# Rent Seeking by Low-Latency Traders: Evidence from Trading on Macroeconomic Announcements

Rent Seeking by Low-Latency Traders: Evidence from Trading on Macroeconomic Announcements Abstract Prices of the highly liquid S&P 500 exchange-traded fund (SPY) and the E-mini future (ES) respond to macroeconomic announcement surprises within five milliseconds, with trading intensity increasing over 100-fold following the news release. However, profits from trading quickly are relatively small, roughly $\$$19,000 (⁠ \$$50,000) per event for SPY (ES). Although the speed of information incorporation has increased in recent years, profits have not. Order flow has become less informative, consistent with prices responding directly to news rather than indirectly through trading. Our evidence indicates that low-latency liquidity demanders do not benefit materially from short-term monopolistic access to information. Received April 18, 2017; editorial decision November 4, 2017 by Editor Andrew Karolyi. 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. Financial information is increasingly being released to, interpreted by, and traded on by computers. Dramatic improvements in technology have allowed computer algorithms to dynamically monitor multiple trading venues and strategically execute orders. These algorithms emphasize speed, and as a result, trade latency has been reduced to milliseconds. The increasing prevalence of low-latency trading (LLT) has led to two main concerns: the welfare implications of investing huge sums to achieve sub-second speeds, and the broader issue of whether the presence of low-latency traders (LLTs) reduces trust in financial markets. Theory points toward mixed welfare implications for LLT. Jovanovic and Menkveld (2016) argue that LLTs face lower adverse selection costs through their ability to quickly change quotes, and as a result LLTs improve gains from trade through their greater willingness to provide liquidity to intertemporally separated buyers and sellers. On the other hand, Biais, Foucault, and Moinas (2015) and Budish, Cramton, and Shim (2015) point to the socially wasteful arms race between LLTs, as they expend greater resources to further reduce latency. Empirically, Brogaard, Hendershott, and Riordan (2014) find evidence that high-frequency traders (HFTs)1 facilitate price discovery by trading in the direction of permanent price changes and against transitory pricing errors. Carrion (2013) finds that individual stock prices incorporate information from order flow and market-wide returns more efficiently on days with high HFT participation. Conrad, Wahal, and Xiang (2015) find that LLT activity leads prices to more closely resemble a random walk, and Chaboud et al. (2014) find that LLT improves price efficiency through lower return autocorrelations and fewer arbitrage opportunities. Other research suggests that the activities of LLTs improve market quality through increased liquidity and lower short-term volatility (Hendershott, Jones, and Menkveld 2011; Chordia, Roll, and Subrahmanyam 2011; Hasbrouck and Saar 2013; Hendershott and Riordan 2013). LLTs have attracted the scrutiny of regulators due to concerns that their technological advantages create an uneven playing field among market participants (Baer and Patterson 2014). Some argue that LLTs’ ability to trade ahead of slower investors allows them to earn profits in excess of the risks involved. Biais, Foucault, and Moinas (2015) have argued that fast traders observe market information before slow traders, thus generating adverse selection and negative externalities. This short-lived monopoly access to information is deemed a market failure that allows LLTs to earn excessive rents.2 These developments have led to arguments in the popular press that markets are “rigged” in favor of high-speed traders (Lewis 2014), which erodes faith in financial markets and could raise firms’ cost of capital. We contribute to the LLT debate and assess whether LLTs possess short-term monopolistic access to information by quantifying the profits available from sub-second trading in response to the release of eighteen different macroeconomic (macro) news announcements. Macro news releases provide a clean experimental setting where the timing of the release is known in advance, information is distributed in machine-readable form, and announcement surprises are relatively easy to interpret. Trading profits therefore depend critically on speed, making this an ideal setting for studying LLT. We analyze quote and transaction data for the highly liquid S&P 500 exchange-traded fund or ETF (SPY) and the E-mini S&P 500 futures contract (ES). Trading intensity increases over 100-fold within five milliseconds following the release of macro news, and there is a significant shift in order imbalances in the direction of the announcement surprise (based on the Bloomberg consensus forecast). Prices react to announcement surprises within five milliseconds. This quick and efficient price response is consistent with the theoretical model of Foucault, Hombert, and Rosu (2016), who argue that LLT trades are more correlated with short-run price changes and that they account for a large fraction of the trading volume around news events. Although LLTs respond swiftly and convincingly to macro news releases, we find that profits from fast trading are surprisingly small relative to widely held conventional wisdom (e.g., Mullins et al. 2013). Specifically, trading in the direction of the announcement surprise results in average dollar profits (across market participants) of$\$$19,000 per event for SPY and roughly \$$50,000 per event for ES. This translates to roughly $\$$15 million in cumulative profits on average each year, which is trivial relative to about \$$4.7 trillion traded in SPY and$\$$35.8 trillion notional value traded in ES in 2012. The \$$15 million is also trivial compared with the cost of price discovery in U.S. markets, which at 0.67% of the market capitalization (French 2008) amounted to roughly $\$$100 billion in 2006. The average price response for our sample of macroeconomic news events is roughly seven basis points (bps), and bid-ask spreads are typically less than one basis point, which would imply larger profit opportunities than what is observed in the data. However, the evidence suggests that the posted quotes around news releases are not the stale, exploitable limit orders of slow investors but rather quickly changing quotes of the liquidity-supplying LLTs. In the first quarter of a second after a news release, we observe over 500 changes to the best bid or offer quote in SPY (across venues). This evidence is consistent with the increasing importance of LLTs as liquidity providers, as suggested by Menkveld (2013). We also exploit a natural experiment to test whether LLTs who receive early information are able to exploit slower traders to earn excess profits. In one controversial practice, Reuters sold access to the University of Michigan’s Consumer Sentiment Index to LLTs two seconds before wide release. Mullins et al. (2013) and Hu, Pan, and Wang (2017) suggest that market participants were not aware of the early release. The practice ended in July 2013 at the request of the New York Attorney General, and this exogenously driven change in early access provides us with a setting to examine the value of fast access to information. We find no evidence that purchasing the two-second early access to Consumer Sentiment data provides LLTs with incremental profits. While profits are lower after Reuters ended the practice, this appears to be part of a general downward trend in trading profits across all macro announcements. A difference-in-difference approach reveals no statistically or economically significant changes in profits between consumer sentiment and other macro announcements, consistent with a quick reaction among liquidity-supplying LLTs. Whether information is released exclusively to algorithmic traders or distributed more broadly, the marginal market participant in the first couple of seconds following the release of machine-readable news is very likely to be a computer. The practice of selling early access to macro news appears more consistent with a profit seeking behavior among information providers rather than the exploitation of slow traders. We next explore the evolution in profits over our sample period and find evidence consistent with faster quote updating by liquidity-supplying LLTs. Profits decrease with the intensity of quoting activity following the announcement, and the quotes-to-trades ratio has increased over time while the available depth and trade sizes have decreased. The speed of market reaction to macro announcements has also increased over the sample period. The informativeness of the post-announcement order flow has decreased over time, which points to an increasing ability of LLT quotes to respond directly to announcement surprises rather than responding indirectly through trading. These findings highlight the LLTs’ lower adverse selection costs when supplying liquidity due to their ability to quickly update quotes in light of new information, consistent with the model of Jovanovic and Menkveld (2016).3 Together, the findings point to faster trading by liquidity-demanding LLTs and yet even faster quote updating by liquidity-supplying LLTs. These findings have important implications for our understanding of LLT. In a clean setting where the importance of speed is paramount, the apparent lack of meaningful profits from trading quickly calls into question naïve descriptions of how speed advantages translate into excessive rents for LLTs. It is not clear that speed confers a meaningful advantage to LLTs in terms of monopolistic access to information. While much of the existing theoretical literature on LLTs relies on rent seeking by monopolistic LLTs who profit at the expense of the slower traders, our empirical results question the validity of speed-based monopolistic access to information. Future theory should not rely on speed alone as a source of monopoly power among LLTs. Our analysis can also inform discussions about regulating LLT. Baron et al. (2016) find that new HFT entrants have a propensity to underperform and exit, which points toward an uneven playing field even among LLTs and suggests that increased regulatory oversight may benefit financial markets. Brogaard and Garriott (2017), on the other hand, find evidence that new LLT entrants lead to crowding out, with reduced spreads and less informative incumbent order flow. Our evidence supports the view that low-latency trading is maturing, with no increased profits over time despite the accelerated market reaction to macro announcements. The absence of outsized profits does not necessarily imply the absence of a need to regulate. However, the idea that the existence of LLT profits indicate a need for regulation is implicit in concerns regarding market failure. In particular, the argument holds that if fast traders are so profitable, they must have short-term monopolistic access to information that allows them to extract rents from other market participants, and that this monopoly power should be regulated. Our findings suggest that media descriptions of LLT profits are overstated. While other microstructure issues, such as the potential for a flash crash, quote stuffing, spoofing, and hammering the close, may continue to warrant attention from regulators, our findings mitigate concerns that LLTs earn outsized profits through speed alone. 1. Data and Descriptive Statistics 1.1 Financial market data: S&P 500 ETF and E-mini futures We study the financial market response to macroeconomic announcements using two of the most liquid stock market instruments: the largest and most heavily traded S&P 500 ETF (SPY), and the S&P 500 E-mini futures (ES). Both instruments have been studied extensively in previous work (e.g., Hasbrouck 2003). For these securities we obtain quote and trade data from Tick Data (now OneMarketData) that is time-stamped to the millisecond. The data allows us to capture price movements and to assign the direction of trade at the millisecond level, which allows us to measure the profitability of trading on announcement surprises.4 Our sample covers 2008–14 for SPY and July 2011–December 2014 for ES. Although the SPY sample is longer, ETFs do not begin trading each day until 9:30 a.m. ES trades 24 hours (except for a break from 4:15 to 4:30 p.m. and from 5:15 to 6:00 p.m.; all times are Eastern Standard Time [EST]), and therefore the ES sample allows us to examine a number of important macroeconomic announcements that are released at 8:30 a.m. The notional traded value of ES is higher than the dollar trading volume in SPY.5 For example, in 2012 the average daily notional value traded was \$$ 142 billion for ES versus a trading volume of$\$$18.5 billion for SPY. Quoted spreads are smaller for SPY, between 0.5 and 1.0 basis points for SPY versus 1–2 basis points for ES, due to the smaller tick size (⁠ \$$0.25 for ES vs. $\$$0.01 for SPY). In our analysis, we explore the market response and profitability of trading in both securities. 1.2 Macroeconomic announcements We obtain information about macro announcements from Bloomberg, including the release date and time, reported value, the median consensus estimate, number of estimates, and the standard deviation across estimates. We consider the macroeconomic series studied in Balduzzi, Elton, and Green (2001) and/or Brogaard, Hendershott, and Riordan (2014), for which Bloomberg reports consensus estimates and the actual announced values. We also consider the University of Michigan Consumer Sentiment Index and the Chicago Purchasing Managers’ Index (PMI), which were released to certain subscribers prior to their wider release to the public. Table 1 presents descriptive information for the twenty-seven announcements considered in our study. All occur at a monthly frequency, with the exception of the University of Michigan Consumer Sentiment Index (biweekly release) and Initial Jobless Claims (weekly release). “Release time” is the most common release time (changes in release time are rare in our 2008–14 sample period).6 We report the earliest time of access for Consumer Sentiment and Chicago PMI. Each of the macroeconomic series we consider is well covered, with large numbers of analysts providing estimates for each release. The lowest average number of estimates is 20 for Personal Consumption, and the highest is 90 for Nonfarm Payrolls. The coverage suggests that these are highly watched, market-moving events. We also observe a reasonable number of positive and negative surprises during the sample period. Table 1 Macroeconomic announcements descriptive statistics Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠ \$$ billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠$\$$billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 The table presents descriptive statistics for the sample of announcements. Release time is the most common release EST time (to subscribers) during the sample period. The sample period covers 2008–14 for announcements released after 9:30 a.m. and July 2011 through December 2014 for announcements released before 9:30 a.m. Obs. is the number of announcement observations during the sample period. Announcement surprises are measured as the reported value less the median Bloomberg estimate. Surprise std. dev. denotes the standard deviation of announcement surprises, Num. of estimates is the mean number of estimates, and Positive (Negative) surprises is the fraction of announcements that are positive (negative). For announcements after (before) 9:30 a.m., we regress the change in SPY midquote (change in S&P 500 E-mini futures midquote) from 5 minutes before to 5 minutes after the announcement on the standardized announcement surprise. Surprise coefficient is the resulting coefficient, with *, **, and *** indicating significance at the 10%, 5%, and 1% levels. Table 1 Macroeconomic announcements descriptive statistics Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠ \$$ billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠ $\$$billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 The table presents descriptive statistics for the sample of announcements. Release time is the most common release EST time (to subscribers) during the sample period. The sample period covers 2008–14 for announcements released after 9:30 a.m. and July 2011 through December 2014 for announcements released before 9:30 a.m. Obs. is the number of announcement observations during the sample period. Announcement surprises are measured as the reported value less the median Bloomberg estimate. Surprise std. dev. denotes the standard deviation of announcement surprises, Num. of estimates is the mean number of estimates, and Positive (Negative) surprises is the fraction of announcements that are positive (negative). For announcements after (before) 9:30 a.m., we regress the change in SPY midquote (change in S&P 500 E-mini futures midquote) from 5 minutes before to 5 minutes after the announcement on the standardized announcement surprise. Surprise coefficient is the resulting coefficient, with *, **, and *** indicating significance at the 10%, 5%, and 1% levels. 1.3 Market-moving events The twenty-seven macroeconomic releases that we consider may not all affect financial markets in a significant way. We begin by objectively assessing which releases are potentially important to low-latency traders. Specifically, we follow Balduzzi, Elton, and Green (2001) and regress percentage midquote price changes, measured from five minutes before to five minutes after the release, on the standardized announcement surprises. Surprises are measured as the difference between the actual value of the release and its median estimate, standardized by its time-series standard deviation. For releases before (after) 9:30 a.m., we use price changes for ES (SPY). The coefficient on the standardized surprise is reported in the final column of Table 1. It represents the change in price associated with a one-standard-deviation increase in announcement surprise. The largest price impact is 30 basis points for a one-standard-deviation change in Nonfarm Payrolls. Eighteen different types of macroeconomic news have a statistically significant impact on stock prices at the 5% level. We restrict our attention to these eighteen releases for the rest of our analysis. The coefficients on CPI, CPI excluding Food and Energy, and Initial Jobless Claims are negative, as higher-than-expected inflation and unemployment had negative implications for the stock market during the sample period. For ease of interpretation, we multiply these surprises by –1 so that all positive surprises are associated with good news for the stock market. 2. Market Response to Macroeconomic News The pace of trading in financial markets has increased rapidly in recent years. In 2000, Busse and Green (2002) find that firm-specific information released during market hours is incorporated into prices within one minute. Speed of communication has since improved dramatically, leading to LLTs who strive to achieve low latency by investing in technology and co-locating their servers in the same data centers as stock exchanges. Hasbrouck and Saar (2013) note that the fastest traders have an effective latency of two to three milliseconds. Brogaard, Hendershott, and Riordan (2014) find that in 2008 and 2009, it took several seconds for macroeconomic news to be incorporated in stock prices. We conjecture that the greater availability of machine-readable news and the increased presence of LLTs in recent years has led to faster information assimilation.7 In this section, we explore the role of LLTs in the process by which macroeconomic news is incorporated into prices. 2.1 Speed of information incorporation Table 2 presents the cumulative midquote returns for SPY and ES in the sub-seconds around eighteen macroeconomic news releases. We calculate the midquote price for the SPY at the beginning of each time period (second or tenth of a second) using the average of the National Best Bid and Offer (NBBO).8 Cumulative midquote returns for each period are computed relative to the midquote that prevailed 20 seconds before the event. The returns for ES are calculated in a similar manner. Negative surprises are releases in which the actual was below the consensus median, (above the consensus for CPI, CPI excluding Food and Energy, and Jobless Claims). Following positive (negative) surprises, we expect the cumulative midquote returns to be positive (negative). In Table 2, we combine positive and negative surprises together and report the mean absolute cumulative returns. Panel A reports the price response of SPY to macro announcements released after 9:30 a.m., and panel B reports the results for ES for the full set of eighteen announcements. Table 2 Stock market price response to macroeconomic news Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** The table reports mean cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures around macroeconomic news announcements. Returns are reported in basis points, and time is labeled in seconds. Cumulative returns are measured relative to the prevailing midquote 20 seconds before the announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding Food and Energy, and Jobless Claims announcements). The returns for negative surprises are multiplied by –1 and averaged with positive surprises. Panel A reports the results for the S&P 500 ETF (SPY), and panel B reports the results for S&P 500 E-mini futures. The SPY sample period covers 2008–14, and the E-mini sample is from July 2011 through December 2014. Statistical significance at the 10%, 5%, and 1% levels are labeled with *, **, and ***. Table 2 Stock market price response to macroeconomic news Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** The table reports mean cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures around macroeconomic news announcements. Returns are reported in basis points, and time is labeled in seconds. Cumulative returns are measured relative to the prevailing midquote 20 seconds before the announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding Food and Energy, and Jobless Claims announcements). The returns for negative surprises are multiplied by –1 and averaged with positive surprises. Panel A reports the results for the S&P 500 ETF (SPY), and panel B reports the results for S&P 500 E-mini futures. The SPY sample period covers 2008–14, and the E-mini sample is from July 2011 through December 2014. Statistical significance at the 10%, 5%, and 1% levels are labeled with *, **, and ***. Prices respond significantly to announcement surprises within the first 100 milliseconds (ms) following the release, which points toward LLT. Kosinski (2008) surveys the literature on reaction time and notes that human reaction (single response to single stimulus) is on the order of 200 milliseconds. The evidence suggests that the marginal market participant at the release of macroeconomic news is a computer that interprets the announcement surprise and revises quotes or routes orders within a tenth of a second. The average price reaction over the first two seconds of 5.4 (4.3) basis points for SPY (ES) accounts for 78% (84%) of the 10-second price reaction. This fraction is considerably larger than the roughly 50% two-second price reaction documented in Brogaard, Hendershott, and Riordan (2014), which is consistent with broader adoption of machine-readable news after the end of their sample in 2009. The announcements of CPI, Factory Orders (in the case of ES), and Leading Index have significant surprise coefficients in Table 1, yet they do not exhibit a significant price reaction in the first 10 seconds after announcement, which suggests that these announcements were either not available in machine-readable format or were not deemed important by LLTs.9 In untabulated results, we find that dropping these events increases the average reaction in SPY and ES by roughly one basis point (the results are otherwise similar). The Consumer Sentiment announcement also merits special attention, as for most of the sample period, early access subscribers were able to obtain information in machine-readable form two seconds prior to wider release. Using the early access time (9:54:58) as the information release time during this period of the sample, we find SPY prices incorporate roughly 73% of the 10-second price response within a half-second, and ES prices react as quickly if not more so.10 Regardless of whether information is released exclusively to LLTs or more widely, LLTs are the primary agents for incorporating new (machine-readable) information into prices. Figure 1 disaggregates positive and negative announcement surprises and plots the average cumulative price response for the SPY (panels A and B) and ES (panels C and D) across announcements. The figures show that the speed of price reaction to negative surprises is similar to the price reaction to positive surprises. Consistent with Table 2, panels A and C reveal that most of the price reaction happens within the first couple of seconds. Panels B and D focus on the two-second subperiod and more finely partition price changes into 100-millisecond intervals. A large portion of the price reaction occurs within the first second. Figure 1 View largeDownload slide Stock market price response to macroeconomic news releases The figure plots the average cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures (futures) around macro news releases. In panels A and C, returns are measured each second relative to midquote 20 seconds before the event. In panels B and D, returns are measured every 100 milliseconds relative to 20 seconds before the event. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. The numbers in the horizontal axis represent the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). Figure 1 View largeDownload slide Stock market price response to macroeconomic news releases The figure plots the average cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures (futures) around macro news releases. In panels A and C, returns are measured each second relative to midquote 20 seconds before the event. In panels B and D, returns are measured every 100 milliseconds relative to 20 seconds before the event. