The DGTW-adjusted portfolio return is computed as in Equation (1) of Wermers (2003) and in Equation (1) of Daniel et al. (1997). 20 Compared with the covariates in Table 4 of Section 4.1, we do not include the regressors related to risk-factor exposures, that is, the loadings on the market, small-minus-big, high-minus-low and momentum factors, because these exposures are directly controlled for by the DGTW procedure. 21 The annual income figures are taken from Khorunzhina (2013), and use 2007 PSID data. 22 While this average Sharpe ratio may seem rather large, note that—for the same period—the market Sharpe ratio (computed using returns and volatility on a NYSE/AMEX/NASDAQ value-weighted index) has been 2.62. 23 The DGTW-adjusted portfolio return is computed as in Equation (1) of Wermers (2003) and in Equation (1) of Daniel et al. (1997). The results are economically similar if we use simple returns or market-adjusted returns. 24 Online Table 16 shows that the results are similar, but less significant, when we measure attention using number of pages or logins instead of seconds. Online Table 17 shows that the results are economically and statistically stronger when we use market-adjusted, rather than DGTW-adjusted, abnormal returns. 25 All |$p$|-values in this section are computed using the bootstrap procedure suggested by Barber, Lyon, and Tsai (1999) with 10,000 bootstrap iterations. 26 These plots exclude the top five percentiles of log-attention, because the data is more sparse in that region of the support, and the nearest neighbor estimates more erratic. 27 The fact that both the coefficients on low- and high-specialization individuals are higher than the corresponding coefficient of 2.33 reported in Table 8 is an indication that investors that look at stock-specific information benefit more from paying attention compared with those that don’t. 28 The results are qualitatively the same when we use one-month to five-month return horizons. References Abel, A. B. , Eberly J. C. , and Panageas S . 2007 . Optimal inattention to the stock market. American Economic Review 92 : 244 – 49 . Google Scholar Crossref Search ADS Abel, A. B. , Eberly J. C. , and Panageas S . 2013 . Optimal inattention to the stock market with information costs and transactions costs. Econometrica 81 : 1455 – 81 . Google Scholar Crossref Search ADS Agnew, J. , Balduzzi P. , and Sunden A . 2003 . Portfolio choice and trading in a large 401(k) plan. American Economic Review 93 : 193 – 215 . Google Scholar Crossref Search ADS Alvarez, F. , Guiso L. , and Lippi F . 2012 . Durable consumption and asset management with transaction and observation costs. American Economic Review 102 : 2272 – 2300 . Google Scholar Crossref Search ADS Amaya, D. , Christoffersen P. , Jacobs K. , and Vasquez A . 2015 . Does realized skewness predict the cross-section of equity returns? Journal of Financial Economics 118 : 135 – 67 . Google Scholar Crossref Search ADS Andrei, D. , and Hasler M . 2015 . Investor attention and stock market volatility. Review of Financial Studies 28 : 34 – 72 . Google Scholar Crossref Search ADS Barber, B. M. , Lyon J. D. , and Tsai C.-L . 1999 . Holding size while improving power in tests of long-run abnormal stock returns. Journal of Finance 54 : 165 – 2020 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2000 . Trading is hazardous to your wealth: The common stock investment performance of individual investors. Journal of Finance 55 : 773 – 806 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2001 . Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics 116 : 261 – 92 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2002 . Online investors: Do the slow die first? Review of Financial Studies 15 : 455 – 87 . Google Scholar Crossref Search ADS Barber, B. M. , and Odean T . 2007 . All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies 21 : 785 – 818 . Google Scholar Crossref Search ADS Calvet, L. E. , Campbell J. Y. , and Sodini P . 2007 . Down or out: Assessing the welfare costs of household investment mistakes. Journal of Political Economy 115 : 707 – 47 . Google Scholar Crossref Search ADS Da, Z. , Engelberg J. , and Gao P . 2011 . In search of attention. Journal of Finance 66 : 1461 – 99 . Google Scholar Crossref Search ADS Daniel, K. , Grinblatt M. , Titman S. , and Wermers R . 1997 . Measuring mutual fund performance with characteristic-based benchmarks. Journal of Finance 52 : 1035 – 58 . Google Scholar Crossref Search ADS Driscoll, J. C. , and Kraay A. C . 1998 . Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics 80 : 549 – 60 . Google Scholar Crossref Search ADS Duan, N. 1983 . Smearing estimate: A nonparametric retransformation method. Journal of the American Statistical Association 78 : 605 – 10 . Google Scholar Crossref Search ADS Frank, M. Z. , and Goyal V. K . 2003 . Testing the pecking order theory of capital structure. Journal of Financial Economics 67 : 217 – 48 . Google Scholar Crossref Search ADS Frydman, C. , Hartzmark S. M. , and Solomon D. H . 2017 . Rolling mental accounts. Review of Financial Studies 31 : 362 – 97 . Google Scholar Crossref Search ADS Gabaix, X. , and Laibson D . 2002 . The 6d bias and the equity-premium puzzle. In NBER Macroeconomics Annual 2001, Volume 16 , 257 – 330 . Cambridge, MA : MIT Press . Gervais, S. , Kaniel R. , and Mingelgrin D. H . 2001 . The high-volume return premium. Journal of Finance 56 : 877 – 919 . Google Scholar Crossref Search ADS Grinblatt, M. , and Keloharju M . 2001 . How distance, language, and culture influence stockholdings and trades. Journal of Finance 56 : 1053 – 73 . Google Scholar Crossref Search ADS Grinblatt, M. , Keloharju M. , and Linnainmaa J . 