Financial Advice and Bank Profits
Daniel, Hoechle,;Stefan, Ruenzi,;Nic, Schaub,;Markus, Schmid,
2018-11-01 00:00:00
Abstract We use a unique data set from a large retail bank containing internal managerial accounting data on revenues and costs per client to analyze how banks and their financial advisors generate profits with customers. We find that advised transactions are associated with higher profits than independently executed trades of the same client. The bank’s own mutual funds and structured products are most profitable for the bank, and profits increase with trade size. We show that advisors recommend exactly those transactions. Furthermore, we find that advised clients achieve a worse performance than independent clients, suggesting that advisors put their employer’s interest first. Received April 21, 2016; editorial decision January 24, 2018 by Editor Itay Goldstein. 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. In this paper, we examine the role of financial advice in the profit generation process of banks. While previous studies either focus on determinants of the profitability of the whole banking sector or on profit drivers at the individual bank level, little is known about factors affecting bank profitability at the customer level. In this study, we use a unique data set containing internal managerial accounting data from a large Swiss retail bank to shed light on the profit generation mechanism of banks and the role of financial advisors in this context. To the best of our knowledge, ours is the first study to use detailed client-level data on revenues, expenses, and eventually profits to investigate how financial advisors generate profits with bank clients. Financial advisors are subject to a conflict of interest: they are supposed to help clients execute the most suitable transactions and at the same time they are expected to maximize profits for their employer. Specifically, on the one hand, advisors may strive to identify and recommend suitable trades because of professional concerns about clients’ well-being. Additionally, advisors typically have a fiduciary duty to their clients and might be concerned about legal liability and reputational loss if they give advice that is not optimal from the clients’ point of view.1 On the other hand, advisors may aim for bank profit maximization out of a sense of duty to their employer. To earn money for the bank may also be motivated by career concerns. Moreover, as the bonus of advisors typically depends on the commission and fee income they generate with clients, they also have direct monetary incentives to maximize bank profits. Given these conflicting incentives, it is an empirical question whether financial advisors recommend the most suitable transactions from the clients’ point of view or promote the most profitable trades from the bank’s point of view.2 Our data set provides information on about 20,000 clients, their nearly 500 advisors, the financial holdings of these clients, and more than 240,000 trades executed by them between January 2002 and June 2005. Most importantly, the data set contains quarterly managerial accounting data on revenues, costs, and profits generated from each individual client. This feature of our data enables us to study drivers of bank profitability at the client level. Moreover, our data include information on the type of financial advice followed by clients, allowing us to examine the role of financial advisors in the bank’s profit generation process using two distinct settings: when clients sometimes rely on advice but also conduct trades independently and when clients completely delegate portfolio management to advisors. Optional financial advice is provided free of any direct costs for clients, while clients pay a semiannual fee when delegating account management to the bank. In a within-person analysis using client-advisor fixed effects, we are able to compare bank profits related to advised and independent trades of the same client-advisor pair and we can investigate the change in bank profits when a client decides to switch from a self-managed to a bank-managed account or vice versa. We start our analysis by investigating the different profit centers related to the bank’s retail client business. Our results show that the income from securities transactions and securities holdings accounts for about 70% of quarterly revenues generated with individual clients in our sample. Interest income only contributes about one-fourth to revenues. The core of our analysis is an investigation of the role of financial advice in generating profits from securities transactions and securities holdings. We find that trades executed based on optional financial advice are associated with significantly higher bank profits than independently executed transactions of the same client. If clients rely on optional financial advice, average quarterly bank profits increase by more than 50%. Focusing on the subgroup of clients who fully delegate account management to the bank, we document that the bank’s profit with a customer increases by about 80% after the switch to a delegated account. We then examine the profits associated