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. The numbers in the horizontal axis represent the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). To statistically test for the speed of price response, we calculate price changes relative to the midquote measured 20 seconds after the announcement. In this setting, price changes should generally be statistically significant when measured before the event and gradually become insignificant as information is incorporated into prices. The resulting |t|-statistics are presented in Figure 2. For SPY, negative news is priced in within four seconds, and positive surprises are incorporated within five seconds. For ES, the analogous numbers are five seconds and two seconds. Taken together, the evidence suggests that machine-readable news and high-speed algorithms have diminished the role of humans while greatly increasing the speed with which prices incorporate new information. Figure 2 View largeDownload slide Speed of stock market price response to macroeconomic news The figure plots the t-statistics of midquote returns for the S&P 500 ETF (SPY) and the S&P 500 E-mini futures (futures) around macro news. Returns are measured each second relative to midquote 20 seconds after the event. The numbers in the horizontal axis is the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. Figure 2 View largeDownload slide Speed of stock market price response to macroeconomic news The figure plots the t-statistics of midquote returns for the S&P 500 ETF (SPY) and the S&P 500 E-mini futures (futures) around macro news. Returns are measured each second relative to midquote 20 seconds after the event. The numbers in the horizontal axis is the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. 2.2 Trading and quoting activity This section analyzes trading and quoting activity around macroeconomic announcements. In particular, we examine the total dollar volume of trades per second (notional value for futures), number of trades per second, number of quote changes per second, and order imbalances in SPY and ES. We use the period five minutes to five seconds before the release time as a benchmark. We report volume, number of trades, and number of quote changes per second to facilitate comparisons across intervals. Table 3 reports the results. The index instruments are highly liquid. In the benchmark period, there are more than 30 trades per second and 350 quote changes in SPY (across all market venues), accompanied by dollar volume of roughly \$$ 2 million per second. We find no changes in trading or quoting activity in the five seconds prior to the release. In the first five milliseconds after the announcement, trading (quoting) activity increases more than 100-fold (15-fold) to more than 6,000 quotes and trades per second, with trading volume jumping to$\$$614 million per second. ES experiences an even larger jump in notional volume, rising from \$$3 million during the benchmark period to about $\$$1,200 million per second in the first five milliseconds after the release. Trading and quoting activity in both instruments remain significantly elevated for up to three seconds after the announcement. Table 3 Stock market activity around macroeconomic news releases Time Dollar volume \$$ millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |$-$|5m to |$-$|5s 2 33 350 0.00 |$-$|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 Time Dollar volume$\$$millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |-|5m to |-|5s 2 33 350 0.00 |-|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 The table reports measures of trading activity around macro news releases. Panel A reports activity for the S&P 500 ETF (SPY), and panel B reports activity for the S&P 500 E-mini futures. Average dollar trading volume and notional volume (# of contracts |\times|Price|\times| 50) are reported in \$$ millions for each reported interval. Also reported are the number of trades per second, the number of quote changes per second, and the average order imbalance during each measured interval. Order imbalance (OI) is computed as the (# of buys – # of sells) / (# of buys |$+$| # of sells), where buys (sells) represents buyer- (seller-)initiated trades. For negative surprises, the negative of OI is used to compute the average across events. The interval –5m to –5 captures activity from 5 minutes to 5 seconds before the announcement. The other rows report the activity in the period reported in that row relative to the announcement. Statistical significance for a difference in means compared with a benchmark period measured –5 minutes to –5 seconds before the event, is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. Table 3 Stock market activity around macroeconomic news releases Time Dollar volume $\$$millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |-|5m to |-|5s 2 33 350 0.00 |-|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 Time Dollar volume \$$ millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |$-$|5m to |$-$|5s 2 33 350 0.00 |$-$|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 The table reports measures of trading activity around macro news releases. Panel A reports activity for the S&P 500 ETF (SPY), and panel B reports activity for the S&P 500 E-mini futures. Average dollar trading volume and notional volume (# of contracts |$\times$|Price|$\times$| 50) are reported in$\$$millions for each reported interval. Also reported are the number of trades per second, the number of quote changes per second, and the average order imbalance during each measured interval. Order imbalance (OI) is computed as the (# of buys – # of sells) / (# of buys |+| # of sells), where buys (sells) represents buyer- (seller-)initiated trades. For negative surprises, the negative of OI is used to compute the average across events. The interval –5m to –5 captures activity from 5 minutes to 5 seconds before the announcement. The other rows report the activity in the period reported in that row relative to the announcement. Statistical significance for a difference in means compared with a benchmark period measured –5 minutes to –5 seconds before the event, is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. We examine whether trading activity is oriented in the direction of announcement surprises by analyzing order imbalances. We assign transactions using the Lee and Ready (1991) algorithm. In particular, trades that are executed at a price higher (lower) than the prevailing midquote are treated as buys (sells). If a trade occurs at the midquote, then we compare the traded price to the previous traded price, and upticks (downticks) are classified as buys (sells). We then calculate order imbalance as (number of buys – number of sells) / (number of buys |+| number of sells). We expect positive order imbalance for positive surprises and the opposite for negative surprises. The last column of Table 3 reports mean order imbalances aggregated across positive and negative surprises, where we multiply negative surprise order imbalances by –1. The evidence is consistent with traders reacting to announcement surprises. In SPY (ES), order imbalance is 0 (0) during the benchmark period and 0.42 (0.18) and highly significant in the first five milliseconds after the news release. Order imbalance remains statistically significant for three seconds but falls considerably and loses significance afterwards. The relation between announcement surprise and order imbalance is similar when using the dollar value of purchases and sales (reported in the Online Appendix, Table IA.1). The evidence suggests that markets quickly incorporate new macroeconomic information, and part of the information is revealed through trading in the direction of the surprise. Our approach implicitly assumes that the trading immediately following macroeconomic announcements reflects the actions of LLTs. We explore the validity of this assumption by examining trading activity in the Nasdaq HFT database, which covers HFT trades in 120 stocks during the sample period 2008–09 (for more details, see Brogaard, Hendershott, and Riordan 2014). Using this data set, we examine transactions around macro announcements that represent HFT liquidity demand. The results are provided in the Online Appendix, Table IA.2. We find that in the first two seconds, HFT liquidity demand in the direction of surprise increases 10-fold, and 86% of HFT liquidity demand is in the direction of surprise in the first half-second of the release. The proportion of trades in the direction of the surprise decreases over time following the release, consistent with HFTs that traded on the news unwinding their positions. The evidence supports the view that macro announcements attract trading by HFTs and other LLTs. 3. Profitability of LLTs on Macroeconomic News The evidence in the previous section suggests that LLTs enhance market efficiency by swiftly and accurately responding to new information. This view is generally consistent with recent research on the effects of LLTs on financial markets (e.g., Brogaard, Hendershott, and Riordan 2014; Carrion 2013; Chaboud et al. 2014). However, the concern of regulators and other market watchdogs is that the contributions of LLTs to market efficiency come at the expense of reduced trust in financial markets. Conventional wisdom holds that LLTs’ speed advantage allows them to exploit slower market participants and earn profits that are disproportionate to the risks involved. For example, Hirschey (2016) finds that HFTs’ aggressive purchases and sales lead those of other investors, and Baron et al. (2016) find that aggressive (liquidity-taking) high-frequency trading is highly profitable on a risk-adjusted basis. In this section, we explore whether low latency translates into outsized profits for LLTs following macroeconomic announcements. In computing profits, we assume that all trades in the direction of the announcement surprise and executed within two seconds of the release are initiated by liquidity-demanding LLTs. We choose a two-second window based on the idea that human traders are unlikely to be able to respond to information within two seconds, and we note that Reuters also chose a two-second window for its early access arrangement for Consumer Sentiment information. The precise timing of the information release is also important for determining profits, and we include trades that occur up to 0.5 second before the official release time to allow for imprecision in the measurement of the release times.11 We calculate the volume-weighted average transaction price during the entry period—that is, purchases following positive surprises and sales following negative surprises, and compare it to the offsetting volume-weighted average transaction prices measured during three post-announcement exit periods: two to five seconds, five seconds to one minute, and one to five minutes after the announcement.12 We measure profits in short time intervals to focus on fast trading. We stop at five minutes after announcements to avoid the impact of other confounding information. Finally, we calculate aggregate dollar profits by multiplying the total dollar volume of trades in the direction of surprise during the entry period by the percentage price change. Table 4 reports the average profits. For SPY, the average total dollar profits across events when exiting two to five seconds after the event (at the volume-weighted offsetting price) are below \$$ 7,000. Using a one- to five-minute exit window increases aggregate profits to $\$$19,000, suggesting some price drift after the first five seconds. The profits from trading on Consumer Sentiment surprises do not exceed \$$6,000 (⁠$\$$8,000 in the case of ES) per event on average for any exit window despite being provided early to subscribing LLTs during most of the sample period. Profits are \$$83,000 for ISM Manufacturing, however, suggesting quick reaction to this information was more profitable. Table 4 Profitability of low-latency trading on macroeconomic news releases S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI – $\$$616 \$$2,709$\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233*** $\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI –$\$$616 \$$2,709 $\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233***$\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports profits for announcements that occur outside of regular trading hours (9:30 a.m. to 4:00 p.m. EST), and panel B reports profits for announcements that occur during regular trading hours. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to 2 seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s–1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Statistical significance is denoted by *, **, *** for significance at the 10%, 5%, and 1% levels. Table 4 Profitability of low-latency trading on macroeconomic news releases S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI – $\$$616 \$$2,709$\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233*** $\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI –$\$$616 \$$2,709 $\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233***$\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports profits for announcements that occur outside of regular trading hours (9:30 a.m. to 4:00 p.m. EST), and panel B reports profits for announcements that occur during regular trading hours. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to 2 seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s–1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Statistical significance is denoted by *, **, *** for significance at the 10%, 5%, and 1% levels. Notional values are considerably higher for ES, which leads to dollar profits that are an order of magnitude higher. For example, with a one- to five-minute exit window, average profits from trading on announcement surprises for Nonfarm Payrolls, Chicago PMI, Existing Home Sales, and ISM Manufacturing all exceed $\$$100,000. The drift in midquotes we see in Panel B of Table 2 for Nonfarm Payrolls and ISM Manufacturing after the first two seconds contributes to the profits for these announcements. For ES, we report profits during and outside of regular trading hours (RTH). Profits from trading outside of RTH are about half those from trading during RTH, possibly because there is more liquidity during RTH. Across all events, the average profits in the futures contract are roughly \$$50,000 per event.13 Figure 3 plots the percentage change in volume-weighted transaction prices surrounding the releases to provide a sense of scale for the dollar profits. We also partition the two-second entry window into smaller increments. We observe returns of about six basis points for SPY if positions are entered within the first tenth of a second and unwound one to five minutes after the announcement. However, these high returns translate to relatively low aggregate dollar profits due to the limited trading in the first tenth of a second. Wider spreads for the futures contract lead to lower returns, just over two basis points, but dollar profits are higher due to larger notional values traded. A half-second delay greatly reduces returns.14 Figure 3 View largeDownload slide Profitability of low-latency trading on macroeconomic news releases The figure shows average percentage profits (in basis points) from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements and unwound later at the volume-weighted average (offsetting) transaction price. The plot shows profits for various entry and exit periods. For example, the entry interval labeled 0.1s refers to the period 0.5 second before to 0.1 second after the event, and the exit period labeled 5m refers to the period 1 to 5 minutes after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Figure 3 View largeDownload slide Profitability of low-latency trading on macroeconomic news releases The figure shows average percentage profits (in basis points) from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements and unwound later at the volume-weighted average (offsetting) transaction price. The plot shows profits for various entry and exit periods. For example, the entry interval labeled 0.1s refers to the period 0.5 second before to 0.1 second after the event, and the exit period labeled 5m refers to the period 1 to 5 minutes after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Our findings are in stark contrast with descriptions of highly profitable “event-jumping” algorithmic trading in the media. For example, Mullins et al. (2013) highlight the March 15, 2013, release of Consumer Sentiment that led SPY prices to fall by$\$$0.27 over five minutes, with 310,000 shares traded in the first second (of which they suggest two-thirds were sales). Their numbers suggest a profit of (2/3 |\times| 310,000 |\times| 0.27) |=| \$$55,800, which is larger but on the same order of magnitude as the $\$$31,578 profit we obtain using volume-weighted average transaction prices for a –0.5- to 2-second entry window and a one- to five-minute exit window. Both numbers are several multiples of the \$$5,200 that we calculate on average for Consumer Sentiment announcements in Table 4. Similarly, the March 15, 2013, Consumer Sentiment aggregate profit we measure when trading in ES is$\$$352,643, which is many times larger than the average Consumer Sentiment profit in ES of \$$7,699. Thus, the examples mentioned in media articles seem to be outliers. An important caveat here is that we do not know the exact trading strategy of the LLTs. It may be that they are able to optimize their trades along some dimension, so as to earn higher profits than those we compute. On the other hand, our analysis focuses only on announcement types that have a significant impact on returns. We also present estimates of potential rather than actual profits, since some liquidity-demanding HFTs may lose money if they initially misinterpret the signal and trade in the wrong direction. In a sense, we provide an upper bound for profits to make them potentially comparable to the claims in the popular press. We explore upper-bound profits more thoroughly in the Online Appendix.15 First, we eliminate unprofitable trades as follows: for positive surprises, we match the lowest buy price in the entry window with the highest sell price in the exit window. We then match the next lowest buy with the next highest sell, and so on, until there are no more profitable trades (and use the opposite process for negative surprises). Compared with Table 4, the resulting profits reported in the Online Appendix, Table IA.8, are 2.5 (3.1) times higher for SPY (ES). In the Online Appendix, Table IA.9, we repeat this process, but determine trade direction optimally using the maximum profit available (rather than using announcement surprises). This perfect-foresight, no-loss approach results in profits that are 3.0 (3.8) times bigger for SPY (ES), which is still relatively small from an economic perspective. In the Online Appendix, Table IA.9, placebo tests that examine LLT profits at times other than the earnings announcement support the argument that profits exist solely following the announcement. For instance, the full foresight profits 15 fifteen minutes prior to the announcement amount to $\$$4,048 (⁠ \$$21,244) for the SPY (ES), compared with$\$$55,505 (⁠ \$$187,402) at the time of the announcement. Another potential concern is that we only consider two instruments, whereas algorithmic traders could conceivably submit orders in hundreds if not thousands of securities. We chose our instruments based on their high liquidity, where small price changes may potentially be profitable due to low quoted spreads and high depth.16 As a robustness check, we examine two additional ETFs, the Nasdaq index (QQQ) and the Russell 2000 index (IWM). We also consider the 30 stocks comprising the Dow Jones Industrial Average (DJIA), which are among the most liquid individual stocks. The results are presented in Table 5. Table 5 Profitability of low-latency trading on macroeconomic news releases: Other instruments Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports the profits in S&P 500 ETF (SPY), panel B reports the profits in S&P 500 E-mini futures (ES), panel C reports the total profits in Nasdaq 100 ETF (QQQ), panel D reports the profits in Russel 2000 ETF (IWM), and panel E reports the profits in Dow Components. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price announcements measured during the half-second before the announcement to the corresponding time in each column after the announcement. For example, 5ms indicates initiating the position during the half-second before the announcement to 5 milliseconds after the announcement. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For ETFs and stocks, the sample period covers 2008–14, and for ES the sample period covers July 2011–December 2014. Table 5 Profitability of low-latency trading on macroeconomic news releases: Other instruments Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports the profits in S&P 500 ETF (SPY), panel B reports the profits in S&P 500 E-mini futures (ES), panel C reports the total profits in Nasdaq 100 ETF (QQQ), panel D reports the profits in Russel 2000 ETF (IWM), and panel E reports the profits in Dow Components. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price announcements measured during the half-second before the announcement to the corresponding time in each column after the announcement. For example, 5ms indicates initiating the position during the half-second before the announcement to 5 milliseconds after the announcement. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For ETFs and stocks, the sample period covers 2008–14, and for ES the sample period covers July 2011–December 2014. We finely partition the two-second entry window to isolate the profits available to LLTs with the lowest latency. Profits are significantly different from zero even with an entry window of five milliseconds after the announcement. All profit estimates in Table 5 are statistically significant from zero at the 1% level, and profits are highest in each instrument using a two-second entry window and one- to five-minute exit window. With these entry and exit windows, the profits from trading fast on QQQ amount to $\$$3,927; for IWM the profits amount to \$$4,151; and for the 30 stocks that compose the DJIA, the profits amount to$\$$7,973. While the average profits from trading in SPY and ES amount to about \$$69,000 per event, the profits from trading in QQQ, IWM, and the 30 component DJIA stocks amount to about $\$$16,000. Thus, compared with SPY and ES, dollar profits are considerably lower in the QQQ and the IWM as well as among the 30 DJIA stocks. While macro news may occasionally be significant enough to permit profits in less liquid securities, our evidence suggests these events are most common in the SPY and the ES. Overall, we find that stock index prices react near instantaneously to macro announcement surprises, yet profits to LLTs are surprisingly small. 4. Trend in Profits and Price Discovery We focus on profits available to liquidity demanders who trade on announcement surprises, which suggests that they profit at the expense of slower and therefore less informed liquidity suppliers. Although speed gives LLTs a potential informational advantage following macroeconomic news releases, an increasing fraction of liquidity is also being provided by fast traders who can post quotes confidently knowing they can update them quickly in light of new information. For example, Table 3 shows that both the number of trades and quotes increase dramatically immediately after the announcement. In this section, we explore the evolution of price discovery and trading profits throughout our sample period. 4.1 Trend in profits Anecdotal evidence suggests that liquidity providers may subscribe to real-time news “to keep from getting ‘flattened’” by other traders (Mullins et al. 2013). We conjecture that liquidity suppliers become increasingly adept at responding to information over time, either by subscribing to the machine-readable news themselves or by improving their ability to react to liquidity demanders. Figure 4 presents profits by year from trading in the first two seconds following macroeconomic surprises.17 For SPY, profits display a hump shape. Profits generally grow from 2008 to 2011, which is consistent with increased availability of machine-readable news, generally increasing market liquidity, and a greater presence of LLTs (e.g., Beschwitz, Keim, and Massa 2015). However, profits peak in 2011 and fall steadily in 2012, 2013, and 2014. Although the sample is shorter for ES, the decline since 2011 is also evident, with average profits from trading on macroeconomic news in 2014 being just \$$ 9,000 for ES. The lack of an increase in profits over time, despite increases in speed among liquidity-demanding LLTs, is consistent with an increasing ability of liquidity providers to react quickly to new public information. Figure 4 View largeDownload slide Trend in profitability of low-latency trading on macroeconomic news The figure plots the average per-event dollar profits from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to two seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Figure 4 View largeDownload slide Trend in profitability of low-latency trading on macroeconomic news The figure plots the average per-event dollar profits from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to two seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. As a robustness check, we also repeat the analysis by excluding certain events that do not move the prices by more than 3 bps in the sample period (Factory Orders and Leading Index for SPY and CPI, CPI excluding Food and Energy, Consumption, Capacity Utilization, Industrial Production, Factory Orders, and Leading Index for Futures). Table IA.3 in the Online Appendix presents the results. The pattern is similar, with profits peaking in 2011 and declining thereafter. We repeat the analysis after filtering out events that are contemporaneous with other announcements (which could potentially lead to conflicting trading signals), and again the results are similar in Table IA.3 in the Online Appendix. The decline in profits is also consistent with a reduction in the importance of announcement surprises since the price reaction to macro news depends on the surprise component. We therefore study the trend in profits after controlling for the impact of announcement surprises. Specifically, we follow the methodology in McQueen and Roley (1993) and allow price reactions to announcement surprises to vary with the business cycle. In particular, we measure the time trend in monthly industrial production (log seasonally adjusted) and compute upper and lower trend values using the 25th and 75th percentiles. The dummy High State (Low State) is equal to 1 if industrial production for the month is above (below) the upper (lower) bound, and 0 otherwise (where the dummy Medium State takes a value of 1). We multiply the stage of business cycle dummies with the absolute value of the announcement surprise and include them in the regression.