2012 . IQ, trading behavior, and performance. Journal of Financial Economics 104 : 339 – 92 . Google Scholar Crossref Search ADS Guiso, L. , and Jappelli T . 2006 . Information acquisition and portfolio performance. Working Paper . Hartzmark, S. M. 2014 . The worst, the best, ignoring all the rest: The rank effect and trading behavior. Review of Financial Studies 28 : 1024 – 59 . Google Scholar Crossref Search ADS Huang, L. , and Liu H . 2007 . Rational inattention and portfolio selection. Journal of Finance 62 : 1999 – 2040 . Google Scholar Crossref Search ADS Huberman, G. 2001 . Familiarity breeds investment. Review of Financial Studies 14 : 659 – 80 . Google Scholar Crossref Search ADS Ivkovic, Z. , Sialm C. , and Weisbenner S . 2008 . Portfolio concentration and the performance of individual investors. Journal of Financial and Quantitative Analysis 43 : 613 – 56 . Google Scholar Crossref Search ADS Ivkovic, Z. , and Weisbenner S . 2005 . Local does as local is: Information content of the geography of individual investors’ common stock investments. Journal of Finance 60 : 267 – 306 . Google Scholar Crossref Search ADS Kacperczyk, M. , Van Nieuwerburgh S. , and Veldkamp L . 2016 . A rational theory of mutual funds’ attention allocation. Econometrica 84 : 571 – 626 . Google Scholar Crossref Search ADS Karlsson, N. , Loewenstein G. , and Seppi D . 2009 . The ostrich effect: Selective attention to information. Journal of Risk and Uncertainty 38 : 95 – 115 . Google Scholar Crossref Search ADS Khorunzhina, N. 2013 . Structural estimation of stock market participation costs. Journal of Economic Dynamics and Control 37 : 2928 – 42 . Google Scholar Crossref Search ADS Korniotis, G. M. , and Kumar A . 2011 . Do portfolio distortions reflect superior information or psychological biases? Review of Economics and Statistics 93 : 244 – 65 . Google Scholar Crossref Search ADS Korniotis, G. M. , and Kumar A . 2013 . Do portfolio distortions reflect superior information or psychological biases? Journal of Financial and Quantitative Analysis 48 : 1 – 45 . Google Scholar Crossref Search ADS Li, J. , and Yu J . 2012 . Investor attention, psychological anchors, and stock return predictability. Journal of Financial Economics 104 : 401 – 19 . Google Scholar Crossref Search ADS Madrian, B. C. , and Shea D. F . 2001 . The power of suggestion: Inertia in 401(k) participation and savings behavior. Quarterly Journal of Economics 116 : 1149 – 87 . Google Scholar Crossref Search ADS Massa, M. , and Simonov A . 2006 . Hedging, familiarity and portfolio choice. Review of Financial Studies 19 : 633 – 85 . Google Scholar Crossref Search ADS Moskowitz, T. J. , Ooi H. Y. , and Pedersen L . 2012 . Time series momentum. Journal of Financial Economics 228 – 50 . Nicolosi, G. , Peng L. , and Zhu N . 2009 . Do individual investors learn from their trading experience? Journal of Financial Markets 317 – 38 . Odean, T. 1998a . Are investors reluctant to realize their losses? Journal of Finance 53 : 1775 – 98 . Google Scholar Crossref Search ADS Odean, T. 1998b . Volume, volatility, price, and profit when all traders are above average. Journal of Finance 53 : 1887 – 934 . Google Scholar Crossref Search ADS Odean, T. 1999 . Do investors trade too much? American Economic Review 89 : 1279 – 98 . Google Scholar Crossref Search ADS Peng, L. , and Xiong W . 2006 . Investor attention, overconfidence and category learning. Journal of Financial Economics 80 : 563 – 602 . Google Scholar Crossref Search ADS Peress, J. 2004 . Wealth, information acquisition, and portfolio choice. Review of Financial Studies 17 : 879 – 914 . Google Scholar Crossref Search ADS Petersen, M. A. 2009 . Estimating standard errors in finance panel data sets: Comparing approaches. Review of financial studies 22 : 435 – 80 . Google Scholar Crossref Search ADS Seasholes, M. S. , and Wu G . 2007 . Predictable behavior, profits, and attention. Journal of Empirical Finance 14 : 590 – 610 . Google Scholar Crossref Search ADS Sialm, C. , Starks L. T. , and Zhang H . 2015 . Defined contribution pension plans: Sticky or discerning money? Journal of Finance 70 : 805 – 38 . Google Scholar Crossref Search ADS Sicherman, N. , Loewenstein G. , Seppi D. , and Utkus S . 2016 . Financial attention. Review of Financial Studies 29 : 863 – 97 . Google Scholar Crossref Search ADS Van Nieuwerburgh, S. , and Veldkamp L . 2009 . Information immobility and the home bias puzzle. Journal of Finance 64 : 1187 – 215 . Google Scholar Crossref Search ADS Van Nieuwerburgh, S. , and Veldkamp L . 2010 . Information acquisition and under-diversification. The Review of Economic Studies 77 : 779 – 805 . Google Scholar Crossref Search ADS Vlastakis, N. , and Markellos N. R . 2012 . Information demand and stock market volatility. Journal of Banking & Finance 36 : 1808 – 21 . Google Scholar Crossref Search ADS Von Gaudecker, H.-M. 2015 . How does household portfolio diversification vary with financial literacy and financial advice? Journal of Finance 70 : 489 – 507 . Google Scholar Crossref Search ADS Wermers, R. 2003 . Is money really smart? New evidence on the relation between mutual fund flows, manager behavior, and performance persistence. Working Paper . Yuan, Y. 2015 . Market-wide attention, trading, and stock returns. Journal of Financial Economics 548 – 64 . © The Author(s) 2018. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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The Review of Financial Studies – Oxford University Press

Published: Dec 1, 2018

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