with different securities transactions and securities holdings of clients to shed light on how exactly financial advisors can influence profitability. We find that profits increase with trade size, as commissions and fees are proportional to the size of transactions, while transaction-related expenses for the bank do not increase to a similar extent with trade size. Moreover, we document that the bank’s own mutual funds and structured products generate the highest transaction- and holding-related profits for the bank. This effect is partly driven by payments from the department that manages the bank’s own funds and from issuers of structured products (so-called “kickbacks”). Thus, if advisors recommend transactions and implement portfolios that maximize expected bank profits, we hypothesize that they should particularly promote larger trades, trades in the bank’s own funds, and trades in structured products. We then investigate directly whether advisors do actually promote trades and implement portfolios in a way that would be expected to maximize bank profits. Consistent with our conjecture, we document that advised trades are indeed significantly larger than independently executed transactions. In addition, we find that advised trades are more likely to be trades in the bank’s own mutual funds and in structured products. Similarly, when clients switch from a self-managed to a bank-managed account, we document a permanent increase in portfolio turnover. We also show that the share of own-bank mutual funds and structured products in clients’ portfolios increases significantly following the delegation of portfolio management to the bank. Next, we explore whether there is meaningful variation of our results in the cross-section of clients, advisors, and client-advisor matches. We find that wealthier clients, more risk-tolerant clients, and older clients who are advised by younger advisors are more likely to carry out advised trades that are particularly profitable to the bank. These investors conduct larger advised trades, and their advised trades are more likely to be trades in the bank’s own mutual funds and in structured products. Our evidence hitherto is consistent with the view that advisors act in the interest of the bank rather than in the interest of their clients. However, financial advice might still be beneficial for clients if it leads to superior after-cost performance. To check this possibility, in the last step, we analyze how financial advice influences clients’ performance. Our evidence shows that a win-win situation does not seem to exist: portfolios of clients who rely on optional financial advice perform significantly worse than portfolios of clients who only trade independently. Focusing on trades in structured products, we document that advised trades tend to underperform independent trades executed by the same client. Finally, we show that switching to a managed account also hurts investors’ portfolio performance. These results are consistent with a number of empirical studies investigating the impact of financial advice on performance (e.g., Hackethal, Haliassos, and Jappelli 2012; Chalmers and Reuter 2015; Foerster et al. 2017; Hoechle et al. 2017). In summary, our findings suggest that advisors put the interest of their employer before the interest of bank customers, potentially neglecting their fiduciary duty to their clients.3 Our study contributes to several strands of the literature. First, it adds to the literature on bank profitability. Among the factors that have been shown to affect bank profits at the industry level are the market interest rate (e.g., Flannery 1981; Ho and Saunders 1981), the market structure (e.g., Heggestad 1977; Rhoades and Rutz 1982), the regulatory environment (e.g., Stiroh and Strahan 2003; Barth, Caprico, and Levine 2004), taxation (e.g., Demirgüç-Kunt and Huizinga 2001; Albertazzi and Gambacorta 2010), and the business cycle (e.g., Albertazzi and Gambacorta 2009). At the individual bank level, previous research shows that banks’ size (e.g., Berger and Mester 1997; Bertay, Demirgüç-Kunt, and Huizinga 2013), capitalization (e.g., Berger 1995; Berger and Bouwman 2013), product mix (e.g., DeYoung and Roland 2001; Stiroh 2004; Demirgüç-Kunt and Huizinga 2010), and corporate and risk governance (e.g., Gorton and Rosen 1995; Aebi, Sabato, and Schmid 2012; Ellul and Yerramilli 2013) matter for profitability. We extend this research by opening up the black box of banks’ profit generation at the client level. Our main contribution is to show that financial advisors play an important role in generating profits for banks by inducing clients to execute transactions that maximize commission and fee income. Second, we contribute to the literature on financial advice. Several theoretical studies shed light on the conflict of interest of financial advisors. They document that while payments from product providers to financial advisors can lead to biased advice, competitive pressure may protect customers from exploitation (e.g., Bolton, Freixas, and Shapiro 2007; Stoughton, Wu, and Zechner 2011; Inderst and Ottaviani 2012). However, empirical evidence on whether