|$^{\mathrm{\thinspace }}$| Table 6 presents results from panel regressions of dollar profits per event (panel A), percentage of profit per event (panel B), and the dollar trading volume per event (panel C) on year dummies and the absolute value of surprises during different stages of the business cycle. All the dependent variables are measured using entry windows from 500 milliseconds before the announcement to two seconds after. We include announcement fixed effects to control for differences in average profitability across announcements (Table 4). It is possible that market-wide news shocks (such as the start or cessation of quantitative easing by the Federal Reserve) could affect the information content and LLT trading profits across all the macro announcements, thus leading to cross-sectional correlation in residuals around the news event. We therefore base our inferences on standard errors clustered by month. We suppress the dummy for the year 2011.18 Table 6 Trading profits by year after controlling for variation in announcement surprises Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 The table presents the coefficient estimates from regressing trading profits and volume on surprises and year dummies. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position five seconds to 1 minute after the event. All strategies use an entry window of 0.5 second before to 2 seconds after announcements. Panel A reports the results for profits per event in dollars, panel B reports the results for percentage profits per event, and panel C reports the results for trading volume per event in dollars. Trading volume in futures is measured as # of contracts |$\times$| Price |$\times$| 50. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Events are allowed to have different responses to surprises at different stages of the business cycle (Low, Medium, and High). Dummy for year 2011 is suppressed. Event fixed effects are included in the regression, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. Table 6 Trading profits by year after controlling for variation in announcement surprises Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 The table presents the coefficient estimates from regressing trading profits and volume on surprises and year dummies. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position five seconds to 1 minute after the event. All strategies use an entry window of 0.5 second before to 2 seconds after announcements. Panel A reports the results for profits per event in dollars, panel B reports the results for percentage profits per event, and panel C reports the results for trading volume per event in dollars. Trading volume in futures is measured as # of contracts |$\times$| Price |$\times$| 50. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Events are allowed to have different responses to surprises at different stages of the business cycle (Low, Medium, and High). Dummy for year 2011 is suppressed. Event fixed effects are included in the regression, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The evidence in Table 6 indicates that profits do increase with the magnitude of the announcement surprise. For example, when unwinding the position one to five minutes after the announcement, a one-standard-deviation increase in surprise (during the Medium state) leads to about$\$$40,000 in higher SPY profits and \$$125,000 in higher ES profits. There is also evidence that the effect of surprises varies with the state of the economy. More importantly, we find no evidence that profits increase over time, and we find some evidence that profits and trading volume decrease after 2011. For example, in the case of SPY with an exit time of 2 to 5 seconds, the coefficients on the year dummies point to a decline in profits of $\$$12,252 in 2013 and \$$9,908 in 2014.19 The dollar trading volume in SPY (ES) also significantly declines by roughly$\$$27 (⁠ \$$33) million in 2013 and $\$$31 (⁠ \$$45) million in 2014 after we control for announcement surprises. The evidence in Table 6 helps mitigate the concern that a decline in surprises over time explains the decline in profits and trading volume.20 The decline in trading volume suggests that over time, lower amounts of trading is required for prices to react to macro announcements. This could occur if over time the liquidity-supplying LLTs change their quotes more in response to the announcements and less in response to order flow. We address this in the next section. 4.2 Effect of quote intensity on profits If observed profits are low due to the presence of quickly reacting liquidity providers, we would expect to see a relation between profits and quote intensity. Specifically, if quotes are slow to update and become stale in light of new information, we would expect greater profit opportunities. On the other hand, rapid quote changes alone could be sufficient to incorporate new information with trading being less profitable. We explore this relation formally by regressing profits on measures of quote intensity. Quoting and trading are positively correlated, and both generally signal a liquid market that could improve profits. By scaling quote intensity by trading intensity, we focus on the relative ability of liquidity providers to react to information. Our variable of interest is the ratio of quotes to trades (QT ratio), measured during the two-second entry window. We also include the ratio of quotes to trades measured during a benchmark period five minutes to five seconds before the event to control for possible time of day effects or longer-term trends. All variables are standardized to facilitate interpretation. Table 7 follows the methodology in Table 6 and allows the effect of announcement surprises to vary with levels of the business cycle. Panel B allows the effect of macro surprises on market conditions to vary with market uncertainty. Specifically, we include the level of the VIX (the market’s expectation of 30-day market volatility implied from S&P 500 index options) as well as an interaction term between announcement surprises and VIX. Both announcement and time fixed effects are included. Standard errors are clustered by month. Table 7 Trading profits around macroeconomic news and measures of quote intensity S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 The table presents the coefficient estimates from regressing trading profits on quote and trading activity around macroeconomic news announcements. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. Trades and quotes are computed from 5 minutes to 5 seconds before the announcement (denoted by Pre-Ann.) and from 0 to 2 seconds after announcements (denoted by Post-Ann). The quote/trade ratio is the number of quote changes over the number of trades. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. All strategies use an entry window of 0.5 seconds before to 2 seconds after announcements. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Panel A allows for events to have different responses to surprises at different stages of business cycle (Low, Medium, and High) and panel B allows for different levels of the VIX. Event and year fixed effects are included in the regression and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. Table 7 Trading profits around macroeconomic news and measures of quote intensity S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 The table presents the coefficient estimates from regressing trading profits on quote and trading activity around macroeconomic news announcements. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. Trades and quotes are computed from 5 minutes to 5 seconds before the announcement (denoted by Pre-Ann.) and from 0 to 2 seconds after announcements (denoted by Post-Ann). The quote/trade ratio is the number of quote changes over the number of trades. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. All strategies use an entry window of 0.5 seconds before to 2 seconds after announcements. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Panel A allows for events to have different responses to surprises at different stages of business cycle (Low, Medium, and High) and panel B allows for different levels of the VIX. Event and year fixed effects are included in the regression and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The evidence in Table 7 indicates that high post-announcement QT ratios lead uniformly to lower profits. This is consistent with a more efficient response by liquidity providers who quickly move quotes toward the equilibrium price. The relation is significant for both SPY and ES. For ES in particular, a one-standard-deviation increase in the QT ratio reduces profits by more than half of the average profits in Table 4 for the three different exit strategies. The results in panel B are similar when the impact of the surprise is allowed to vary with the level of the VIX. Profits decrease with the post-announcement QT ratio, but not with the pre-announcement ratio. The findings suggest that active liquidity providers respond quickly to new information, which reduces profit opportunities for liquidity-demanding LLTs. The evidence is consistent with Brogaard, Hagstromer, et al. (2015), who argue that increasing the speed of market-making increases market liquidity through reduced adverse selection. Figure 5 provides further evidence of faster reaction among liquidity providers in the two-second period after the announcements. The figure plots quoted depth, average trade size, and the QT ratio by year. Depth is measured following each quote change during the two-second period after the announcement as the average of shares (for SPY) or the number of contracts (for ES) offered for trade at the best bid and offer prices. Trade size is the average trade size in shares (number of contracts) for the SPY (ES) traded during the two-second period after the announcement. The measures are first computed for each event, then averaged for each announcement type (e.g., Nonfarm Payroll or Consumer Sentiment) each year and finally averaged across events each year. Consistent with reduced latency among liquidity providers, Figure 5 shows that the QT ratio has generally increased over time, while quoted depths and trade sizes have declined.21 Figure 5 View largeDownload slide Trend in quotes-to-trades ratio, quote depth, and trade size The figure plots the trend in average quotes-to-trades ratio, quoted depth, and trade size around each macroeconomic announcement. QT ratio is the ratio of number of quotes to number of trades in a given period. Quoted depth is the average of number of shares at best bid price and number of shares at best ask price. Trade size is the average volume per trade (for futures, it is the number of contracts per trade). Reported are the average values for the period beginning with the announcement and ending 2 seconds later. The numbers are averages across events for the year. Figure 5 View largeDownload slide Trend in quotes-to-trades ratio, quote depth, and trade size The figure plots the trend in average quotes-to-trades ratio, quoted depth, and trade size around each macroeconomic announcement. QT ratio is the ratio of number of quotes to number of trades in a given period. Quoted depth is the average of number of shares at best bid price and number of shares at best ask price. Trade size is the average volume per trade (for futures, it is the number of contracts per trade). Reported are the average values for the period beginning with the announcement and ending 2 seconds later. The numbers are averages across events for the year. Figure 6 plots the trend over time in the speed of market response to macro news. Our first measure of response speed is the fraction of market reaction in the first two seconds after a macroeconomic release that occurs in the first 100 milliseconds, |${S1=\thinspace r\left( t,t+0.1 \right)} \mathord{\left/ {\vphantom {{S1=\thinspace r\left( t,t+0.1 \right)} {r\left( t,t+2 \right)}}} \right. } {r\left( t,t+2 \right)},\thinspace $|where |$r\left( t,t+0.1 \right)$| is the return in the first 100 milliseconds after the release and |$\mathrm{r}\left( t+2 \right)\thinspace $|is the return in the first 2 seconds after the release. S1 is unbounded and less intuitive when the numerator and denominator have conflicting signs. Therefore, similar to Beschwitz, Keim, and Massa (2015), we also calculate the ratio of the absolute return in the first 100 milliseconds after the release to the sum of the absolute return in the first 100 milliseconds and the absolute return in the subsequent 1.9 seconds, |${S2=\thinspace \vert r\left( t,t+0.1 \right)\vert } \mathord{\left/ {\vphantom {{S2=\thinspace \vert r\left( t,t+0.1 \right)\vert } {\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right| \right).}}} \right. } {\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right| \right).}$||$S2\thinspace$| is bounded below by 0 and above by 1. Figure 6 View largeDownload slide Trend in the speed of market reaction The figure plots the trend in speed of market reaction over time. In panel A, the speed of market reaction (S1) is measured as the fraction of the 2-second price response that occurs in first 100 milliseconds after release, |$S1 = r\left( t,t+0.1 \right)/r(t,t+2)$|⁠, where |$r(t,t+0.1)$| is the return in the first 100 milliseconds after the release for the S&P 500 ETF (solid line) or S&P 500 E-mini futures (dotted line) and |$r\left( t,t+2 \right)$| is the return in the first 2 seconds after the release. In panel B, the speed of reaction (S2) is expressed as the ratio of absolute return in first 100 milliseconds after release to the sum of absolute return in first 100 milliseconds and the absolute return in the subsequent 1.9 seconds, |${S2= \vert r\left( t,t+0.1 \right)\vert }/{\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right|\right).}$| Each speed measurement is computed from midquotes each event day and averaged across the event type for a given year. The plot shows averages across events each year. Figure 6 View largeDownload slide Trend in the speed of market reaction The figure plots the trend in speed of market reaction over time. In panel A, the speed of market reaction (S1) is measured as the fraction of the 2-second price response that occurs in first 100 milliseconds after release, |$S1 = r\left( t,t+0.1 \right)/r(t,t+2)$|⁠, where |$r(t,t+0.1)$| is the return in the first 100 milliseconds after the release for the S&P 500 ETF (solid line) or S&P 500 E-mini futures (dotted line) and |$r\left( t,t+2 \right)$| is the return in the first 2 seconds after the release. In panel B, the speed of reaction (S2) is expressed as the ratio of absolute return in first 100 milliseconds after release to the sum of absolute return in first 100 milliseconds and the absolute return in the subsequent 1.9 seconds, |${S2= \vert r\left( t,t+0.1 \right)\vert }/{\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right|\right).}$| Each speed measurement is computed from midquotes each event day and averaged across the event type for a given year. The plot shows averages across events each year. Higher values of the response speed measures imply that the reaction to the macroeconomic announcement is concentrated in the first few milliseconds of release. Both under- and overreaction in the first few 100 milliseconds result in lower values of the measures, as reversals after the first 100 milliseconds result in negative values for S1 and larger denominators for S2. Figure 6 documents an increase in the speed of trading over time using both measures for SPY as well as ES. The increased speed of response is consistent with quicker reactions among liquidity-demanding LLTs and faster response from liquidity-supplying LLTs. 4.3 Impact of early access to macroeconomic news In 2007 Reuters began compensating the University of Michigan for the exclusive right to distribute their Consumer Sentiment survey. Reuters created a two-tiered access system for their customers: standard clients would have access to the information at 9:55 a.m. (five minutes before wide distribution), and premium subscribers could access the information in machine-readable form an additional two seconds early at 9:54:58 a.m.22 Although Reuters advertised its early access arrangement to LLTs, the practice was not widely known among other market participants until a former employee filed a lawsuit against the company suggesting it was illegal. In July 2013, Reuters agreed to end the practice at the request of the New York Attorney General.23 In the previous subsection we found evidence that the decline in the profits associated with liquidity-demanding LLTs may be related to the quick updating of quotes by liquidity-supplying LLTs. The early access to the Consumer Sentiment news release provides us with a natural experiment to test whether liquidity-demanding LLTs are able to profit from slow traders who may be unaware of their informational disadvantage. The timing of the suspension of early access is exogenous, and we use a difference-in-difference approach to control for changes in trading activity before and after the suspension of the practice. We focus on the sample period near the change, January 2013–June 2013 for the early access period and July 2013–December 2013 for the no-early-access period. During the early access period, ES had a volume per second of$\$$552 million in the first quarter-second following Consumer Sentiment information, compared with an average of \$$296 million following the other announcements. After ending the early access practice, the volume per second drops to just $\$$44 million in the first quarter-second, which suggests a huge effect due to the change. However, average volume in all other announcements also falls considerably to \$$37 million after July 2013, which highlights the importance of using a difference-in-difference approach. Table 8 reports the difference-in-difference estimates for trading volume for the first quarter-second (e.g., [(44 – 552) – (37 – 296)] |$=$| –$\$$249 million), as well as for other time intervals. Table 8 Effect of advanced access to consumer sentiment information on market activity and profits Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume \$$M Number of trades Number of quotes Value $\$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |-443<sup>***</sup>| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume \$$M Number of trades Number of quotes Value$\$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |-443<sup>***</sup>| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s \$$5,003 $\$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s \$$5,003$\$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 The table compares market activity and trading profits for Consumer Sentiment announcements relative to other macroeconomic news. We measure the incremental effect of Consumer Sentiment during the period in which Thomson Reuters sold two-second early access to Consumer Sentiment information, and we compare this difference to the analogous measure calculated after Reuters ended the practice in July 2013. The difference-in-difference estimates below are the post-advanced-feed period difference less the advanced-feed period difference. The advanced-feed sample is from January 2013–June 2013 and post-advanced-feed sample is from July 2013–December 2013. Panel A reports the estimates for stock market activity in the S&P 500 ETF (SPY) and the S&P 500 E-mini futures, and panel B reports the estimates for aggregate per event dollar profits. Number of trades and quotes are per second, and dollar volume and notional contract value are given in \$$ millions per second. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. Table 8 Effect of advanced access to consumer sentiment information on market activity and profits Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume $\$$M Number of trades Number of quotes Value \$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |$-443<sup>***</sup>$| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume$\$$M Number of trades Number of quotes Value \$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |$-443<sup>***</sup>$| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s $\$$5,003 \$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s$\$$5,003 \$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 The table compares market activity and trading profits for Consumer Sentiment announcements relative to other macroeconomic news. We measure the incremental effect of Consumer Sentiment during the period in which Thomson Reuters sold two-second early access to Consumer Sentiment information, and we compare this difference to the analogous measure calculated after Reuters ended the practice in July 2013. The difference-in-difference estimates below are the post-advanced-feed period difference less the advanced-feed period difference. The advanced-feed sample is from January 2013–June 2013 and post-advanced-feed sample is from July 2013–December 2013. Panel A reports the estimates for stock market activity in the S&P 500 ETF (SPY) and the S&P 500 E-mini futures, and panel B reports the estimates for aggregate per event dollar profits. Number of trades and quotes are per second, and dollar volume and notional contract value are given in \millions per second. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. There is modest evidence of a shift in trades and quotes from the first quarter-second to later in the first couple of seconds for Consumer Sentiment relative to the other announcements. However, the shift in quoting intensity does not translate into a significant change in profits. The incremental change in trading profit after Reuters ended early access is statistically insignificant. Relative to other macro announcements, early access to Consumer Sentiment had a modest impact on trading or profits. Overall, the practice of tiered release of information appears to have had little incremental impact on LLT profits or more generally on the process by which information is incorporated into prices. Whether information is released exclusively to algorithmic traders or distributed more broadly, the marginal market participant in the first couple of seconds following the release of machine-readable news is very likely to be a computer. The evidence suggests that regulations that constrain data-gathering firms to release information to clients at a single time may be unnecessary, although requiring transparency among information distributors regarding when information is available to various client groups would likely help improve faith in financial markets. In general, the practice of selling early access to market news is consistent with profit-seeking behavior by information providers. With a two-second head start, it would be possible for the slowest LLT to trade on new information more quickly than the fastest LLT. Therefore, the only way for LLTs to ensure that their costly investment in trade speed is not undercut is to invest in early access to information. In this way, information providers “force” LLTs to pay for early access. This type of profit-seeking behavior also applies to exchange access (co-location) fees, which puts a downward pressure on LLT profits. 4.4 Trend in price discovery If liquidity providers are increasingly able to react to new public information, we would expect to see a reduction over time in the information contained in the post-announcement order flow. We test this conjecture using the state space model approach of Brogaard, Hendershott, and Riordan (2014). They explore a sample of HFT trades and find that the liquidity-demanding trades facilitate price discovery by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors. In our setting, we assume that trades executed within the first two seconds following macro news releases are initiated by liquidity-demanding LLTs, and we examine the impact of their order imbalances on the permanent price changes. For each event day, we sample the midquote price at the beginning of each 100-millisecond interval from two minutes before to two minutes after an event. We then estimate an Unobserved Component Model to extract the change in permanent and temporary price components. In particular, following Brogaard, Hendershott, and Riordan (2014) and Menkveld, Koopman, and Lucas (2007), the Observation Equation (1) and State Equation (2) are described as follows: \begin{align} p_{t} & = m_{t} + s_{t}\\ \end{align} (1) \begin{align} m_{t} & = m_{t-1} + w_{t}, \end{align} (2) where |p_{t}| refers to the log of midquote at the end of each tenth of a second, |m_{t}| is the unobserved true or efficient price, |w_{t}| is the permanent component, and |s_{t}| is the transitory component. In the first stage, we estimate the two components for each event day. In the second stage, we regress the change in permanent component (⁠|w_{t})| and the temporary component (⁠|s_{t})| on the order imbalance (OIB) during that 100-millisecond interval, in the first two seconds after the event, as follows: \begin{align} w_{t} & = c + \alpha \textit{ OIB}_{t} + v_{t}\\ \end{align} (3) \begin{align} s_{t} & = k + \mu s_{t-1} + \beta OIB_{t} + u_{t}. \end{align} (4) We estimate the Unobserved Component Model in Equations (1) and (2) and the regressions in Equations (3) and (4) separately for each announcement24 and then average |\alpha | and |\beta | coefficients across announcements each year and calculate the corresponding standard errors, which are clustered by month. The results are presented in Table 9. The coefficient estimates of |\alpha | and |\beta | are presented over the periods –120 to –60 seconds, 0 to 2 seconds, and 60 to 120 seconds, with time 0 being the announcement. The table reports statistical significance for each coefficient estimate using one, two, or three stars to denote significance at the 0.1, 0.05, and 0.01 levels. We also test whether parameters estimated during the zero- to two-second interval are statistically different from estimates from the periods before and after. We display significance for these tests at the 5% level with bold font (for the periods of –120 to –60 or 60 to 120 seconds). Table 9 Permanent and temporary effects of order imbalance on prices around macroeconomic news Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** This table presents the results of state space model estimation. The log midquote price |p_{t}| is modeled to have a permanent component |m_{t}| and a transitory component |s_{t}|⁠. The permanent component |m_{t}| is modeled as a random walk, and the transitory component is modeled as a stationary process as follows: \begin{align*} P_{t}&= m_{t} + s_{t}\\ m_{t}&= m_{t-1} + w_{t}\\ w_{t}&= c + \alpha OIB_{t} + v_{t}\\s_{t}&= k + \mu s_{t-1} + \beta OIB_{t} + u_{t} \end{align*} The two components for each event day are estimated using an unobserved component model with log of midquotes observed every 100 milliseconds in the interval from 2 minutes before to 2 minutes after the event. Then the components in the following three intervals, 120 seconds to 60 seconds before the announcement (–120s to –60s), the first two seconds after the event (0 to 2s), and 60 seconds to 120 seconds after the announcement (60s to 120s), are regressed on the order imbalance (OIB|_{t})| during the interval. The coefficient is the change to the corresponding component of price in basis points for unit change in order imbalance. The reported results are the time-series average across events of the estimates, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The S&P 500 ETF (SPY) sample in panel A covers 2008–14, and the E-mini futures sample in panel B covers July 2011–December 2014. Numbers in bold indicate that the mean for corresponding year is statistically different from the mean during the interval 0 to 2 seconds after announcement at the 5% level. Table 9 Permanent and temporary effects of order imbalance on prices around macroeconomic news Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** This table presents the results of state space model estimation. The log midquote price |p_{t}| is modeled to have a permanent component |m_{t}| and a transitory component |s_{t}|⁠. The permanent component |m_{t}| is modeled as a random walk, and the transitory component is modeled as a stationary process as follows: \begin{align*} P_{t}&= m_{t} + s_{t}\\ m_{t}&= m_{t-1} + w_{t}\\ w_{t}&= c + \alpha OIB_{t} + v_{t}\\s_{t}&= k + \mu s_{t-1} + \beta OIB_{t} + u_{t} \end{align*} The two components for each event day are estimated using an unobserved component model with log of midquotes observed every 100 milliseconds in the interval from 2 minutes before to 2 minutes after the event. Then the components in the following three intervals, 120 seconds to 60 seconds before the announcement (–120s to –60s), the first two seconds after the event (0 to 2s), and 60 seconds to 120 seconds after the announcement (60s to 120s), are regressed on the order imbalance (OIB|_{t})| during the interval. The coefficient is the change to the corresponding component of price in basis points for unit change in order imbalance. The reported results are the time-series average across events of the estimates, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The S&P 500 ETF (SPY) sample in panel A covers 2008–14, and the E-mini futures sample in panel B covers July 2011–December 2014. Numbers in bold indicate that the mean for corresponding year is statistically different from the mean during the interval 0 to 2 seconds after announcement at the 5% level. Over the whole sample, we see that the post-announcement SPY order imbalance (labeled 0 to 2 seconds) positively predicts movement in the permanent price component, consistent with Brogaard, Hendershott, and Riordan (2014). The coefficient on the transitory component is orders of magnitude lower. For the 2008–14 period, the impact of order flow on the permanent component is a statistically significant 0.224 basis points per unit of OIB. For the temporary component the impact is 0.005 basis points per unit of OIB. In the case of ES, over the 2011–14 sample period, the impact of order flow on the permanent component is 0.408 basis points per unit of OIB, and on the temporary component it is a statistically insignificant 0.02 basis points. While the impact of OIB is positive for the temporary component in the case of SPY, it is orders of magnitude smaller than that for the permanent component. The impact of order flow on the permanent price movements declines in recent years. The coefficient |\alpha |⁠, which measures the impact of liquidity-demanding LLTs on the permanent component of price changes, is the highest in 2011 for both SPY and ES. For SPY, |\alpha | is 0.668 in 2011 and 0.587 in 2012 but declines to 0.229 in 2013 and –0.027 in 2014. In the case of ES, |\alpha | is 1.06 in 2011 but declines to 0.64 in 2012, 0.22 in 2013, and 0.01 in 2014. In both the instruments, the difference in |\alpha | between 2011 and either 2013 or 2014 is statistically significant.25 The decrease in the informativeness of LLT order flow over time is consistent with the hypothesis that prices respond to news with little trading, either because liquidity providers also have access to the announcement information or they have become increasingly adept at quickly reacting to information in the order flow within the first two seconds after the announcement. Table 9 shows that in 2014, post-announcement order flow is not related to the permanent component of prices. The evidence that order flow no longer contains information following macroeconomic announcements is consistent with liquidity-supplying LLTs subscribing to news in digital form and adjusting prices rather than reacting to order flow. This is consistent with Lyle and Naughton (2016), who note that technological improvements have helped to enhance the monitoring ability of market makers who efficiently update quotes and avoid being picked off on stale quotes. Our evidence would seem to imply that liquidity-supplying LLTs should exit the market around macro announcements. However, Brogaard, Carrion, et al. (forthcoming) show that, on average, HFTs profit from supplying liquidity even during periods with extreme price movements, which may help explain their continuing participation. Also, Jovanovic and Menkveld (2016) argue that LLTs’ ability to quickly change quotes makes them more willing to provide liquidity. Consistent with this view, we find that quotes react more quickly to macro news surprises over time. 4.5 Discussion In the context of macro announcements, we find that the liquidity-supplying LLTs quickly adjust their quotes in the direction of the surprise, as they are becoming increasingly adept at reacting to order flow shocks in recent years, possibly by subscribing to machine-readable news. We do not find evidence consistent with liquidity-demanding LLTs exploiting slow retail traders. However, one concern is that in a world with LLTs, other liquidity providers are driven out and often liquidity is not available when needed, as in the case of the flash crash.26 Does this mean that LLTs should face regulation? Our response is that the rules should not be changed to eliminate the speed advantage of the LLTs for three broad reasons. First, our evidence suggests that market forces are working to reduce the LLTs’ speed advantage, which, in terms of profits, is not large to begin with. Moreover, since prices adjust to information shocks in milliseconds, it is unlikely that the slow individual investors will trade at prices far from the equilibrium price. Second, for a proper welfare comparison, it is important to consider a world with LLTs and a counterfactual world without LLTs. The literature provides no evidence that market quality would be better in a world without LLTs, notwithstanding the flash crash. The only evidence is from the pre-LLT world, when New York Stock Exchange (NYSE) specialists would often call market halts (the equivalent of circuit breakers) when the order imbalances became large. Halts are, of course, extreme illiquidity events. And third, while it is true that a social planner may not choose to spend the vast fortunes on reducing trading latency, one has to be mindful of unintended consequences of introducing regulations that eliminate LLT incentives to develop technologies that increase communication speeds. 5. Conclusion Is LLT simply faster trading? The speed of trading has increased steadily for decades, and it is unclear whether LLT represents a fundamental shift in how markets operate. Yet the introduction of many different trading venues, fragmentation of trading, and the large disparity in the speed of trading between LLTs and other market participants may have fundamentally changed markets in favor of those with resources to expend on latency-decreasing technology. We contribute to the LLT debate by exploring the profitability of fast trading following the release of macroeconomic news. Our evidence suggests that the marginal investor immediately following the release of macroeconomic information is a computer algorithm. Trading intensity in SPY and ES increases 100-fold within five milliseconds following the release of macroeconomic news. The result is a remarkably efficient response to news, with prices responding to announcement surprises within milliseconds. Although LLTs respond swiftly and convincingly to macroeconomic news releases, we find that the trading profits on announcement surprises are far smaller than those suggested in the popular press. The lack of meaningful profits from fast trading does not lend credence to naïve descriptions of how speed advantages translate into excessive rents for LLTs. While much of the existing theoretical literature on LLT relies on rent seeking by monopolistic LLTs who profit at the expense of the slower traders, our empirical results question the validity of speed-based monopolistic access to information. The findings are consistent with the increasingly faster quote updates over time by the liquidity-supplying LLTs. We find no evidence that the controversial practice of selling two-second early access to consumer sentiment information leads to incremental profits, possibly because both the liquidity demanders and suppliers around macroeconomic announcements are LLTs. Trading profits decrease with quote intensity and are lower in recent years. Quoted depths and trade sizes decrease, while the speed of trading has increased over time. There is a reduction in the informativeness of the post-announcement order flow over time, which points to an increasing ability of LLT quotes to respond directly to announcement surprises rather than indirectly through trading. Our results suggest that any potential market failure due to the fast traders’ monopolistic access to information is being addressed through market forces. We thank Ekkehart Boehmer, Jonathan Brogaard, Nandini Gupta, Terry Hendershott, Craig Holden, Vincent van Kervel, Jonghyuk Kim, Andrew Karolyi (the editor), Andrei Kirilenko, Katya Malinova, Albert Menkveld, Ryan Riordan, Michel Robe, Elvira Sojli, an anonymous referee, and seminar participants at the 2017 AFA meetings, the ABFER 2016 meetings, the EFA 2016 meetings, 11th Imperial College Hedge Fund Conference, 12th Annual Central Bank Conference on Microstructure of Financial Markets, the Behavioral Finance and Capital Markets conference at the University of South Australia, Deakin University, Frankfurt School of Finance and Management, Goethe University, Indiana University, Queens University, Stevens Institute of Technology, Tulane University, Vienna University and the SEC for helpful comments. Part of the work on this paper was done when Tarun Chordia was a Professorial Research Fellow at Deakin University. Supplementary data can be found on The Review of Financial Studies web site. Footnotes 1 HFTs are a subset of LLTs, as specifically defined by the Security and Exchange Commission’s (SEC’s) concept release on equity market structure (https://www.sec.gov/rules/concept/2010/34-61358.pdf). In the rest of the paper, we will use the term HFTs only for LLTs that fit the SEC definition. We study the Nasdaq HFT data in Section 2.2. 2 Anecdotal evidence abounds of high and remarkably consistent profits for high-speed trading firms. For example, the IPO prospectus for Virtu Financial noted that it had but one losing trading day over the course of four years. http://www.sec.gov/Archives/edgar/data/1592386/000104746914002070/a2218589zs-1.htm 3 Scholtus, van Dijk, and Frijns (2014) document that LLTs improve market quality following macro news releases. 4 Any errors in the classification of the trade direction will add noise but not bias to the profit estimates. 5 Each ES contract represents a contract size of \50 times the index value in points. For an S&P 500 index value of 2,000, each contract represents a notional value of\$$100,000. 6 Release time exceptions during our sample period are as follows: (i) personal income was usually released at 08:30 a.m., with the exception of December 23, 2014, when it was released at 10:00 a.m.; (ii) 10:00 a.m. was the most common release time for ISM Non-Manufacturing, with the exception of February 5, 2008, when it was released at 08:55 a.m.; (ii) University of Michigan Consumer Sentiment scheduled release time was 09:55 a.m. But when early access was available, it was released to subscribers at 09:54:58 a.m. 7 A specialized industry has sprung up to deliver machine-readable financial information to LLTs in milliseconds. For example, RavenPack is a news analytics firm that provides tradeable information to subscribers with a latency of 300 milliseconds, and Beschwitz, Keim, and Massa (2015) document increases in market response speed following coverage by RavenPack. 8 We thank Joel Hasbrouck for providing code to compute NBBO. See Hasbrouck (2010) for details. Holden and Jacobsen (2014) suggest that with extremely low latencies (as response times accelerate), the NBBO may not exist from the perspective of a trader, as the best quote information from distant exchanges may not be time synchronized. See also Angel (2014). 9 Table 1 uses a 5-minute time window rather than 10 seconds, and it also relies on a continuous measure of announcement surprise rather than grouping surprises into positive and negative categories. We continue to find an insignificant 10-second price response if we use the continuous surprise measure as in Table 1. 10 Section 4.3 analyzes the incremental profitability of trading on early access to Consumer Sentiment in more detail. 11 We find no evidence of timing inaccuracy for ES, although for SPY the half-second return prior to the official release time is a significant 0.6 basis points across announcements (Table 2). 12 Although LLTs have the ability to unwind very quickly, they may hold directional positions over longer periods if profits are available. We explore a variety of exit windows with the goal of obtaining an upper bound on the profits available from fast trading on macro news. 13 In the Online Appendix, Table IA.4, we report profits for each event per month to make the profits across events comparable. This method of computing profits does not affect the relative importance of events considered here. 14 Aggregate dollar profits of \$$19,000 per event in SPY and $\$$50,000 per event in ES are negligible in light of the costs involved in subscribing to real-time access to machine-readable news. For example, AlphaFlash (part of Deutsche Börse Group) charges roughly \$$10,000 per month for machine-readable access to several macroeconomic series (including inflation and employment announcements), plus an additional$\$$1,500 for access to the ISM announcements and \$$1,000 per month for Chicago PMI. Separately, Reuters charged up to $\$$6,000 per month for early access to Consumer Sentiment information. Moreover, these expenses do not include initial setup fees and other monthly product fees or take into account commissions on trading. Thus, it would appear that subscribing to machine-readable news and trading on announcement surprises in SPY and ES would be routinely profitable only for relatively few LLTs with the lowest latencies. 15 We thank the referee for suggesting this analysis. 16 For example, according to State Street Global Advisors (the fund that manages SPY), average daily volume in SPY in 2014 was higher than the combined daily volume of the top 18 holdings in the S&P 500. 17 In unreported results, we find that measuring profits from trades during the first second following announcement releases results in smaller profits in general but produces a similar decline in profits in recent years. 18 For ES, there is no coefficient reported for surprise in the Low State as no low state observations occur during the ES sample period, 2011–14. 19 Table IA.7 in the Online Appendix depicts the decline in profits for the other instruments: QQQ, IWM, and the DJIA component stocks. 20 The results are qualitatively similar when we repeat the exercise with a shorter entry window, –0.5 to 1 second. The dummies for 2013 and 2014 in the SPY 1- to 5-minute exit strategy, for example, are – \$$31K and –$\$$22K, compared with –35K and – \$$24K in the current specification. 20a We also find no evidence that the decline in profits is related to the SEC naked access ban highlighted in Chakrabarty et al. (2016). Specifically, we examine market activity during three-month pre- and post-ban periods. The evidence is presented in the Online Appendix, Table IA.5, and indicates that there is no discernable drop in quoting or trading activity around macroeconomic release times. While the ban may have limited the activity of a subset of LLTs, it does not appear to have had a material effect on the liquid securities we consider, especially if the participating LLTs had broker-dealer licenses. 21 Our quotes-to-trades ratio measure is generally lower than the ratio of order submissions to order executions for the median firm reported in Hasbrouck and Saar (2013). While their measure is based on all displayed order messages for a particular stock, our measure uses only quote changes at the top of the order book. 22 Baer and Patterson (2014) notes that the New York Attorney General’s office sent subpoenas to more than a half-dozen HFTs, and the brief filed against Reuters describes their premium subscribers as “ultra low-latency,” which is consistent with HFTs being active market participants following macro news. 23 See Hu, Pan, and Wang (2017) for more details. 24 Brogaard, Hendershott, and Riordan (2014) estimate Equations (1)–(4) in one step using a Kalman filter and maximum likelihood. We opt for a two-step approach due to our small estimation samples. Stock and Watson (1989) point out that a two-step approach helps prevent misspecification in Equations (3) and (4) from inducing inconsistency in Equations (1) and (2), but at the cost of potential inefficiency. 25 We also test the informativeness of trades using the Hasbrouck (1991) VAR methodology. We implement the VAR model for midquote changes and trades. Each announcement day, we estimate the VAR in transaction time for all transactions in the first two seconds after the announcement. The Online Appendix, Table IA.6, reports the results. Similar to the state space results, the informativeness of trades decreases over time since 2011. 26 Kirilenko et al. (2017) show that while the HFTs did not cause the flash crash, they exacerbated the decline in prices by becoming liquidity demanders themselves. References Angel, J. 2014 . When finance meets physics: The impact of the speed of light on financial markets and their regulation. Financial Review 49 : 271 – 81 . Google Scholar Crossref Search ADS Baer, J., and Patterson. S. 2014 . Goldman, Barclays, Credit Suisse draw high-speed trading scrutiny. Wall Street Journal, May 9 . Balduzzi, P., Elton, E. J. and Green. T. C. 2001 . Economic news and bond prices: Evidence from the U.S. Treasury market. Journal of Financial and Quantitative Analysis 36 : 523 – 43 . Google Scholar Crossref Search ADS Baron, M. D., Brogaard, J. Hagströmer, B. and Kirilenko. A. A. 2016 . Risk and return in high frequency trading. Working Paper, Cornell University. Beschwitz, B. von, D. B. Keim, and Massa. M. 2015 . First to “read” the news: News analytics and high frequency trading. Working Paper , the Wharton School. Biais, B., Foucault, T. and Moinas. S. 2015 . Equilibrium fast trading. Journal of Financial Economics 116 : 292 – 313 . Google Scholar Crossref Search ADS Brogaard, J., Carrion, A. Moyaert, T. Riordan, R. Shkilko, A. and Sokolov. K. Forthcoming. High-frequency trading and extreme price movements. 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Journal of Financial Markets 16 : 680 – 711 . Google Scholar Crossref Search ADS Chaboud, A. P., Chiquoine, B. Hjalmarsson, E. and Vega. C. 2014 . Rise of the machines: algorithmic trading in the foreign exchange market. Journal of Finance 69 : 2045 – 84 . Google Scholar Crossref Search ADS Chakrabarty, B., Jain, P. K. Shkilko, A. and Sokolov. K. 2016 . Speed of Market access and market quality: Evidence from the SEC naked access ban. Working Paper , Saint Louis University. Chordia, T., Roll, R. and Subrahmanyam. A. 2011 . Recent trends in trading activity and market quality. Journal of Financial Economics 101 : 243 – 63 . Google Scholar Crossref Search ADS Conrad, J. S., Wahal, S. and Xiang. J. 2015 . High frequency quoting, trading, and the efficiency of prices. Journal of Financial Economics 116 : 271 – 91 . Google Scholar Crossref Search ADS Foucault, T., Hombert, J. and Rosu. I. 2016 . News trading and speed. Journal of Finance 71 : 335 – 81 . Google Scholar Crossref Search ADS French, K. 2008 . Presidential address: The cost of active investing. Journal of Finance 63 : 1537 – 73 . Google Scholar Crossref Search ADS Hasbrouck, J. 1991 . Measuring the information content of stock trades. Journal of Finance 46 : 179 – 207 . Google Scholar Crossref Search ADS Hasbrouck, J. 2003 . Intraday price formation in U.S. equity index markets. Journal of Finance 58 : 2375 – 2400 . Google Scholar Crossref Search ADS Hasbrouck, J. 2010 . The best bid and offer: A short note on programs and practices. Working Paper , New York University. Hasbrouck, J., and Saar. G. 2013 . Low-latency trading. Journal of Financial Markets 16 : 646 – 79 . Google Scholar Crossref Search ADS Hendershott, T., Jones, C. M. and Menkveld. A. J. 2011 . Does algorithmic trading improve liquidity? Journal of Finance 66 : 1 – 33 . Google Scholar Crossref Search ADS Hendershott, T., and Riordan. R. 2013 . Algorithmic trading and the market for liquidity. 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M. J. 1991 . Inferring trade direction from intraday data. Journal of Finance 46 : 733 – 46 . Google Scholar Crossref Search ADS Lewis, M. 2014 . Flash boys: A Wall Street revolt . New York : W. W. Norton. Lyle, M. R., and Naughton. J. P. 2016 . How does algorithmic trading improve market quality? Working Paper , Kellogg School of Management. McQueen, G., and Roley. V. V. 1993 . Stock prices, news, and business conditions. Review of Financial Studies 6 : 683 – 707 . Google Scholar Crossref Search ADS Menkveld, A. J. 2013 . High frequency trading and the new market makers. Journal of Financial Markets 16 : 712 – 40 . Google Scholar Crossref Search ADS Menkveld, A. J., Koopman, S. J. and Lucas. A. 2007 . Modeling around-the-clock price discovery for cross-listed stocks using state space methods. Journal of Business & Economic Statistics 25 : 213 – 25 . Google Scholar Crossref Search ADS Mullins, B., Rothfeld, M. McGinty, T. and Strasburg. J. 2013 . Traders pay for an early peek at key data. Wall Street Journal, June 12 . Scholtus, M., van Dijk, D. and Frijns. B. 2014 . Speed, algorithmic trading, and market quality around macro economic news announcements. Journal of Banking and Finance 38 : 89 – 105 . Google Scholar Crossref Search ADS Stock, J. H., and Watson. M. W. 1989 . New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual 4 : 351 – 94 . Google Scholar Crossref Search ADS © 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

# Rent Seeking by Low-Latency Traders: Evidence from Trading on Macroeconomic Announcements

The Review of Financial Studies, Volume 31 (12) – Dec 1, 2018
38 pages

Publisher
Oxford University Press
ISSN
0893-9454
eISSN
1465-7368
DOI
10.1093/rfs/hhy025
Publisher site
See Article on Publisher Site

### Abstract

Abstract Prices of the highly liquid S&P 500 exchange-traded fund (SPY) and the E-mini future (ES) respond to macroeconomic announcement surprises within five milliseconds, with trading intensity increasing over 100-fold following the news release. However, profits from trading quickly are relatively small, roughly $\$$19,000 (⁠ \$$50,000) per event for SPY (ES). Although the speed of information incorporation has increased in recent years, profits have not. Order flow has become less informative, consistent with prices responding directly to news rather than indirectly through trading. Our evidence indicates that low-latency liquidity demanders do not benefit materially from short-term monopolistic access to information. Received April 18, 2017; editorial decision November 4, 2017 by Editor Andrew Karolyi. 