financial advisors recommend the trades most suitable for the clients or the transactions most profitable to the financial services firms is limited. Bergstresser, Chalmers, and Tufano (2009) find that net flows of broker-sold mutual funds increase with (maximum) front loads and 12b-1 fees. In a related study, Christoffersen, Evans, and Musto (2013) show that sales loads paid to brokers and other revenues shared with brokers have a positive impact on funds’ inflows. Our paper differs from these studies in that our data enable us to focus not only on mutual funds, but to investigate the entire product menu that is available to individual investors and their advisors. Moreover, one requires client-level data on profits (not only revenues) to determine which transactions are most attractive from the advisors’ perspective because financial advisors add an additional layer of commissions and fees and providing advice is also associated with costs. We document that the bank’s own mutual funds and structured products are the most profitable products from the entire product menu available and consistent with this finding we show that advisors recommend exactly those transactions.4 Finally, our study is also related to the literature on structured products. Henderson and Pearson (2011) investigate the overpricing of a popular type of structured products in the U.S. and estimate that it amounts to about 8%, resulting in a negative expected return. They conclude that it is difficult to rationalize purchases of structured products by informed rational investors. Bergstresser (2008) analyzes the performance of structured products in an international sample and documents that structured products underperform common benchmarks, most likely reflecting the premium built into the prices of these products at issuance. Nevertheless, structured products enjoy great popularity among retail investors.5 We are the first to show that structured products generate substantial profits for distributors (and not only for issuers). Moreover, the high profitability of these products induces financial advisors to promote them strongly to their customers, providing an explanation for why these products are so popular among retail investors. Our results stress the importance of the ongoing debate on commissions and fees paid in the financial services industry. The recent financial crisis has motivated several countries to consider new regulations to better protect private investors’ interests. In the United States, based on the 2010 Dodd-Frank Wall Street Reform and Consumer Protection Act, the SEC is currently considering imposing a uniform standard of care for all types of financial advisors, but there are no attempts so far to ban payments from product providers to financial advisors, which we show to affect advisors’ recommendations (see, e.g., Baer and Ackerman 2015). Moreover, in 2014, the European Union published a revised version of its Markets in Financial Instruments Directive (MiFID II), which states that independent financial advisors must transfer all commissions and fees paid by third parties to their clients (see, e.g., Johnson 2014).6 However, these new rules only apply to financial advisors declaring themselves to be independent and leaving the business model of nonindependent advisors largely unchanged. In addition, the ban on payments of product providers to financial advisors does not necessarily affect the bank’s own products and commissions customers pay directly to the bank for transactions in any product, both of which we show to also have a substantial influence on profitability and eventually advisors’ recommendations in our analysis. Moreover, even in the absence of direct monetary incentives, career concerns are likely to provide indirect incentives to increase revenues with clients, as often those advisors get promoted that contribute most to the bank’s performance (see, e.g., Craig and Silver-Greenberg 2012). Thus, regulatory steps taken so far might not be sufficient to align interests of financial advisors and clients. 1. Data and Variables 1.1 Data and sample selection The data set used in this study was provided by a large Swiss retail bank, which we will simply call the bank henceforth. Our bank offers a broad range of financial products and services to its clients such as securities accounts, savings accounts, retirement savings accounts, checking accounts, mortgages, and loans. It operates a network of bank branches throughout Switzerland and several branches abroad. The data cover the time period from January 2002 to June 2005. The typical customers are traditional bank branch clients relying on a strong and long-lasting relationship with their bank. The bank is typically the house bank of its clients and clients usually do not have accounts with other banks. This feature allows for a comprehensive view of the overall financial situation and portfolio structure of the clients examined. The clients in our sample constitute a random sample comprising 90% of the bank’s private clients whose main account is denominated in Swiss Francs and whose wealth at the bank exceeds CHF 75,000 (equivalent to roughly USD 56,000 