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. Financial information is increasingly being released to, interpreted by, and traded on by computers. Dramatic improvements in technology have allowed computer algorithms to dynamically monitor multiple trading venues and strategically execute orders. These algorithms emphasize speed, and as a result, trade latency has been reduced to milliseconds. The increasing prevalence of low-latency trading (LLT) has led to two main concerns: the welfare implications of investing huge sums to achieve sub-second speeds, and the broader issue of whether the presence of low-latency traders (LLTs) reduces trust in financial markets. Theory points toward mixed welfare implications for LLT. Jovanovic and Menkveld (2016) argue that LLTs face lower adverse selection costs through their ability to quickly change quotes, and as a result LLTs improve gains from trade through their greater willingness to provide liquidity to intertemporally separated buyers and sellers. On the other hand, Biais, Foucault, and Moinas (2015) and Budish, Cramton, and Shim (2015) point to the socially wasteful arms race between LLTs, as they expend greater resources to further reduce latency. Empirically, Brogaard, Hendershott, and Riordan (2014) find evidence that high-frequency traders (HFTs)1 facilitate price discovery by trading in the direction of permanent price changes and against transitory pricing errors. Carrion (2013) finds that individual stock prices incorporate information from order flow and market-wide returns more efficiently on days with high HFT participation. Conrad, Wahal, and Xiang (2015) find that LLT activity leads prices to more closely resemble a random walk, and Chaboud et al. (2014) find that LLT improves price efficiency through lower return autocorrelations and fewer arbitrage opportunities. Other research suggests that the activities of LLTs improve market quality through increased liquidity and lower short-term volatility (Hendershott, Jones, and Menkveld 2011; Chordia, Roll, and Subrahmanyam 2011; Hasbrouck and Saar 2013; Hendershott and Riordan 2013). LLTs have attracted the scrutiny of regulators due to concerns that their technological advantages create an uneven playing field among market participants (Baer and Patterson 2014). Some argue that LLTs’ ability to trade ahead of slower investors allows them to earn profits in excess of the risks involved. Biais, Foucault, and Moinas (2015) have argued that fast traders observe market information before slow traders, thus generating adverse selection and negative externalities. This short-lived monopoly access to information is deemed a market failure that allows LLTs to earn excessive rents.2 These developments have led to arguments in the popular press that markets are “rigged” in favor of high-speed traders (Lewis 2014), which erodes faith in financial markets and could raise firms’ cost of capital. We contribute to the LLT debate and assess whether LLTs possess short-term monopolistic access to information by quantifying the profits available from sub-second trading in response to the release of eighteen different macroeconomic (macro) news announcements. Macro news releases provide a clean experimental setting where the timing of the release is known in advance, information is distributed in machine-readable form, and announcement surprises are relatively easy to interpret. Trading profits therefore depend critically on speed, making this an ideal setting for studying LLT. We analyze quote and transaction data for the highly liquid S&P 500 exchange-traded fund or ETF (SPY) and the E-mini S&P 500 futures contract (ES). Trading intensity increases over 100-fold within five milliseconds following the release of macro news, and there is a significant shift in order imbalances in the direction of the announcement surprise (based on the Bloomberg consensus forecast). Prices react to announcement surprises within five milliseconds. This quick and efficient price response is consistent with the theoretical model of Foucault, Hombert, and Rosu (2016), who argue that LLT trades are more correlated with short-run price changes and that they account for a large fraction of the trading volume around news events. Although LLTs respond swiftly and convincingly to macro news releases, we find that profits from fast trading are surprisingly small relative to widely held conventional wisdom (e.g., Mullins et al. 2013). Specifically, trading in the direction of the announcement surprise results in average dollar profits (across market participants) of$\$$19,000 per event for SPY and roughly \$$50,000 per event for ES. This translates to roughly $\$$15 million in cumulative profits on average each year, which is trivial relative to about \$$4.7 trillion traded in SPY and$\$$35.8 trillion notional value traded in ES in 2012. The \$$15 million is also trivial compared with the cost of price discovery in U.S. markets, which at 0.67% of the market capitalization (French 2008) amounted to roughly $\$$100 billion in 2006. The average price response for our sample of macroeconomic news events is roughly seven basis points (bps), and bid-ask spreads are typically less than one basis point, which would imply larger profit opportunities than what is observed in the data. However, the evidence suggests that the posted quotes around news releases are not the stale, exploitable limit orders of slow investors but rather quickly changing quotes of the liquidity-supplying LLTs. In the first quarter of a second after a news release, we observe over 500 changes to the best bid or offer quote in SPY (across venues). This evidence is consistent with the increasing importance of LLTs as liquidity providers, as suggested by Menkveld (2013). We also exploit a natural experiment to test whether LLTs who receive early information are able to exploit slower traders to earn excess profits. In one controversial practice, Reuters sold access to the University of Michigan’s Consumer Sentiment Index to LLTs two seconds before wide release. Mullins et al. (2013) and Hu, Pan, and Wang (2017) suggest that market participants were not aware of the early release. The practice ended in July 2013 at the request of the New York Attorney General, and this exogenously driven change in early access provides us with a setting to examine the value of fast access to information. We find no evidence that purchasing the two-second early access to Consumer Sentiment data provides LLTs with incremental profits. While profits are lower after Reuters ended the practice, this appears to be part of a general downward trend in trading profits across all macro announcements. A difference-in-difference approach reveals no statistically or economically significant changes in profits between consumer sentiment and other macro announcements, consistent with a quick reaction among liquidity-supplying LLTs. Whether information is released exclusively to algorithmic traders or distributed more broadly, the marginal market participant in the first couple of seconds following the release of machine-readable news is very likely to be a computer. The practice of selling early access to macro news appears more consistent with a profit seeking behavior among information providers rather than the exploitation of slow traders. We next explore the evolution in profits over our sample period and find evidence consistent with faster quote updating by liquidity-supplying LLTs. Profits decrease with the intensity of quoting activity following the announcement, and the quotes-to-trades ratio has increased over time while the available depth and trade sizes have decreased. The speed of market reaction to macro announcements has also increased over the sample period. The informativeness of the post-announcement order flow has decreased over time, which points to an increasing ability of LLT quotes to respond directly to announcement surprises rather than responding indirectly through trading. These findings highlight the LLTs’ lower adverse selection costs when supplying liquidity due to their ability to quickly update quotes in light of new information, consistent with the model of Jovanovic and Menkveld (2016).3 Together, the findings point to faster trading by liquidity-demanding LLTs and yet even faster quote updating by liquidity-supplying LLTs. These findings have important implications for our understanding of LLT. In a clean setting where the importance of speed is paramount, the apparent lack of meaningful profits from trading quickly calls into question naïve descriptions of how speed advantages translate into excessive rents for LLTs. It is not clear that speed confers a meaningful advantage to LLTs in terms of monopolistic access to information. While much of the existing theoretical literature on LLTs relies on rent seeking by monopolistic LLTs who profit at the expense of the slower traders, our empirical results question the validity of speed-based monopolistic access to information. Future theory should not rely on speed alone as a source of monopoly power among LLTs. Our analysis can also inform discussions about regulating LLT. Baron et al. (2016) find that new HFT entrants have a propensity to underperform and exit, which points toward an uneven playing field even among LLTs and suggests that increased regulatory oversight may benefit financial markets. Brogaard and Garriott (2017), on the other hand, find evidence that new LLT entrants lead to crowding out, with reduced spreads and less informative incumbent order flow. Our evidence supports the view that low-latency trading is maturing, with no increased profits over time despite the accelerated market reaction to macro announcements. The absence of outsized profits does not necessarily imply the absence of a need to regulate. However, the idea that the existence of LLT profits indicate a need for regulation is implicit in concerns regarding market failure. In particular, the argument holds that if fast traders are so profitable, they must have short-term monopolistic access to information that allows them to extract rents from other market participants, and that this monopoly power should be regulated. Our findings suggest that media descriptions of LLT profits are overstated. While other microstructure issues, such as the potential for a flash crash, quote stuffing, spoofing, and hammering the close, may continue to warrant attention from regulators, our findings mitigate concerns that LLTs earn outsized profits through speed alone. 1. Data and Descriptive Statistics 1.1 Financial market data: S&P 500 ETF and E-mini futures We study the financial market response to macroeconomic announcements using two of the most liquid stock market instruments: the largest and most heavily traded S&P 500 ETF (SPY), and the S&P 500 E-mini futures (ES). Both instruments have been studied extensively in previous work (e.g., Hasbrouck 2003). For these securities we obtain quote and trade data from Tick Data (now OneMarketData) that is time-stamped to the millisecond. The data allows us to capture price movements and to assign the direction of trade at the millisecond level, which allows us to measure the profitability of trading on announcement surprises.4 Our sample covers 2008–14 for SPY and July 2011–December 2014 for ES. Although the SPY sample is longer, ETFs do not begin trading each day until 9:30 a.m. ES trades 24 hours (except for a break from 4:15 to 4:30 p.m. and from 5:15 to 6:00 p.m.; all times are Eastern Standard Time [EST]), and therefore the ES sample allows us to examine a number of important macroeconomic announcements that are released at 8:30 a.m. The notional traded value of ES is higher than the dollar trading volume in SPY.5 For example, in 2012 the average daily notional value traded was \$$ 142 billion for ES versus a trading volume of$\$$18.5 billion for SPY. Quoted spreads are smaller for SPY, between 0.5 and 1.0 basis points for SPY versus 1–2 basis points for ES, due to the smaller tick size (⁠ \$$0.25 for ES vs. $\$$0.01 for SPY). In our analysis, we explore the market response and profitability of trading in both securities. 1.2 Macroeconomic announcements We obtain information about macro announcements from Bloomberg, including the release date and time, reported value, the median consensus estimate, number of estimates, and the standard deviation across estimates. We consider the macroeconomic series studied in Balduzzi, Elton, and Green (2001) and/or Brogaard, Hendershott, and Riordan (2014), for which Bloomberg reports consensus estimates and the actual announced values. We also consider the University of Michigan Consumer Sentiment Index and the Chicago Purchasing Managers’ Index (PMI), which were released to certain subscribers prior to their wider release to the public. Table 1 presents descriptive information for the twenty-seven announcements considered in our study. All occur at a monthly frequency, with the exception of the University of Michigan Consumer Sentiment Index (biweekly release) and Initial Jobless Claims (weekly release). “Release time” is the most common release time (changes in release time are rare in our 2008–14 sample period).6 We report the earliest time of access for Consumer Sentiment and Chicago PMI. Each of the macroeconomic series we consider is well covered, with large numbers of analysts providing estimates for each release. The lowest average number of estimates is 20 for Personal Consumption, and the highest is 90 for Nonfarm Payrolls. The coverage suggests that these are highly watched, market-moving events. We also observe a reasonable number of positive and negative surprises during the sample period. Table 1 Macroeconomic announcements descriptive statistics Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠ \$$ billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠$\$$billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 The table presents descriptive statistics for the sample of announcements. Release time is the most common release EST time (to subscribers) during the sample period. The sample period covers 2008–14 for announcements released after 9:30 a.m. and July 2011 through December 2014 for announcements released before 9:30 a.m. Obs. is the number of announcement observations during the sample period. Announcement surprises are measured as the reported value less the median Bloomberg estimate. Surprise std. dev. denotes the standard deviation of announcement surprises, Num. of estimates is the mean number of estimates, and Positive (Negative) surprises is the fraction of announcements that are positive (negative). For announcements after (before) 9:30 a.m., we regress the change in SPY midquote (change in S&P 500 E-mini futures midquote) from 5 minutes before to 5 minutes after the announcement on the standardized announcement surprise. Surprise coefficient is the resulting coefficient, with *, **, and *** indicating significance at the 10%, 5%, and 1% levels. Table 1 Macroeconomic announcements descriptive statistics Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠ \$$ billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 Announcement Release time Frequency Obs. Surprise std. dev. Num. of estimates Positive surprises Negative surprises Surprise coefficient CPI MoM (% change) 8:30 Monthly 42 0.12% 83 21% 40% –0.06*** CPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.08% 81 21% 33% –0.05** Durable Goods Orders (% change) 8:30 Monthly 42 3.22% 78 64% 31% 0.03 Housing Starts (thousands) 8:30 Monthly 40 61.9 80 45% 55% 0.05** Initial Jobless Claims (thousands) 8:30 Weekly 183 15.6 48 44% 55% –0.05*** Nonfarm Payrolls (change in thousands) 8:30 Monthly 42 56.3 90 50% 50% 0.30*** Personal Consumption (% change) 8:30 Monthly 42 0.45% 20 48% 48% 0.06** Personal Income (% change) 8:30 Monthly 42 0.37% 74 26% 48% 0.01 PPI MoM (% change) 8:30 Monthly 42 0.27% 74 36% 50% 0.03 PPI MoM excl. Food and Energy (% change) 8:30 Monthly 42 0.15% 69 36% 29% 0.02 Retail Sales (% change) 8:30 Monthly 42 0.30% 82 43% 43% 0.10*** Trade Balance (⁠ $\$$billions) 8:30 Monthly 42 3.8 71 48% 52% –0.02 Unemployment Rate (% level) 8:30 Monthly 42 0.14% 85 24% 57% –0.05 Capacity Utilization (% level) 9:15 Monthly 42 0.36% 65 45% 45% 0.04*** Industrial Production (% change) 9:15 Monthly 42 0.40% 82 43% 48% 0.04** Chicago PMI (index value) 9:42 Monthly 84 4.0 53 58% 40% 0.15*** Consumer Sentiment (index value) 9:54:58 Biweekly 168 2.9 64 54% 45% 0.06*** Business Inventories (% change) 10:00 Monthly 84 0.21% 48 40% 43% 0.01 Construction Spending (% change) 10:00 Monthly 83 0.99% 49 51% 47% 0.02 Consumer Confidence (index value) 10:00 Monthly 84 5.4 71 48% 51% 0.22*** Existing Home Sales (thousands) 10:00 Monthly 84 216.2 73 49% 48% 0.13*** Factory Orders (% change) 10:00 Monthly 83 0.70% 62 49% 47% 0.06*** ISM Manufacturing (index value) 10:00 Monthly 84 1.9 77 64% 35% 0.23*** ISM Non-Manufacturing (index value) 10:00 Monthly 84 2.1 72 57% 43% 0.06** Leading Indicators (% change) 10:00 Monthly 84 0.20% 53 51% 29% 0.07** New Home Sales (thousands) 10:00 Monthly 83 36.0 73 45% 53% 0.14*** Wholesale Inventories (% change) 10:00 Monthly 85 0.57% 31 54% 40% –0.02 The table presents descriptive statistics for the sample of announcements. Release time is the most common release EST time (to subscribers) during the sample period. The sample period covers 2008–14 for announcements released after 9:30 a.m. and July 2011 through December 2014 for announcements released before 9:30 a.m. Obs. is the number of announcement observations during the sample period. Announcement surprises are measured as the reported value less the median Bloomberg estimate. Surprise std. dev. denotes the standard deviation of announcement surprises, Num. of estimates is the mean number of estimates, and Positive (Negative) surprises is the fraction of announcements that are positive (negative). For announcements after (before) 9:30 a.m., we regress the change in SPY midquote (change in S&P 500 E-mini futures midquote) from 5 minutes before to 5 minutes after the announcement on the standardized announcement surprise. Surprise coefficient is the resulting coefficient, with *, **, and *** indicating significance at the 10%, 5%, and 1% levels. 1.3 Market-moving events The twenty-seven macroeconomic releases that we consider may not all affect financial markets in a significant way. We begin by objectively assessing which releases are potentially important to low-latency traders. Specifically, we follow Balduzzi, Elton, and Green (2001) and regress percentage midquote price changes, measured from five minutes before to five minutes after the release, on the standardized announcement surprises. Surprises are measured as the difference between the actual value of the release and its median estimate, standardized by its time-series standard deviation. For releases before (after) 9:30 a.m., we use price changes for ES (SPY). The coefficient on the standardized surprise is reported in the final column of Table 1. It represents the change in price associated with a one-standard-deviation increase in announcement surprise. The largest price impact is 30 basis points for a one-standard-deviation change in Nonfarm Payrolls. Eighteen different types of macroeconomic news have a statistically significant impact on stock prices at the 5% level. We restrict our attention to these eighteen releases for the rest of our analysis. The coefficients on CPI, CPI excluding Food and Energy, and Initial Jobless Claims are negative, as higher-than-expected inflation and unemployment had negative implications for the stock market during the sample period. For ease of interpretation, we multiply these surprises by –1 so that all positive surprises are associated with good news for the stock market. 2. Market Response to Macroeconomic News The pace of trading in financial markets has increased rapidly in recent years. In 2000, Busse and Green (2002) find that firm-specific information released during market hours is incorporated into prices within one minute. Speed of communication has since improved dramatically, leading to LLTs who strive to achieve low latency by investing in technology and co-locating their servers in the same data centers as stock exchanges. Hasbrouck and Saar (2013) note that the fastest traders have an effective latency of two to three milliseconds. Brogaard, Hendershott, and Riordan (2014) find that in 2008 and 2009, it took several seconds for macroeconomic news to be incorporated in stock prices. We conjecture that the greater availability of machine-readable news and the increased presence of LLTs in recent years has led to faster information assimilation.7 In this section, we explore the role of LLTs in the process by which macroeconomic news is incorporated into prices. 2.1 Speed of information incorporation Table 2 presents the cumulative midquote returns for SPY and ES in the sub-seconds around eighteen macroeconomic news releases. We calculate the midquote price for the SPY at the beginning of each time period (second or tenth of a second) using the average of the National Best Bid and Offer (NBBO).8 Cumulative midquote returns for each period are computed relative to the midquote that prevailed 20 seconds before the event. The returns for ES are calculated in a similar manner. Negative surprises are releases in which the actual was below the consensus median, (above the consensus for CPI, CPI excluding Food and Energy, and Jobless Claims). Following positive (negative) surprises, we expect the cumulative midquote returns to be positive (negative). In Table 2, we combine positive and negative surprises together and report the mean absolute cumulative returns. Panel A reports the price response of SPY to macro announcements released after 9:30 a.m., and panel B reports the results for ES for the full set of eighteen announcements. Table 2 Stock market price response to macroeconomic news Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** The table reports mean cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures around macroeconomic news announcements. Returns are reported in basis points, and time is labeled in seconds. Cumulative returns are measured relative to the prevailing midquote 20 seconds before the announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding Food and Energy, and Jobless Claims announcements). The returns for negative surprises are multiplied by –1 and averaged with positive surprises. Panel A reports the results for the S&P 500 ETF (SPY), and panel B reports the results for S&P 500 E-mini futures. The SPY sample period covers 2008–14, and the E-mini sample is from July 2011 through December 2014. Statistical significance at the 10%, 5%, and 1% levels are labeled with *, **, and ***. Table 2 Stock market price response to macroeconomic news Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** Panel A: S&P 500 ETF (SPY) Time Chicago PMI Consumer Sentiment Consumer Confidence Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.2 0.1 0.4 0.0 –0.4 0.0 0.7 0.0 –0.1 0.1 |-|1.0 0.0 0.0 0.4 0.2 –0.4 0.4 0.6 –0.4 0.0 0.1 |-|0.5 –0.1 0.1 0.5 0.3 –0.3 0.5 0.6 –0.6 0.2 0.1 0.0 0.6 0.4 2.8*** 1.2** –0.4 0.6 0.7 –0.3 0.2 0.6*** 0.1 2.7*** 2.0*** 4.9*** 1.7*** –0.2 2.0*** 1.4** –0.2 0.5 1.7*** 0.2 2.9*** 2.6*** 5.9*** 2.4*** 0.0 2.0*** 1.3** 0.0 0.8 2.1*** 0.3 3.2*** 3.0*** 6.4*** 3.0*** 0.4 2.0*** 1.3** 0.1 1.5*** 2.4*** 0.4 3.5*** 3.2*** 6.7*** 3.6*** 0.4 2.3*** 1.5** –0.1 2.0*** 2.7*** 0.5 3.7*** 3.5*** 7.4*** 4.4*** 0.6 3.2*** 2.4*** 0.5 2.7*** 3.3*** 1.0 4.5*** 4.0*** 9.0*** 5.9*** 1.6*** 6.6*** 4.6*** –0.4 4.7*** 4.6*** 2.0 5.1*** 4.2*** 10.5*** 7.2*** 1.9*** 8.0*** 6.1*** –0.2 6.0*** 5.4*** 5.0 6.0*** 4.4*** 12.6*** 8.2*** 1.9** 10.0*** 7.8*** 0.4 7.3*** 6.4*** 10.0 7.1*** 4.8*** 12.3*** 8.8*** 2.4*** 10.7*** 7.8*** 1.2 7.6*** 6.9*** Panel B: S&P 500 E-mini futures Time CPI CPI excl. Food and Energy Housing Starts Jobless Claims Nonfarm Payrolls Personal Consump. Retail Sales Capacity Utilization Industrial Production |-|5.0 0.4 0.1 0.1 0.1 0.1 –0.3 0.2 0.1 –0.1 |-|1.0 0.3 –0.1 –0.1 0.0 –1.2 0.0 0.1 0.1 0.0 |-|0.5 0.3 0.2 0.0 –0.1 –3.3 0.0 0.2 0.1 0.0 0.0 0.4 0.3 0.0 0.0 –4.0 0.1 0.1 0.1 0.3 0.1 0.3 0.6 –0.2 0.2 0.4 0.1 –0.1 1.0** 1.3*** 0.2 0.3 1.1 –0.1 1.2*** 5.5 0.3 0.1 1.0** 1.3*** 0.3 0.2 1.2 0.2 2.1*** 6.6 0.6 0.4 0.9** 1.3*** 0.4 0.6 1.1 0.2 2.3*** 7.4* 0.9 0.5 0.9** 1.2*** 0.5 0.6 1.0 0.3 2.3*** 7.4* 0.9 2.2* 0.9** 1.3*** 1.0 0.6 1.9** 1.7** 2.4*** 9.3** 2.4* 3.8*** 1.0** 1.4*** 2.0 0.6 2.2* 3.0*** 3.0*** 14.6*** 2.0 5.6*** 0.9** 1.4*** 5.0 0.7 2.7* 3.7*** 3.6*** 21.0*** 2.4 5.7*** 1.3*** 1.7*** 10.0 1.6 2.2* 3.7*** 3.1*** 20.4*** 2.5 6.0*** 1.7*** 2.0*** Time Chicago PMI Consumer Sentiment Consumer Confid. Existing Home Sales Factory Orders ISM Manu. ISM Non-Manu. Leading Index New Home Sales All events |-|5.0 –0.4 –0.1 0.0 0.2 –0.6* –0.1 0.3 0.0 0.0 0.0 |-|1.0 –0.3 –0.1 –0.3 0.2 –0.6* 0.2 0.3 0.1 0.1 –0.1 |-|0.5 –0.2 0.0 –0.2 0.2 –0.5* 0.0 0.5 0.0 0.1 –0.2 0.0 0.1 0.3 0.9 0.5 –0.7** 0.2 0.4 0.1 0.0 –0.1 0.1 4.1*** 2.9*** 5.0*** 0.8 –0.7** 3.5*** 1.4** 0.2 0.1 1.2*** 0.2 4.0*** 3.2*** 5.6*** 1.0** –0.7* 3.3*** 1.4** 0.5 –0.1 1.9*** 0.3 4.3*** 3.0*** 5.4*** 1.4*** –0.3 2.9*** 1.2** 0.4 0.0 2.1*** 0.4 4.4*** 3.0*** 5.6*** 1.5*** –0.4 3.8*** 1.6*** 0.4 0.0 2.3*** 0.5 4.5*** 3.0*** 5.5*** 2.2*** –0.6 5.2*** 3.1*** 0.5 0.3 2.7*** 1.0 5.5*** 3.2*** 6.0*** 3.1*** 0.3 7.9*** 4.2*** 0.1 1.1 3.5*** 2.0 5.7*** 3.1*** 6.8*** 3.6*** 0.3 8.1*** 4.7*** 0.1 2.2** 4.3*** 5.0 5.2*** 3.2*** 7.7*** 4.0*** 0.4 10.4*** 5.4*** 0.1 2.3*** 5.1*** 10.0 5.9*** 3.2*** 7.2*** 4.5*** 0.3 11.2*** 4.8*** 0.2 3.2*** 5.1*** The table reports mean cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures around macroeconomic news announcements. Returns are reported in basis points, and time is labeled in seconds. Cumulative returns are measured relative to the prevailing midquote 20 seconds before the announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding Food and Energy, and Jobless Claims announcements). The returns for negative surprises are multiplied by –1 and averaged with positive surprises. Panel A reports the results for the S&P 500 ETF (SPY), and panel B reports the results for S&P 500 E-mini futures. The SPY sample period covers 2008–14, and the E-mini sample is from July 2011 through December 2014. Statistical significance at the 10%, 5%, and 1% levels are labeled with *, **, and ***. Prices respond significantly to announcement surprises within the first 100 milliseconds (ms) following the release, which points toward LLT. Kosinski (2008) surveys the literature on reaction time and notes that human reaction (single response to single stimulus) is on the order of 200 milliseconds. The evidence suggests that the marginal market participant at the release of macroeconomic news is a computer that interprets the announcement surprise and revises quotes or routes orders within a tenth of a second. The average price reaction over the first two seconds of 5.4 (4.3) basis points for SPY (ES) accounts for 78% (84%) of the 10-second price reaction. This fraction is considerably larger than the roughly 50% two-second price reaction documented in Brogaard, Hendershott, and Riordan (2014), which is consistent with broader adoption of machine-readable news after the end of their sample in 2009. The announcements of CPI, Factory Orders (in the case of ES), and Leading Index have significant surprise coefficients in Table 1, yet they do not exhibit a significant price reaction in the first 10 seconds after announcement, which suggests that these announcements were either not available in machine-readable format or were not deemed important by LLTs.9 In untabulated results, we find that dropping these events increases the average reaction in SPY and ES by roughly one basis point (the results are otherwise similar). The Consumer Sentiment announcement also merits special attention, as for most of the sample period, early access subscribers were able to obtain information in machine-readable form two seconds prior to wider release. Using the early access time (9:54:58) as the information release time during this period of the sample, we find SPY prices incorporate roughly 73% of the 10-second price response within a half-second, and ES prices react as quickly if not more so.10 Regardless of whether information is released exclusively to LLTs or more widely, LLTs are the primary agents for incorporating new (machine-readable) information into prices. Figure 1 disaggregates positive and negative announcement surprises and plots the average cumulative price response for the SPY (panels A and B) and ES (panels C and D) across announcements. The figures show that the speed of price reaction to negative surprises is similar to the price reaction to positive surprises. Consistent with Table 2, panels A and C reveal that most of the price reaction happens within the first couple of seconds. Panels B and D focus on the two-second subperiod and more finely partition price changes into 100-millisecond intervals. A large portion of the price reaction occurs within the first second. Figure 1 View largeDownload slide Stock market price response to macroeconomic news releases The figure plots the average cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures (futures) around macro news releases. In panels A and C, returns are measured each second relative to midquote 20 seconds before the event. In panels B and D, returns are measured every 100 milliseconds relative to 20 seconds before the event. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. The numbers in the horizontal axis represent the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). Figure 1 View largeDownload slide Stock market price response to macroeconomic news releases The figure plots the average cumulative midquote returns for the S&P 500 ETF (SPY) and S&P 500 E-mini futures (futures) around macro news releases. In panels A and C, returns are measured each second relative to midquote 20 seconds before the event. In panels B and D, returns are measured every 100 milliseconds relative to 20 seconds before the event. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. The numbers in the horizontal axis represent the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). To statistically test for the speed of price response, we calculate price changes relative to the midquote measured 20 seconds after the announcement. In this setting, price changes should generally be statistically significant when measured before the event and gradually become insignificant as information is incorporated into prices. The resulting |t|-statistics are presented in Figure 2. For SPY, negative news is priced in within four seconds, and positive surprises are incorporated within five seconds. For ES, the analogous numbers are five seconds and two seconds. Taken together, the evidence suggests that machine-readable news and high-speed algorithms have diminished the role of humans while greatly increasing the speed with which prices incorporate new information. Figure 2 View largeDownload slide Speed of stock market price response to macroeconomic news The figure plots the t-statistics of midquote returns for the S&P 500 ETF (SPY) and the S&P 500 E-mini futures (futures) around macro news. Returns are measured each second relative to midquote 20 seconds after the event. The numbers in the horizontal axis is the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. Figure 2 View largeDownload slide Speed of stock market price response to macroeconomic news The figure plots the t-statistics of midquote returns for the S&P 500 ETF (SPY) and the S&P 500 E-mini futures (futures) around macro news. Returns are measured each second relative to midquote 20 seconds after the event. The numbers in the horizontal axis is the time in seconds relative to event announcement. Negative (positive) surprises are events in which the announcement was below (above) the consensus median forecast (the opposite is true for CPI, CPI excluding food and energy and jobless claims announcements). The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. 2.2 Trading and quoting activity This section analyzes trading and quoting activity around macroeconomic announcements. In particular, we examine the total dollar volume of trades per second (notional value for futures), number of trades per second, number of quote changes per second, and order imbalances in SPY and ES. We use the period five minutes to five seconds before the release time as a benchmark. We report volume, number of trades, and number of quote changes per second to facilitate comparisons across intervals. Table 3 reports the results. The index instruments are highly liquid. In the benchmark period, there are more than 30 trades per second and 350 quote changes in SPY (across all market venues), accompanied by dollar volume of roughly \$$ 2 million per second. We find no changes in trading or quoting activity in the five seconds prior to the release. In the first five milliseconds after the announcement, trading (quoting) activity increases more than 100-fold (15-fold) to more than 6,000 quotes and trades per second, with trading volume jumping to$\$$614 million per second. ES experiences an even larger jump in notional volume, rising from \$$3 million during the benchmark period to about $\$$1,200 million per second in the first five milliseconds after the release. Trading and quoting activity in both instruments remain significantly elevated for up to three seconds after the announcement. Table 3 Stock market activity around macroeconomic news releases Time Dollar volume \$$ millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |$-$|5m to |$-$|5s 2 33 350 0.00 |$-$|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 Time Dollar volume$\$$millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |-|5m to |-|5s 2 33 350 0.00 |-|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 The table reports measures of trading activity around macro news releases. Panel A reports activity for the S&P 500 ETF (SPY), and panel B reports activity for the S&P 500 E-mini futures. Average dollar trading volume and notional volume (# of contracts |\times|Price|\times| 50) are reported in \$$ millions for each reported interval. Also reported are the number of trades per second, the number of quote changes per second, and the average order imbalance during each measured interval. Order imbalance (OI) is computed as the (# of buys – # of sells) / (# of buys |$+$| # of sells), where buys (sells) represents buyer- (seller-)initiated trades. For negative surprises, the negative of OI is used to compute the average across events. The interval –5m to –5 captures activity from 5 minutes to 5 seconds before the announcement. The other rows report the activity in the period reported in that row relative to the announcement. Statistical significance for a difference in means compared with a benchmark period measured –5 minutes to –5 seconds before the event, is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. Table 3 Stock market activity around macroeconomic news releases Time Dollar volume $\$$millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |-|5m to |-|5s 2 33 350 0.00 |-|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 Time Dollar volume \$$ millions (per second) Number of trades (per second) Number of quote changes (per second) Order imbalance Panel A: S&P 500 ETF (SPY) |$-$|5m to |$-$|5s 2 33 350 0.00 |$-$|5s to 0 2 47 247 0.05** 0 to 5ms 614*** 6,189*** 6,389*** 0.42*** 5ms to 10ms 566*** 5,489*** 6,608*** 0.38*** 10ms to 50ms 106*** 1,474*** 3,632*** 0.20*** 50ms to 100ms 49*** 909*** 2,530*** 0.17*** 100ms to 250ms 28* 496*** 1,562*** 0.12*** 250ms to 500ms 29* 468*** 1,433*** 0.11*** 500ms to 1000ms 21 406*** 1,464*** 0.07*** 1000ms to 2000ms 11 245** 1,015*** 0.07*** 2s to 3s 8 196 852*** 0.07*** 3s to 5m 3 63 618 0.01 Panel B: S&P 500 E-mini futures –5m to –5s 3 11 37 0.00 –5s to 0 4 12 22 0.02 0 to 5ms 1,195*** 3,745*** 994*** 0.18* 5ms to 10ms 1,591*** 4,781*** 1,029*** 0.33*** 10ms to 50ms 774*** 2,227*** 455*** 0.19*** 50ms to 100ms 373*** 1,106*** 524*** 0.13*** 100ms to 250ms 121*** 412*** 330*** 0.12*** 250ms to 500ms 77** 269*** 209*** 0.13*** 500ms to 1000ms 55* 199** 194*** 0.13*** 1000ms to 2000ms 33 107 146*** 0.13*** 2s to 3s 21 70 123*** 0.10*** 3s to 5m 8 27 83*** 0.03 The table reports measures of trading activity around macro news releases. Panel A reports activity for the S&P 500 ETF (SPY), and panel B reports activity for the S&P 500 E-mini futures. Average dollar trading volume and notional volume (# of contracts |$\times$|Price|$\times$| 50) are reported in$\$$millions for each reported interval. Also reported are the number of trades per second, the number of quote changes per second, and the average order imbalance during each measured interval. Order imbalance (OI) is computed as the (# of buys – # of sells) / (# of buys |+| # of sells), where buys (sells) represents buyer- (seller-)initiated trades. For negative surprises, the negative of OI is used to compute the average across events. The interval –5m to –5 captures activity from 5 minutes to 5 seconds before the announcement. The other rows report the activity in the period reported in that row relative to the announcement. Statistical significance for a difference in means compared with a benchmark period measured –5 minutes to –5 seconds before the event, is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The SPY sample period covers 2008–14, and the futures sample is from July 2011 to December 2014. We examine whether trading activity is oriented in the direction of announcement surprises by analyzing order imbalances. We assign transactions using the Lee and Ready (1991) algorithm. In particular, trades that are executed at a price higher (lower) than the prevailing midquote are treated as buys (sells). If a trade occurs at the midquote, then we compare the traded price to the previous traded price, and upticks (downticks) are classified as buys (sells). We then calculate order imbalance as (number of buys – number of sells) / (number of buys |+| number of sells). We expect positive order imbalance for positive surprises and the opposite for negative surprises. The last column of Table 3 reports mean order imbalances aggregated across positive and negative surprises, where we multiply negative surprise order imbalances by –1. The evidence is consistent with traders reacting to announcement surprises. In SPY (ES), order imbalance is 0 (0) during the benchmark period and 0.42 (0.18) and highly significant in the first five milliseconds after the news release. Order imbalance remains statistically significant for three seconds but falls considerably and loses significance afterwards. The relation between announcement surprise and order imbalance is similar when using the dollar value of purchases and sales (reported in the Online Appendix, Table IA.1). The evidence suggests that markets quickly incorporate new macroeconomic information, and part of the information is revealed through trading in the direction of the surprise. Our approach implicitly assumes that the trading immediately following macroeconomic announcements reflects the actions of LLTs. We explore the validity of this assumption by examining trading activity in the Nasdaq HFT database, which covers HFT trades in 120 stocks during the sample period 2008–09 (for more details, see Brogaard, Hendershott, and Riordan 2014). Using this data set, we examine transactions around macro announcements that represent HFT liquidity demand. The results are provided in the Online Appendix, Table IA.2. We find that in the first two seconds, HFT liquidity demand in the direction of surprise increases 10-fold, and 86% of HFT liquidity demand is in the direction of surprise in the first half-second of the release. The proportion of trades in the direction of the surprise decreases over time following the release, consistent with HFTs that traded on the news unwinding their positions. The evidence supports the view that macro announcements attract trading by HFTs and other LLTs. 3. Profitability of LLTs on Macroeconomic News The evidence in the previous section suggests that LLTs enhance market efficiency by swiftly and accurately responding to new information. This view is generally consistent with recent research on the effects of LLTs on financial markets (e.g., Brogaard, Hendershott, and Riordan 2014; Carrion 2013; Chaboud et al. 2014). However, the concern of regulators and other market watchdogs is that the contributions of LLTs to market efficiency come at the expense of reduced trust in financial markets. Conventional wisdom holds that LLTs’ speed advantage allows them to exploit slower market participants and earn profits that are disproportionate to the risks involved. For example, Hirschey (2016) finds that HFTs’ aggressive purchases and sales lead those of other investors, and Baron et al. (2016) find that aggressive (liquidity-taking) high-frequency trading is highly profitable on a risk-adjusted basis. In this section, we explore whether low latency translates into outsized profits for LLTs following macroeconomic announcements. In computing profits, we assume that all trades in the direction of the announcement surprise and executed within two seconds of the release are initiated by liquidity-demanding LLTs. We choose a two-second window based on the idea that human traders are unlikely to be able to respond to information within two seconds, and we note that Reuters also chose a two-second window for its early access arrangement for Consumer Sentiment information. The precise timing of the information release is also important for determining profits, and we include trades that occur up to 0.5 second before the official release time to allow for imprecision in the measurement of the release times.11 We calculate the volume-weighted average transaction price during the entry period—that is, purchases following positive surprises and sales following negative surprises, and compare it to the offsetting volume-weighted average transaction prices measured during three post-announcement exit periods: two to five seconds, five seconds to one minute, and one to five minutes after the announcement.12 We measure profits in short time intervals to focus on fast trading. We stop at five minutes after announcements to avoid the impact of other confounding information. Finally, we calculate aggregate dollar profits by multiplying the total dollar volume of trades in the direction of surprise during the entry period by the percentage price change. Table 4 reports the average profits. For SPY, the average total dollar profits across events when exiting two to five seconds after the event (at the volume-weighted offsetting price) are below \$$ 7,000. Using a one- to five-minute exit window increases aggregate profits to $\$$19,000, suggesting some price drift after the first five seconds. The profits from trading on Consumer Sentiment surprises do not exceed \$$6,000 (⁠$\$$8,000 in the case of ES) per event on average for any exit window despite being provided early to subscribing LLTs during most of the sample period. Profits are \$$83,000 for ISM Manufacturing, however, suggesting quick reaction to this information was more profitable. Table 4 Profitability of low-latency trading on macroeconomic news releases S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI – $\$$616 \$$2,709$\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233*** $\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI –$\$$616 \$$2,709 $\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233***$\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports profits for announcements that occur outside of regular trading hours (9:30 a.m. to 4:00 p.m. EST), and panel B reports profits for announcements that occur during regular trading hours. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to 2 seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s–1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Statistical significance is denoted by *, **, *** for significance at the 10%, 5%, and 1% levels. Table 4 Profitability of low-latency trading on macroeconomic news releases S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI – $\$$616 \$$2,709$\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233*** $\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** S&P 500 ETF (SPY) S&P 500 E-mini futures Announcement 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Events outside regular trading hours (RTH) CPI –$\$$616 \$$2,709 $\$$13,232 CPI excl. Food and Energy –4,088 –1,290 10,109 Housing Start 1,477 8,069** 16,282 Jobless Claims 2,408 1,447 |-|982 Nonfarm Payroll 162,449*** 221,196** 285,866** Consumption 1,982 15,839 20,179 Retail Sales 2,140 8,584 25,472** Capacity Utilization 134 423 2,516 Industrial Production –116 733 3,988 Outside RTH events 17,183*** 24,219*** 33,548*** Panel B: Events during regular trading hours Chicago PMI \$$10,233***$\$$10,798*** \$$23,467*** 40,166** 29,341* 105,328*** Consumer Sentiment 1,894*** 4,607*** 5,188* –1,472 4,392 7,699 Consumer Confidence 15,244*** 21,910*** 24,251*** 77,176*** 49,850** 9,794 Existing Home Sales 6,562*** 11,016** 22,331*** 16,538 45,768 101,824 Factory Orders 257 714 –1,117 281 1,050 –3,599 ISM Manufacturing 16,490*** 44,364*** 83,044*** 103,338*** 228,663** 386,334** ISM Non-manufacturing 6,099*** 5,994** 3,754 19,619 –9,329 8,150 Leading Index –123 5,433 5,438 2,582 14,003 14,152 New Home Sales 5,851*** 10,028*** 12,662*** 6,340 16,683** 14,942 During RTH events 6,600*** 12,134*** 18,771*** 26,937*** 39,257*** 66,663*** All events 6,600*** 12,134*** 18,771*** 21,936*** 31,547*** 49,685*** The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports profits for announcements that occur outside of regular trading hours (9:30 a.m. to 4:00 p.m. EST), and panel B reports profits for announcements that occur during regular trading hours. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to 2 seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s–1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Statistical significance is denoted by *, **, *** for significance at the 10%, 5%, and 1% levels. Notional values are considerably higher for ES, which leads to dollar profits that are an order of magnitude higher. For example, with a one- to five-minute exit window, average profits from trading on announcement surprises for Nonfarm Payrolls, Chicago PMI, Existing Home Sales, and ISM Manufacturing all exceed $\$$100,000. The drift in midquotes we see in Panel B of Table 2 for Nonfarm Payrolls and ISM Manufacturing after the first two seconds contributes to the profits for these announcements. For ES, we report profits during and outside of regular trading hours (RTH). Profits from trading outside of RTH are about half those from trading during RTH, possibly because there is more liquidity during RTH. Across all events, the average profits in the futures contract are roughly \$$50,000 per event.13 Figure 3 plots the percentage change in volume-weighted transaction prices surrounding the releases to provide a sense of scale for the dollar profits. We also partition the two-second entry window into smaller increments. We observe returns of about six basis points for SPY if positions are entered within the first tenth of a second and unwound one to five minutes after the announcement. However, these high returns translate to relatively low aggregate dollar profits due to the limited trading in the first tenth of a second. Wider spreads for the futures contract lead to lower returns, just over two basis points, but dollar profits are higher due to larger notional values traded. A half-second delay greatly reduces returns.14 Figure 3 View largeDownload slide Profitability of low-latency trading on macroeconomic news releases The figure shows average percentage profits (in basis points) from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements and unwound later at the volume-weighted average (offsetting) transaction price. The plot shows profits for various entry and exit periods. For example, the entry interval labeled 0.1s refers to the period 0.5 second before to 0.1 second after the event, and the exit period labeled 5m refers to the period 1 to 5 minutes after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Figure 3 View largeDownload slide Profitability of low-latency trading on macroeconomic news releases The figure shows average percentage profits (in basis points) from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements and unwound later at the volume-weighted average (offsetting) transaction price. The plot shows profits for various entry and exit periods. For example, the entry interval labeled 0.1s refers to the period 0.5 second before to 0.1 second after the event, and the exit period labeled 5m refers to the period 1 to 5 minutes after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Our findings are in stark contrast with descriptions of highly profitable “event-jumping” algorithmic trading in the media. For example, Mullins et al. (2013) highlight the March 15, 2013, release of Consumer Sentiment that led SPY prices to fall by$\$$0.27 over five minutes, with 310,000 shares traded in the first second (of which they suggest two-thirds were sales). Their numbers suggest a profit of (2/3 |\times| 310,000 |\times| 0.27) |=| \$$55,800, which is larger but on the same order of magnitude as the $\$$31,578 profit we obtain using volume-weighted average transaction prices for a –0.5- to 2-second entry window and a one- to five-minute exit window. Both numbers are several multiples of the \$$5,200 that we calculate on average for Consumer Sentiment announcements in Table 4. Similarly, the March 15, 2013, Consumer Sentiment aggregate profit we measure when trading in ES is$\$$352,643, which is many times larger than the average Consumer Sentiment profit in ES of \$$7,699. Thus, the examples mentioned in media articles seem to be outliers. An important caveat here is that we do not know the exact trading strategy of the LLTs. It may be that they are able to optimize their trades along some dimension, so as to earn higher profits than those we compute. On the other hand, our analysis focuses only on announcement types that have a significant impact on returns. We also present estimates of potential rather than actual profits, since some liquidity-demanding HFTs may lose money if they initially misinterpret the signal and trade in the wrong direction. In a sense, we provide an upper bound for profits to make them potentially comparable to the claims in the popular press. We explore upper-bound profits more thoroughly in the Online Appendix.15 First, we eliminate unprofitable trades as follows: for positive surprises, we match the lowest buy price in the entry window with the highest sell price in the exit window. We then match the next lowest buy with the next highest sell, and so on, until there are no more profitable trades (and use the opposite process for negative surprises). Compared with Table 4, the resulting profits reported in the Online Appendix, Table IA.8, are 2.5 (3.1) times higher for SPY (ES). In the Online Appendix, Table IA.9, we repeat this process, but determine trade direction optimally using the maximum profit available (rather than using announcement surprises). This perfect-foresight, no-loss approach results in profits that are 3.0 (3.8) times bigger for SPY (ES), which is still relatively small from an economic perspective. In the Online Appendix, Table IA.9, placebo tests that examine LLT profits at times other than the earnings announcement support the argument that profits exist solely following the announcement. For instance, the full foresight profits 15 fifteen minutes prior to the announcement amount to $\$$4,048 (⁠ \$$21,244) for the SPY (ES), compared with$\$$55,505 (⁠ \$$187,402) at the time of the announcement. Another potential concern is that we only consider two instruments, whereas algorithmic traders could conceivably submit orders in hundreds if not thousands of securities. We chose our instruments based on their high liquidity, where small price changes may potentially be profitable due to low quoted spreads and high depth.16 As a robustness check, we examine two additional ETFs, the Nasdaq index (QQQ) and the Russell 2000 index (IWM). We also consider the 30 stocks comprising the Dow Jones Industrial Average (DJIA), which are among the most liquid individual stocks. The results are presented in Table 5. Table 5 Profitability of low-latency trading on macroeconomic news releases: Other instruments Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports the profits in S&P 500 ETF (SPY), panel B reports the profits in S&P 500 E-mini futures (ES), panel C reports the total profits in Nasdaq 100 ETF (QQQ), panel D reports the profits in Russel 2000 ETF (IWM), and panel E reports the profits in Dow Components. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price announcements measured during the half-second before the announcement to the corresponding time in each column after the announcement. For example, 5ms indicates initiating the position during the half-second before the announcement to 5 milliseconds after the announcement. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For ETFs and stocks, the sample period covers 2008–14, and for ES the sample period covers July 2011–December 2014. Table 5 Profitability of low-latency trading on macroeconomic news releases: Other instruments Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 Entry window (–500ms, x) Exit time 5ms 10ms 50ms 100ms 250ms 500ms 1,000ms 2,000ms Panel A: S&P 500 ETF (SPY) 2s to 5s 2,128 2,437 3,227 3,721 4,448 6,112 6,808 6,600 5s to 1m 2,555 2,861 3,638 4,364 5,463 8,654 11,041 12,134 1m to 5m 2,834 3,219 4,424 5,635 7,477 12,290 16,478 18,772 Panel B: S&P 500 E-mini futures (ES) 2s to 5s 2,114 2,469 6,637 10,963 13,988 18,107 21,236 21,936 5s to 1m 2,239 2,567 5,341 10,695 12,983 18,602 27,945 31,547 1m to 5m 2,441 2,864 6,378 13,854 17,438 27,528 42,554 49,685 Panel C: Nasdaq 100 ETF (QQQ) 2s to 5s 607 656 828 1,064 1,453 1,740 2,126 2,012 5s to 1m 575 616 813 1,082 1,562 1,997 2,262 2,435 1m to 5m 791 870 1,164 1,524 2,112 2,970 3,364 3,927 Panel D: Russel 2000 ETF (IWM) 2s to 5s 370 410 512 593 735 941 970 927 5s to 1m 518 554 758 902 1,152 1,642 2,004 2,242 1m to 5m 805 843 1,182 1,454 1,881 2,719 3,468 4,151 Panel E: DJIA index component stocks 2s to 5s 643 694 951 1,206 1,606 2,164 2,511 2,104 5s to 1m 902 947 1,245 1,611 2,276 3,385 4,491 4,792 1m to 5m 1,257 1,327 1,793 2,366 3,423 5,174 7,087 7,973 The table reports average per-event dollar profits from trading on macroeconomic announcement surprises. Panel A reports the profits in S&P 500 ETF (SPY), panel B reports the profits in S&P 500 E-mini futures (ES), panel C reports the total profits in Nasdaq 100 ETF (QQQ), panel D reports the profits in Russel 2000 ETF (IWM), and panel E reports the profits in Dow Components. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price announcements measured during the half-second before the announcement to the corresponding time in each column after the announcement. For example, 5ms indicates initiating the position during the half-second before the announcement to 5 milliseconds after the announcement. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For ETFs and stocks, the sample period covers 2008–14, and for ES the sample period covers July 2011–December 2014. We finely partition the two-second entry window to isolate the profits available to LLTs with the lowest latency. Profits are significantly different from zero even with an entry window of five milliseconds after the announcement. All profit estimates in Table 5 are statistically significant from zero at the 1% level, and profits are highest in each instrument using a two-second entry window and one- to five-minute exit window. With these entry and exit windows, the profits from trading fast on QQQ amount to $\$$3,927; for IWM the profits amount to \$$4,151; and for the 30 stocks that compose the DJIA, the profits amount to$\$$7,973. While the average profits from trading in SPY and ES amount to about \$$69,000 per event, the profits from trading in QQQ, IWM, and the 30 component DJIA stocks amount to about $\$$16,000. Thus, compared with SPY and ES, dollar profits are considerably lower in the QQQ and the IWM as well as among the 30 DJIA stocks. While macro news may occasionally be significant enough to permit profits in less liquid securities, our evidence suggests these events are most common in the SPY and the ES. Overall, we find that stock index prices react near instantaneously to macro announcement surprises, yet profits to LLTs are surprisingly small. 4. Trend in Profits and Price Discovery We focus on profits available to liquidity demanders who trade on announcement surprises, which suggests that they profit at the expense of slower and therefore less informed liquidity suppliers. Although speed gives LLTs a potential informational advantage following macroeconomic news releases, an increasing fraction of liquidity is also being provided by fast traders who can post quotes confidently knowing they can update them quickly in light of new information. For example, Table 3 shows that both the number of trades and quotes increase dramatically immediately after the announcement. In this section, we explore the evolution of price discovery and trading profits throughout our sample period. 4.1 Trend in profits Anecdotal evidence suggests that liquidity providers may subscribe to real-time news “to keep from getting ‘flattened’” by other traders (Mullins et al. 2013). We conjecture that liquidity suppliers become increasingly adept at responding to information over time, either by subscribing to the machine-readable news themselves or by improving their ability to react to liquidity demanders. Figure 4 presents profits by year from trading in the first two seconds following macroeconomic surprises.17 For SPY, profits display a hump shape. Profits generally grow from 2008 to 2011, which is consistent with increased availability of machine-readable news, generally increasing market liquidity, and a greater presence of LLTs (e.g., Beschwitz, Keim, and Massa 2015). However, profits peak in 2011 and fall steadily in 2012, 2013, and 2014. Although the sample is shorter for ES, the decline since 2011 is also evident, with average profits from trading on macroeconomic news in 2014 being just \$$ 9,000 for ES. The lack of an increase in profits over time, despite increases in speed among liquidity-demanding LLTs, is consistent with an increasing ability of liquidity providers to react quickly to new public information. Figure 4 View largeDownload slide Trend in profitability of low-latency trading on macroeconomic news The figure plots the average per-event dollar profits from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to two seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Figure 4 View largeDownload slide Trend in profitability of low-latency trading on macroeconomic news The figure plots the average per-event dollar profits from trading on macroeconomic announcement surprises. Positions are assumed to be entered into at the volume-weighted average purchase (sale) price for positive (negative) announcements measured during the half-second before to two seconds after the event. Positions are unwound at the volume-weighted average (offsetting) transaction price during different intervals after the event. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. The S&P 500 ETF (SPY) sample period covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. As a robustness check, we also repeat the analysis by excluding certain events that do not move the prices by more than 3 bps in the sample period (Factory Orders and Leading Index for SPY and CPI, CPI excluding Food and Energy, Consumption, Capacity Utilization, Industrial Production, Factory Orders, and Leading Index for Futures). Table IA.3 in the Online Appendix presents the results. The pattern is similar, with profits peaking in 2011 and declining thereafter. We repeat the analysis after filtering out events that are contemporaneous with other announcements (which could potentially lead to conflicting trading signals), and again the results are similar in Table IA.3 in the Online Appendix. The decline in profits is also consistent with a reduction in the importance of announcement surprises since the price reaction to macro news depends on the surprise component. We therefore study the trend in profits after controlling for the impact of announcement surprises. Specifically, we follow the methodology in McQueen and Roley (1993) and allow price reactions to announcement surprises to vary with the business cycle. In particular, we measure the time trend in monthly industrial production (log seasonally adjusted) and compute upper and lower trend values using the 25th and 75th percentiles. The dummy High State (Low State) is equal to 1 if industrial production for the month is above (below) the upper (lower) bound, and 0 otherwise (where the dummy Medium State takes a value of 1). We multiply the stage of business cycle dummies with the absolute value of the announcement surprise and include them in the regression.|$^{\mathrm{\thinspace }}$| Table 6 presents results from panel regressions of dollar profits per event (panel A), percentage of profit per event (panel B), and the dollar trading volume per event (panel C) on year dummies and the absolute value of surprises during different stages of the business cycle. All the dependent variables are measured using entry windows from 500 milliseconds before the announcement to two seconds after. We include announcement fixed effects to control for differences in average profitability across announcements (Table 4). It is possible that market-wide news shocks (such as the start or cessation of quantitative easing by the Federal Reserve) could affect the information content and LLT trading profits across all the macro announcements, thus leading to cross-sectional correlation in residuals around the news event. We therefore base our inferences on standard errors clustered by month. We suppress the dummy for the year 2011.18 Table 6 Trading profits by year after controlling for variation in announcement surprises Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 The table presents the coefficient estimates from regressing trading profits and volume on surprises and year dummies. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position five seconds to 1 minute after the event. All strategies use an entry window of 0.5 second before to 2 seconds after announcements. Panel A reports the results for profits per event in dollars, panel B reports the results for percentage profits per event, and panel C reports the results for trading volume per event in dollars. Trading volume in futures is measured as # of contracts |$\times$| Price |$\times$| 50. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Events are allowed to have different responses to surprises at different stages of the business cycle (Low, Medium, and High). Dummy for year 2011 is suppressed. Event fixed effects are included in the regression, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. Table 6 Trading profits by year after controlling for variation in announcement surprises Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 Panel A: Dollar profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 5,901*** 4,394 9,336 Surprise (Medium) 11,751*** 25,066*** 40,663*** 51,274*** 83,407*** 125,122*** Surprise (High) 6,149*** 11,489*** 15,290*** 17,544** 20,272 35,944* |$\quad$| 2008 –5,133 –12,556 –13,823 |$\quad$| 2009 –4,811 2,196 6,580 |$\quad$| 2010 –2,765 1,707 7,630 |$\quad$| 2012 –5,731 –9,693 –8,648 –26,685 –39,961 –90,131 |$\quad$| 2013 –12,252** –27,957** –35,222* –76,888*** –100,537** –161,107* |$\quad$| 2014 –9,908** –19,152* –24,163 –55,422*** –68,635 –119,210 Adjusted |$R^{\mathrm{2}}$| 0.12 0.10 0.12 0.21 0.13 0.13 Panel B: Percentage of profits per event by year S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Surprise (Low) 0.04** 0.04* 0.11** Surprise (Medium) 0.02*** 0.04*** 0.06* 0.03*** 0.04** 0.09*** Surprise (High) 0.04** 0.05* 0.07 –0.01 0.01 0.11 |$\quad$| 2008 0.01 0.01 0.002 |$\quad$| 2009 –0.01 0.00 0.04 |$\quad$| 2010 0.02 0.00 0.09 |$\quad$| 2012 –0.00 –0.02 –0.05 –0.01 0.05 0.09 |$\quad$| 2013 –0.01 –0.08** –0.09 –0.05*** 0.01 0.08 |$\quad$| 2014 –0.02** –0.07** –0.04 –0.03*** 0.02 0.08 Adjusted |$R^{2}$| 0.03 0.01 0.02 0.04 0.01 0.02 Panel C: Dollar trading volume by year Coefficients S&P 500 ETF (SPY) S&P 500 E-mini futures Surprise (Low) 16,463,936*** Surprise (Medium) 20,572,733*** 69,646,438*** Surprise (High) 4,704,317 69,269,435** |$\quad$| 2008 –14,777,253* |$\quad$| 2009 –15,516,504* |$\quad$| 2010 –7,352,549 |$\quad$| 2012 –11,401,986 –7,268,610 |$\quad$| 2013 –26,568,629** –33,487,032 |$\quad$| 2014 –30,874,367*** –45,329,419* Adjusted |$R^{2}$| 0.24 0.18 The table presents the coefficient estimates from regressing trading profits and volume on surprises and year dummies. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position five seconds to 1 minute after the event. All strategies use an entry window of 0.5 second before to 2 seconds after announcements. Panel A reports the results for profits per event in dollars, panel B reports the results for percentage profits per event, and panel C reports the results for trading volume per event in dollars. Trading volume in futures is measured as # of contracts |$\times$| Price |$\times$| 50. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Events are allowed to have different responses to surprises at different stages of the business cycle (Low, Medium, and High). Dummy for year 2011 is suppressed. Event fixed effects are included in the regression, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The evidence in Table 6 indicates that profits do increase with the magnitude of the announcement surprise. For example, when unwinding the position one to five minutes after the announcement, a one-standard-deviation increase in surprise (during the Medium state) leads to about$\$$40,000 in higher SPY profits and \$$125,000 in higher ES profits. There is also evidence that the effect of surprises varies with the state of the economy. More importantly, we find no evidence that profits increase over time, and we find some evidence that profits and trading volume decrease after 2011. For example, in the case of SPY with an exit time of 2 to 5 seconds, the coefficients on the year dummies point to a decline in profits of $\$$12,252 in 2013 and \$$9,908 in 2014.19 The dollar trading volume in SPY (ES) also significantly declines by roughly$\$$27 (⁠ \$$33) million in 2013 and $\$$31 (⁠ \$$45) million in 2014 after we control for announcement surprises. The evidence in Table 6 helps mitigate the concern that a decline in surprises over time explains the decline in profits and trading volume.20 The decline in trading volume suggests that over time, lower amounts of trading is required for prices to react to macro announcements. This could occur if over time the liquidity-supplying LLTs change their quotes more in response to the announcements and less in response to order flow. We address this in the next section. 4.2 Effect of quote intensity on profits If observed profits are low due to the presence of quickly reacting liquidity providers, we would expect to see a relation between profits and quote intensity. Specifically, if quotes are slow to update and become stale in light of new information, we would expect greater profit opportunities. On the other hand, rapid quote changes alone could be sufficient to incorporate new information with trading being less profitable. We explore this relation formally by regressing profits on measures of quote intensity. Quoting and trading are positively correlated, and both generally signal a liquid market that could improve profits. By scaling quote intensity by trading intensity, we focus on the relative ability of liquidity providers to react to information. Our variable of interest is the ratio of quotes to trades (QT ratio), measured during the two-second entry window. We also include the ratio of quotes to trades measured during a benchmark period five minutes to five seconds before the event to control for possible time of day effects or longer-term trends. All variables are standardized to facilitate interpretation. Table 7 follows the methodology in Table 6 and allows the effect of announcement surprises to vary with levels of the business cycle. Panel B allows the effect of macro surprises on market conditions to vary with market uncertainty. Specifically, we include the level of the VIX (the market’s expectation of 30-day market volatility implied from S&P 500 index options) as well as an interaction term between announcement surprises and VIX. Both announcement and time fixed effects are included. Standard errors are clustered by month. Table 7 Trading profits around macroeconomic news and measures of quote intensity S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 The table presents the coefficient estimates from regressing trading profits on quote and trading activity around macroeconomic news announcements. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. Trades and quotes are computed from 5 minutes to 5 seconds before the announcement (denoted by Pre-Ann.) and from 0 to 2 seconds after announcements (denoted by Post-Ann). The quote/trade ratio is the number of quote changes over the number of trades. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. All strategies use an entry window of 0.5 seconds before to 2 seconds after announcements. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Panel A allows for events to have different responses to surprises at different stages of business cycle (Low, Medium, and High) and panel B allows for different levels of the VIX. Event and year fixed effects are included in the regression and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. Table 7 Trading profits around macroeconomic news and measures of quote intensity S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 S&P 500 ETF (SPY) S&P 500 E-mini futures Coefficients 2s to 5s 5s to 1m 1m to 5m 2s to 5s 5s to 1m 1m to 5m Panel A: Quote intensity, stages of business cycle Surprise (Low) 5,181** 3,015 7,533 Surprise (Medium) 11,402*** 24,188*** 39,764*** 48,795*** 77,569*** 115,923*** Surprise (High) 5,809*** 10,735*** 14,630*** 14,552* 16,786 32,070 Pre-Ann Quote/Trade –357 804 –687 2,782 –7,396 –17,027 Post-Ann Quote/Trade –2,272*** –5,170*** –5,810** –10,332*** –19,057*** –28,474** Adjusted |$R^{\mathrm{2}}$| 0.12 0.11 0.12 0.24 0.14 0.14 Panel B: Quote intensity, adjusting for VIX Surprise 7,706*** 15,015*** 27,621*** –62,651*** –152,471*** –235,478*** VIX 423** 631* 1,161** –2,810 –3,605 –8,191 VIX * Surprise 2 –17 –147 5,934*** 12,338*** 18,949*** Pre-Ann Quote/Trade 702 3,254 3,203 1,926 –8,644 –18,871 Post-Ann Quote/Trade –2,235*** –4,961** –5,487** –10,421*** –19,196*** –28,734** Adjusted |$R^{\mathrm{2}}$| 0.13 0.10 0.11 0.26 0.17 0.17 The table presents the coefficient estimates from regressing trading profits on quote and trading activity around macroeconomic news announcements. Surprise is the absolute value of the standardized announcement surprise, with the standard deviation of surprise computed using time series of surprises for each event. Trades and quotes are computed from 5 minutes to 5 seconds before the announcement (denoted by Pre-Ann.) and from 0 to 2 seconds after announcements (denoted by Post-Ann). The quote/trade ratio is the number of quote changes over the number of trades. The three different models represent different exit times for the trading strategy. For example, 5s to 1m indicates unwinding the position 5 seconds to 1 minute after the event. All strategies use an entry window of 0.5 seconds before to 2 seconds after announcements. The S&P 500 ETF (SPY) sample covers 2008–14, and the E-mini futures sample is from July 2011 to December 2014. Panel A allows for events to have different responses to surprises at different stages of business cycle (Low, Medium, and High) and panel B allows for different levels of the VIX. Event and year fixed effects are included in the regression and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The evidence in Table 7 indicates that high post-announcement QT ratios lead uniformly to lower profits. This is consistent with a more efficient response by liquidity providers who quickly move quotes toward the equilibrium price. The relation is significant for both SPY and ES. For ES in particular, a one-standard-deviation increase in the QT ratio reduces profits by more than half of the average profits in Table 4 for the three different exit strategies. The results in panel B are similar when the impact of the surprise is allowed to vary with the level of the VIX. Profits decrease with the post-announcement QT ratio, but not with the pre-announcement ratio. The findings suggest that active liquidity providers respond quickly to new information, which reduces profit opportunities for liquidity-demanding LLTs. The evidence is consistent with Brogaard, Hagstromer, et al. (2015), who argue that increasing the speed of market-making increases market liquidity through reduced adverse selection. Figure 5 provides further evidence of faster reaction among liquidity providers in the two-second period after the announcements. The figure plots quoted depth, average trade size, and the QT ratio by year. Depth is measured following each quote change during the two-second period after the announcement as the average of shares (for SPY) or the number of contracts (for ES) offered for trade at the best bid and offer prices. Trade size is the average trade size in shares (number of contracts) for the SPY (ES) traded during the two-second period after the announcement. The measures are first computed for each event, then averaged for each announcement type (e.g., Nonfarm Payroll or Consumer Sentiment) each year and finally averaged across events each year. Consistent with reduced latency among liquidity providers, Figure 5 shows that the QT ratio has generally increased over time, while quoted depths and trade sizes have declined.21 Figure 5 View largeDownload slide Trend in quotes-to-trades ratio, quote depth, and trade size The figure plots the trend in average quotes-to-trades ratio, quoted depth, and trade size around each macroeconomic announcement. QT ratio is the ratio of number of quotes to number of trades in a given period. Quoted depth is the average of number of shares at best bid price and number of shares at best ask price. Trade size is the average volume per trade (for futures, it is the number of contracts per trade). Reported are the average values for the period beginning with the announcement and ending 2 seconds later. The numbers are averages across events for the year. Figure 5 View largeDownload slide Trend in quotes-to-trades ratio, quote depth, and trade size The figure plots the trend in average quotes-to-trades ratio, quoted depth, and trade size around each macroeconomic announcement. QT ratio is the ratio of number of quotes to number of trades in a given period. Quoted depth is the average of number of shares at best bid price and number of shares at best ask price. Trade size is the average volume per trade (for futures, it is the number of contracts per trade). Reported are the average values for the period beginning with the announcement and ending 2 seconds later. The numbers are averages across events for the year. Figure 6 plots the trend over time in the speed of market response to macro news. Our first measure of response speed is the fraction of market reaction in the first two seconds after a macroeconomic release that occurs in the first 100 milliseconds, |${S1=\thinspace r\left( t,t+0.1 \right)} \mathord{\left/ {\vphantom {{S1=\thinspace r\left( t,t+0.1 \right)} {r\left( t,t+2 \right)}}} \right. } {r\left( t,t+2 \right)},\thinspace $|where |$r\left( t,t+0.1 \right)$| is the return in the first 100 milliseconds after the release and |$\mathrm{r}\left( t+2 \right)\thinspace $|is the return in the first 2 seconds after the release. S1 is unbounded and less intuitive when the numerator and denominator have conflicting signs. Therefore, similar to Beschwitz, Keim, and Massa (2015), we also calculate the ratio of the absolute return in the first 100 milliseconds after the release to the sum of the absolute return in the first 100 milliseconds and the absolute return in the subsequent 1.9 seconds, |${S2=\thinspace \vert r\left( t,t+0.1 \right)\vert } \mathord{\left/ {\vphantom {{S2=\thinspace \vert r\left( t,t+0.1 \right)\vert } {\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right| \right).}}} \right. } {\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right| \right).}$||$S2\thinspace$| is bounded below by 0 and above by 1. Figure 6 View largeDownload slide Trend in the speed of market reaction The figure plots the trend in speed of market reaction over time. In panel A, the speed of market reaction (S1) is measured as the fraction of the 2-second price response that occurs in first 100 milliseconds after release, |$S1 = r\left( t,t+0.1 \right)/r(t,t+2)$|⁠, where |$r(t,t+0.1)$| is the return in the first 100 milliseconds after the release for the S&P 500 ETF (solid line) or S&P 500 E-mini futures (dotted line) and |$r\left( t,t+2 \right)$| is the return in the first 2 seconds after the release. In panel B, the speed of reaction (S2) is expressed as the ratio of absolute return in first 100 milliseconds after release to the sum of absolute return in first 100 milliseconds and the absolute return in the subsequent 1.9 seconds, |${S2= \vert r\left( t,t+0.1 \right)\vert }/{\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right|\right).}$| Each speed measurement is computed from midquotes each event day and averaged across the event type for a given year. The plot shows averages across events each year. Figure 6 View largeDownload slide Trend in the speed of market reaction The figure plots the trend in speed of market reaction over time. In panel A, the speed of market reaction (S1) is measured as the fraction of the 2-second price response that occurs in first 100 milliseconds after release, |$S1 = r\left( t,t+0.1 \right)/r(t,t+2)$|⁠, where |$r(t,t+0.1)$| is the return in the first 100 milliseconds after the release for the S&P 500 ETF (solid line) or S&P 500 E-mini futures (dotted line) and |$r\left( t,t+2 \right)$| is the return in the first 2 seconds after the release. In panel B, the speed of reaction (S2) is expressed as the ratio of absolute return in first 100 milliseconds after release to the sum of absolute return in first 100 milliseconds and the absolute return in the subsequent 1.9 seconds, |${S2= \vert r\left( t,t+0.1 \right)\vert }/{\left( \left| r\left( t,t+0.1 \right) \right|+\left| r\left( t+0.1,t+2 \right) \right|\right).}$| Each speed measurement is computed from midquotes each event day and averaged across the event type for a given year. The plot shows averages across events each year. Higher values of the response speed measures imply that the reaction to the macroeconomic announcement is concentrated in the first few milliseconds of release. Both under- and overreaction in the first few 100 milliseconds result in lower values of the measures, as reversals after the first 100 milliseconds result in negative values for S1 and larger denominators for S2. Figure 6 documents an increase in the speed of trading over time using both measures for SPY as well as ES. The increased speed of response is consistent with quicker reactions among liquidity-demanding LLTs and faster response from liquidity-supplying LLTs. 4.3 Impact of early access to macroeconomic news In 2007 Reuters began compensating the University of Michigan for the exclusive right to distribute their Consumer Sentiment survey. Reuters created a two-tiered access system for their customers: standard clients would have access to the information at 9:55 a.m. (five minutes before wide distribution), and premium subscribers could access the information in machine-readable form an additional two seconds early at 9:54:58 a.m.22 Although Reuters advertised its early access arrangement to LLTs, the practice was not widely known among other market participants until a former employee filed a lawsuit against the company suggesting it was illegal. In July 2013, Reuters agreed to end the practice at the request of the New York Attorney General.23 In the previous subsection we found evidence that the decline in the profits associated with liquidity-demanding LLTs may be related to the quick updating of quotes by liquidity-supplying LLTs. The early access to the Consumer Sentiment news release provides us with a natural experiment to test whether liquidity-demanding LLTs are able to profit from slow traders who may be unaware of their informational disadvantage. The timing of the suspension of early access is exogenous, and we use a difference-in-difference approach to control for changes in trading activity before and after the suspension of the practice. We focus on the sample period near the change, January 2013–June 2013 for the early access period and July 2013–December 2013 for the no-early-access period. During the early access period, ES had a volume per second of$\$$552 million in the first quarter-second following Consumer Sentiment information, compared with an average of \$$296 million following the other announcements. After ending the early access practice, the volume per second drops to just $\$$44 million in the first quarter-second, which suggests a huge effect due to the change. However, average volume in all other announcements also falls considerably to \$$37 million after July 2013, which highlights the importance of using a difference-in-difference approach. Table 8 reports the difference-in-difference estimates for trading volume for the first quarter-second (e.g., [(44 – 552) – (37 – 296)] |$=$| –$\$$249 million), as well as for other time intervals. Table 8 Effect of advanced access to consumer sentiment information on market activity and profits Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume \$$M Number of trades Number of quotes Value $\$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |-443<sup>***</sup>| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume \$$M Number of trades Number of quotes Value$\$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |-443<sup>***</sup>| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s \$$5,003 $\$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s \$$5,003$\$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 The table compares market activity and trading profits for Consumer Sentiment announcements relative to other macroeconomic news. We measure the incremental effect of Consumer Sentiment during the period in which Thomson Reuters sold two-second early access to Consumer Sentiment information, and we compare this difference to the analogous measure calculated after Reuters ended the practice in July 2013. The difference-in-difference estimates below are the post-advanced-feed period difference less the advanced-feed period difference. The advanced-feed sample is from January 2013–June 2013 and post-advanced-feed sample is from July 2013–December 2013. Panel A reports the estimates for stock market activity in the S&P 500 ETF (SPY) and the S&P 500 E-mini futures, and panel B reports the estimates for aggregate per event dollar profits. Number of trades and quotes are per second, and dollar volume and notional contract value are given in \$$ millions per second. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. Table 8 Effect of advanced access to consumer sentiment information on market activity and profits Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume $\$$M Number of trades Number of quotes Value \$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |$-443<sup>***</sup>$| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel A: Stock market activity S&P 500 ETF (SPY) S&P 500 E-mini futures Time Volume$\$$M Number of trades Number of quotes Value \$$M Number of trades Number of quotes –5m to -5s 0 3 –1 1 3 7 –5s to 0 0 1 39 –1 –8 –5 0.25s 4 –273 –1,356 –249 –720 |$-443<sup>***</sup>$| 0.5s 2 –14 303 8 –3 –120 1s 35 316 699 46 144 29 2s 8 91 173 39 113 52 3s 2 47 426 15 20 39 3s to 5m 1 15 97 1 4 16 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s $\$$5,003 \$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 Panel B: Trading Profits Exit Time S&P 500 ETF (SPY) S&P 500 E-mini futures 2s–5s$\$$5,003 \$$9,589 5s–1m –2,364 3,574 1m–5m –85 –43,164 The table compares market activity and trading profits for Consumer Sentiment announcements relative to other macroeconomic news. We measure the incremental effect of Consumer Sentiment during the period in which Thomson Reuters sold two-second early access to Consumer Sentiment information, and we compare this difference to the analogous measure calculated after Reuters ended the practice in July 2013. The difference-in-difference estimates below are the post-advanced-feed period difference less the advanced-feed period difference. The advanced-feed sample is from January 2013–June 2013 and post-advanced-feed sample is from July 2013–December 2013. Panel A reports the estimates for stock market activity in the S&P 500 ETF (SPY) and the S&P 500 E-mini futures, and panel B reports the estimates for aggregate per event dollar profits. Number of trades and quotes are per second, and dollar volume and notional contract value are given in \millions per second. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. There is modest evidence of a shift in trades and quotes from the first quarter-second to later in the first couple of seconds for Consumer Sentiment relative to the other announcements. However, the shift in quoting intensity does not translate into a significant change in profits. The incremental change in trading profit after Reuters ended early access is statistically insignificant. Relative to other macro announcements, early access to Consumer Sentiment had a modest impact on trading or profits. Overall, the practice of tiered release of information appears to have had little incremental impact on LLT profits or more generally on the process by which information is incorporated into prices. Whether information is released exclusively to algorithmic traders or distributed more broadly, the marginal market participant in the first couple of seconds following the release of machine-readable news is very likely to be a computer. The evidence suggests that regulations that constrain data-gathering firms to release information to clients at a single time may be unnecessary, although requiring transparency among information distributors regarding when information is available to various client groups would likely help improve faith in financial markets. In general, the practice of selling early access to market news is consistent with profit-seeking behavior by information providers. With a two-second head start, it would be possible for the slowest LLT to trade on new information more quickly than the fastest LLT. Therefore, the only way for LLTs to ensure that their costly investment in trade speed is not undercut is to invest in early access to information. In this way, information providers “force” LLTs to pay for early access. This type of profit-seeking behavior also applies to exchange access (co-location) fees, which puts a downward pressure on LLT profits. 4.4 Trend in price discovery If liquidity providers are increasingly able to react to new public information, we would expect to see a reduction over time in the information contained in the post-announcement order flow. We test this conjecture using the state space model approach of Brogaard, Hendershott, and Riordan (2014). They explore a sample of HFT trades and find that the liquidity-demanding trades facilitate price discovery by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors. In our setting, we assume that trades executed within the first two seconds following macro news releases are initiated by liquidity-demanding LLTs, and we examine the impact of their order imbalances on the permanent price changes. For each event day, we sample the midquote price at the beginning of each 100-millisecond interval from two minutes before to two minutes after an event. We then estimate an Unobserved Component Model to extract the change in permanent and temporary price components. In particular, following Brogaard, Hendershott, and Riordan (2014) and Menkveld, Koopman, and Lucas (2007), the Observation Equation (1) and State Equation (2) are described as follows: \begin{align} p_{t} & = m_{t} + s_{t}\\ \end{align} (1) \begin{align} m_{t} & = m_{t-1} + w_{t}, \end{align} (2) where |p_{t}| refers to the log of midquote at the end of each tenth of a second, |m_{t}| is the unobserved true or efficient price, |w_{t}| is the permanent component, and |s_{t}| is the transitory component. In the first stage, we estimate the two components for each event day. In the second stage, we regress the change in permanent component (⁠|w_{t})| and the temporary component (⁠|s_{t})| on the order imbalance (OIB) during that 100-millisecond interval, in the first two seconds after the event, as follows: \begin{align} w_{t} & = c + \alpha \textit{ OIB}_{t} + v_{t}\\ \end{align} (3) \begin{align} s_{t} & = k + \mu s_{t-1} + \beta OIB_{t} + u_{t}. \end{align} (4) We estimate the Unobserved Component Model in Equations (1) and (2) and the regressions in Equations (3) and (4) separately for each announcement24 and then average |\alpha | and |\beta | coefficients across announcements each year and calculate the corresponding standard errors, which are clustered by month. The results are presented in Table 9. The coefficient estimates of |\alpha | and |\beta | are presented over the periods –120 to –60 seconds, 0 to 2 seconds, and 60 to 120 seconds, with time 0 being the announcement. The table reports statistical significance for each coefficient estimate using one, two, or three stars to denote significance at the 0.1, 0.05, and 0.01 levels. We also test whether parameters estimated during the zero- to two-second interval are statistically different from estimates from the periods before and after. We display significance for these tests at the 5% level with bold font (for the periods of –120 to –60 or 60 to 120 seconds). Table 9 Permanent and temporary effects of order imbalance on prices around macroeconomic news Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** This table presents the results of state space model estimation. The log midquote price |p_{t}| is modeled to have a permanent component |m_{t}| and a transitory component |s_{t}|⁠. The permanent component |m_{t}| is modeled as a random walk, and the transitory component is modeled as a stationary process as follows: \begin{align*} P_{t}&= m_{t} + s_{t}\\ m_{t}&= m_{t-1} + w_{t}\\ w_{t}&= c + \alpha OIB_{t} + v_{t}\\s_{t}&= k + \mu s_{t-1} + \beta OIB_{t} + u_{t} \end{align*} The two components for each event day are estimated using an unobserved component model with log of midquotes observed every 100 milliseconds in the interval from 2 minutes before to 2 minutes after the event. Then the components in the following three intervals, 120 seconds to 60 seconds before the announcement (–120s to –60s), the first two seconds after the event (0 to 2s), and 60 seconds to 120 seconds after the announcement (60s to 120s), are regressed on the order imbalance (OIB|_{t})| during the interval. The coefficient is the change to the corresponding component of price in basis points for unit change in order imbalance. The reported results are the time-series average across events of the estimates, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The S&P 500 ETF (SPY) sample in panel A covers 2008–14, and the E-mini futures sample in panel B covers July 2011–December 2014. Numbers in bold indicate that the mean for corresponding year is statistically different from the mean during the interval 0 to 2 seconds after announcement at the 5% level. Table 9 Permanent and temporary effects of order imbalance on prices around macroeconomic news Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** Permanent impact of order flow (⁠|\alpha )| Temporary impact of order flow (⁠|\beta )| Year –120s to –60s 0s to 2s 60s to 120s –120s to –60s 0s to 2s 60s to 120s Panel A: S&P 500 ETF (SPY) 2008 –0.040 –0.189* –0.023 –0.0003* –0.0004* –0.0002 2009 0.017** 0.007 0.037*** –0.0003 –0.0028* –0.0003* 2010 0.038*** 0.283*** 0.065*** –0.0005** –0.0011 –0.0006** 2011 0.050*** 0.668*** 0.084*** –0.0021*** 0.0089 –0.0017** 2012 0.039*** 0.587*** 0.067*** –0.0019*** 0.0098*** –0.0020*** 2013 0.020*** 0.229** 0.047*** –0.0025*** 0.0134 –0.0026*** 2014 0.021*** –0.027 0.051*** –0.0012*** 0.0039 –0.0013*** 2008–14 0.021*** 0.224*** 0.047 –0.0012*** 0.0045** –0.0012*** Panel B: S&P 500 E-mini futures 2011 0.014* 1.064*** 0.056** –0.032*** 0.070 –0.029*** 2012 –0.003 0.643*** 0.008*** –0.030*** 0.039*** –0.032*** 2013 0.003 0.220*** –0.002 –0.015*** 0.053 –0.025*** 2014 –0.010*** 0.01 –0.013*** –0.021*** –0.054* –0.030*** 2011–14 –0.001 0.408*** 0.006 –0.023*** 0.021 –0.029*** This table presents the results of state space model estimation. The log midquote price |p_{t}| is modeled to have a permanent component |m_{t}| and a transitory component |s_{t}|⁠. The permanent component |m_{t}| is modeled as a random walk, and the transitory component is modeled as a stationary process as follows: \begin{align*} P_{t}&= m_{t} + s_{t}\\ m_{t}&= m_{t-1} + w_{t}\\ w_{t}&= c + \alpha OIB_{t} + v_{t}\\s_{t}&= k + \mu s_{t-1} + \beta OIB_{t} + u_{t} \end{align*} The two components for each event day are estimated using an unobserved component model with log of midquotes observed every 100 milliseconds in the interval from 2 minutes before to 2 minutes after the event. Then the components in the following three intervals, 120 seconds to 60 seconds before the announcement (–120s to –60s), the first two seconds after the event (0 to 2s), and 60 seconds to 120 seconds after the announcement (60s to 120s), are regressed on the order imbalance (OIB|_{t})| during the interval. The coefficient is the change to the corresponding component of price in basis points for unit change in order imbalance. The reported results are the time-series average across events of the estimates, and standard errors are clustered by month. Statistical significance is denoted by *, **, and *** for significance at the 10%, 5%, and 1% levels. The S&P 500 ETF (SPY) sample in panel A covers 2008–14, and the E-mini futures sample in panel B covers July 2011–December 2014. Numbers in bold indicate that the mean for corresponding year is statistically different from the mean during the interval 0 to 2 seconds after announcement at the 5% level. Over the whole sample, we see that the post-announcement SPY order imbalance (labeled 0 to 2 seconds) positively predicts movement in the permanent price component, consistent with Brogaard, Hendershott, and Riordan (2014). The coefficient on the transitory component is orders of magnitude lower. For the 2008–14 period, the impact of order flow on the permanent component is a statistically significant 0.224 basis points per unit of OIB. For the temporary component the impact is 0.005 basis points per unit of OIB. In the case of ES, over the 2011–14 sample period, the impact of order flow on the permanent component is 0.408 basis points per unit of OIB, and on the temporary component it is a statistically insignificant 0.02 basis points. While the impact of OIB is positive for the temporary component in the case of SPY, it is orders of magnitude smaller than that for the permanent component. The impact of order flow on the permanent price movements declines in recent years. The coefficient |\alpha |⁠, which measures the impact of liquidity-demanding LLTs on the permanent component of price changes, is the highest in 2011 for both SPY and ES. For SPY, |\alpha | is 0.668 in 2011 and 0.587 in 2012 but declines to 0.229 in 2013 and –0.027 in 2014. In the case of ES, |\alpha | is 1.06 in 2011 but declines to 0.64 in 2012, 0.22 in 2013, and 0.01 in 2014. In both the instruments, the difference in |\alpha | between 2011 and either 2013 or 2014 is statistically significant.25 The decrease in the informativeness of LLT order flow over time is consistent with the hypothesis that prices respond to news with little trading, either because liquidity providers also have access to the announcement information or they have become increasingly adept at quickly reacting to information in the order flow within the first two seconds after the announcement. Table 9 shows that in 2014, post-announcement order flow is not related to the permanent component of prices. The evidence that order flow no longer contains information following macroeconomic announcements is consistent with liquidity-supplying LLTs subscribing to news in digital form and adjusting prices rather than reacting to order flow. This is consistent with Lyle and Naughton (2016), who note that technological improvements have helped to enhance the monitoring ability of market makers who efficiently update quotes and avoid being picked off on stale quotes. Our evidence would seem to imply that liquidity-supplying LLTs should exit the market around macro announcements. However, Brogaard, Carrion, et al. (forthcoming) show that, on average, HFTs profit from supplying liquidity even during periods with extreme price movements, which may help explain their continuing participation. Also, Jovanovic and Menkveld (2016) argue that LLTs’ ability to quickly change quotes makes them more willing to provide liquidity. Consistent with this view, we find that quotes react more quickly to macro news surprises over time. 4.5 Discussion In the context of macro announcements, we find that the liquidity-supplying LLTs quickly adjust their quotes in the direction of the surprise, as they are becoming increasingly adept at reacting to order flow shocks in recent years, possibly by subscribing to machine-readable news. We do not find evidence consistent with liquidity-demanding LLTs exploiting slow retail traders. However, one concern is that in a world with LLTs, other liquidity providers are driven out and often liquidity is not available when needed, as in the case of the flash crash.26 Does this mean that LLTs should face regulation? Our response is that the rules should not be changed to eliminate the speed advantage of the LLTs for three broad reasons. First, our evidence suggests that market forces are working to reduce the LLTs’ speed advantage, which, in terms of profits, is not large to begin with. Moreover, since prices adjust to information shocks in milliseconds, it is unlikely that the slow individual investors will trade at prices far from the equilibrium price. Second, for a proper welfare comparison, it is important to consider a world with LLTs and a counterfactual world without LLTs. The literature provides no evidence that market quality would be better in a world without LLTs, notwithstanding the flash crash. The only evidence is from the pre-LLT world, when New York Stock Exchange (NYSE) specialists would often call market halts (the equivalent of circuit breakers) when the order imbalances became large. Halts are, of course, extreme illiquidity events. And third, while it is true that a social planner may not choose to spend the vast fortunes on reducing trading latency, one has to be mindful of unintended consequences of introducing regulations that eliminate LLT incentives to develop technologies that increase communication speeds. 5. Conclusion Is LLT simply faster trading? The speed of trading has increased steadily for decades, and it is unclear whether LLT represents a fundamental shift in how markets operate. Yet the introduction of many different trading venues, fragmentation of trading, and the large disparity in the speed of trading between LLTs and other market participants may have fundamentally changed markets in favor of those with resources to expend on latency-decreasing technology. We contribute to the LLT debate by exploring the profitability of fast trading following the release of macroeconomic news. Our evidence suggests that the marginal investor immediately following the release of macroeconomic information is a computer algorithm. Trading intensity in SPY and ES increases 100-fold within five milliseconds following the release of macroeconomic news. The result is a remarkably efficient response to news, with prices responding to announcement surprises within milliseconds. Although LLTs respond swiftly and convincingly to macroeconomic news releases, we find that the trading profits on announcement surprises are far smaller than those suggested in the popular press. The lack of meaningful profits from fast trading does not lend credence to naïve descriptions of how speed advantages translate into excessive rents for LLTs. While much of the existing theoretical literature on LLT relies on rent seeking by monopolistic LLTs who profit at the expense of the slower traders, our empirical results question the validity of speed-based monopolistic access to information. The findings are consistent with the increasingly faster quote updates over time by the liquidity-supplying LLTs. We find no evidence that the controversial practice of selling two-second early access to consumer sentiment information leads to incremental profits, possibly because both the liquidity demanders and suppliers around macroeconomic announcements are LLTs. Trading profits decrease with quote intensity and are lower in recent years. Quoted depths and trade sizes decrease, while the speed of trading has increased over time. There is a reduction in the informativeness of the post-announcement order flow over time, which points to an increasing ability of LLT quotes to respond directly to announcement surprises rather than indirectly through trading. Our results suggest that any potential market failure due to the fast traders’ monopolistic access to information is being addressed through market forces. We thank Ekkehart Boehmer, Jonathan Brogaard, Nandini Gupta, Terry Hendershott, Craig Holden, Vincent van Kervel, Jonghyuk Kim, Andrew Karolyi (the editor), Andrei Kirilenko, Katya Malinova, Albert Menkveld, Ryan Riordan, Michel Robe, Elvira Sojli, an anonymous referee, and seminar participants at the 2017 AFA meetings, the ABFER 2016 meetings, the EFA 2016 meetings, 11th Imperial College Hedge Fund Conference, 12th Annual Central Bank Conference on Microstructure of Financial Markets, the Behavioral Finance and Capital Markets conference at the University of South Australia, Deakin University, Frankfurt School of Finance and Management, Goethe University, Indiana University, Queens University, Stevens Institute of Technology, Tulane University, Vienna University and the SEC for helpful comments. Part of the work on this paper was done when Tarun Chordia was a Professorial Research Fellow at Deakin University. Supplementary data can be found on The Review of Financial Studies web site. Footnotes 1 HFTs are a subset of LLTs, as specifically defined by the Security and Exchange Commission’s (SEC’s) concept release on equity market structure (https://www.sec.gov/rules/concept/2010/34-61358.pdf). In the rest of the paper, we will use the term HFTs only for LLTs that fit the SEC definition. We study the Nasdaq HFT data in Section 2.2. 2 Anecdotal evidence abounds of high and remarkably consistent profits for high-speed trading firms. For example, the IPO prospectus for Virtu Financial noted that it had but one losing trading day over the course of four years. http://www.sec.gov/Archives/edgar/data/1592386/000104746914002070/a2218589zs-1.htm 3 Scholtus, van Dijk, and Frijns (2014) document that LLTs improve market quality following macro news releases. 4 Any errors in the classification of the trade direction will add noise but not bias to the profit estimates. 5 Each ES contract represents a contract size of \50 times the index value in points. For an S&P 500 index value of 2,000, each contract represents a notional value of\$$100,000. 6 Release time exceptions during our sample period are as follows: (i) personal income was usually released at 08:30 a.m., with the exception of December 23, 2014, when it was released at 10:00 a.m.; (ii) 10:00 a.m. was the most common release time for ISM Non-Manufacturing, with the exception of February 5, 2008, when it was released at 08:55 a.m.; (ii) University of Michigan Consumer Sentiment scheduled release time was 09:55 a.m. But when early access was available, it was released to subscribers at 09:54:58 a.m. 7 A specialized industry has sprung up to deliver machine-readable financial information to LLTs in milliseconds. For example, RavenPack is a news analytics firm that provides tradeable information to subscribers with a latency of 300 milliseconds, and Beschwitz, Keim, and Massa (2015) document increases in market response speed following coverage by RavenPack. 8 We thank Joel Hasbrouck for providing code to compute NBBO. See Hasbrouck (2010) for details. Holden and Jacobsen (2014) suggest that with extremely low latencies (as response times accelerate), the NBBO may not exist from the perspective of a trader, as the best quote information from distant exchanges may not be time synchronized. See also Angel (2014). 9 Table 1 uses a 5-minute time window rather than 10 seconds, and it also relies on a continuous measure of announcement surprise rather than grouping surprises into positive and negative categories. We continue to find an insignificant 10-second price response if we use the continuous surprise measure as in Table 1. 10 Section 4.3 analyzes the incremental profitability of trading on early access to Consumer Sentiment in more detail. 11 We find no evidence of timing inaccuracy for ES, although for SPY the half-second return prior to the official release time is a significant 0.6 basis points across announcements (Table 2). 12 Although LLTs have the ability to unwind very quickly, they may hold directional positions over longer periods if profits are available. We explore a variety of exit windows with the goal of obtaining an upper bound on the profits available from fast trading on macro news. 13 In the Online Appendix, Table IA.4, we report profits for each event per month to make the profits across events comparable. This method of computing profits does not affect the relative importance of events considered here. 14 Aggregate dollar profits of \$$19,000 per event in SPY and $\$$50,000 per event in ES are negligible in light of the costs involved in subscribing to real-time access to machine-readable news. For example, AlphaFlash (part of Deutsche Börse Group) charges roughly \$$10,000 per month for machine-readable access to several macroeconomic series (including inflation and employment announcements), plus an additional$\$$1,500 for access to the ISM announcements and \$$1,000 per month for Chicago PMI. Separately, Reuters charged up to $\$$6,000 per month for early access to Consumer Sentiment information. Moreover, these expenses do not include initial setup fees and other monthly product fees or take into account commissions on trading. Thus, it would appear that subscribing to machine-readable news and trading on announcement surprises in SPY and ES would be routinely profitable only for relatively few LLTs with the lowest latencies. 15 We thank the referee for suggesting this analysis. 16 For example, according to State Street Global Advisors (the fund that manages SPY), average daily volume in SPY in 2014 was higher than the combined daily volume of the top 18 holdings in the S&P 500. 17 In unreported results, we find that measuring profits from trades during the first second following announcement releases results in smaller profits in general but produces a similar decline in profits in recent years. 18 For ES, there is no coefficient reported for surprise in the Low State as no low state observations occur during the ES sample period, 2011–14. 19 Table IA.7 in the Online Appendix depicts the decline in profits for the other instruments: QQQ, IWM, and the DJIA component stocks. 20 The results are qualitatively similar when we repeat the exercise with a shorter entry window, –0.5 to 1 second. The dummies for 2013 and 2014 in the SPY 1- to 5-minute exit strategy, for example, are – \$$31K and –$\$$22K, compared with –35K and – \$$24K in the current specification. 20a We also find no evidence that the decline in profits is related to the SEC naked access ban highlighted in Chakrabarty et al. (2016). Specifically, we examine market activity during three-month pre- and post-ban periods. The evidence is presented in the Online Appendix, Table IA.5, and indicates that there is no discernable drop in quoting or trading activity around macroeconomic release times. While the ban may have limited the activity of a subset of LLTs, it does not appear to have had a material effect on the liquid securities we consider, especially if the participating LLTs had broker-dealer licenses. 21 Our quotes-to-trades ratio measure is generally lower than the ratio of order submissions to order executions for the median firm reported in Hasbrouck and Saar (2013). While their measure is based on all displayed order messages for a particular stock, our measure uses only quote changes at the top of the order book. 22 Baer and Patterson (2014) notes that the New York Attorney General’s office sent subpoenas to more than a half-dozen HFTs, and the brief filed against Reuters describes their premium subscribers as “ultra low-latency,” which is consistent with HFTs being active market participants following macro news. 23 See Hu, Pan, and Wang (2017) for more details. 24 Brogaard, Hendershott, and Riordan (2014) estimate Equations (1)–(4) in one step using a Kalman filter and maximum likelihood. We opt for a two-step approach due to our small estimation samples. Stock and Watson (1989) point out that a two-step approach helps prevent misspecification in Equations (3) and (4) from inducing inconsistency in Equations (1) and (2), but at the cost of potential inefficiency. 25 We also test the informativeness of trades using the Hasbrouck (1991) VAR methodology. We implement the VAR model for midquote changes and trades. Each announcement day, we estimate the VAR in transaction time for all transactions in the first two seconds after the announcement. The Online Appendix, Table IA.6, reports the results. Similar to the state space results, the informativeness of trades decreases over time since 2011. 26 Kirilenko et al. (2017) show that while the HFTs did not cause the flash crash, they exacerbated the decline in prices by becoming liquidity demanders themselves. References Angel, J. 2014 . When finance meets physics: The impact of the speed of light on financial markets and their regulation. Financial Review 49 : 271 – 81 . 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### Journal

The Review of Financial StudiesOxford University Press

Published: Dec 1, 2018