during our investigation period) at least once prior to December 2003.7 As of December 2003, 42.0% of Swiss residents subject to taxation had a net wealth (including nonfinancial wealth) of more than CHF 50,000 (Swiss Federal Statistical Office 2012). Hence, clients are on average wealthier than the median private person in Switzerland. As our analysis focuses on the profit-generating role of investment recommendations of advisors, we exclude clients who do not trade at all during our investigation period. Our final sample consists of 20,411 clients, assigned to 461 advisors, executing a total of 242,421 trades between January 2002 and June 2005.8 1.2 Optional financial advice and managed accounts When opening an account at our bank, each client is assigned to an advisor who serves as the main contact person for the client. Clients can conduct their financial transactions independently, they can make use of optional financial advice provided by bank employees for free, or they can completely delegate account management to the bank in return for a semiannual fee. Our data set provides information on contacts between clients and their advisors. This feature of our data allows us to identify individual trades based on optional financial advice. For the clients who never delegate account management to the bank, we have information on 60,402 client-advisor contacts. Contacts may include everything from a client receiving a rather impersonal mailing to an in-person meeting between the client and the advisor. 11,004 contacts are explicitly classified as advisory contacts, out of which 41.0% are initiated by the advisor. Clients who never delegate account management to the bank execute a total of 154,846 trades during our sample period. Figure 1 illustrates how these trades are distributed around advisory contacts. Advisory contacts are associated with an increased number of trades. In the figure, the advisory contact takes place on day t = 0 and trades also peak on this day. However, an exceptionally high number of trades also takes place on the days following the advisory contact. Therefore, we classify a trade as an advised trade if it is executed within five trading days after an advisory contact, that is, between t = 0 and t = 4.9 57.0% of all advisory contacts are associated with at least one trade during this period. If a client decides to trade after interacting with the advisor, the client executes 1.6 transactions on average. This results in 10,105 advised transactions in our data set. Thus, 4.2% of all trades in our sample are classified as advised. 38.0% of these advised trades take place after a contact that is classified as advisor initiated.10 Figure 1 View largeDownload slide Number of trades around advisory contacts This figure shows the number of trades around advisory contacts. The contact between the client and the advisor takes place on day $$t = 0$$. Between January 2002 and June 2005, trades in self-managed accounts total 154,846, of which 10,105 transactions take place within the time period from $$t = 0$$ to $$t = 4$$ after an advisory contact. Figure 1 View largeDownload slide Number of trades around advisory contacts This figure shows the number of trades around advisory contacts. The contact between the client and the advisor takes place on day $$t = 0$$. Between January 2002 and June 2005, trades in self-managed accounts total 154,846, of which 10,105 transactions take place within the time period from $$t = 0$$ to $$t = 4$$ after an advisory contact. There are 2,161 advised clients in our data set, meaning that 10.6% of all clients trade on advice at least once during our sample period. Advised clients execute 30,694 independent trades besides the 10,105 advised transactions. Hence, even clients who trade on advice at least once execute most of their transactions independently, highlighting the importance of analyzing the impact of optional financial advice at the trade level and not at the client level. Rather than making use of financial advice only occasionally, clients can also completely delegate account management to the bank. The data set provides information on whether a client has a managed account at least once during our investigation period. There are 1,244 customers with managed accounts in our sample (equivalent to about 6.1% of all clients). Unfortunately, this variable is time-invariant and we do not know when account management is assumed by the bank. However, as we have data on the management fees that are paid semiannually for managed accounts, we can determine when clients switch to and from managed accounts indirectly. There are 371 clients who decide to delegate account management to our bank during our investigation period from January 2002 to June 2005 and 153 clients who switch from a bank-managed account to a self-managed account according to this definition. A large number of trades (87,575, or 36.1% of all trades) are associated with clients who delegate account management to the bank at least once. About one-sixth of these trades are executed while the accounts are self-managed and the remaining ones are carried out while the bank manages the accounts. Finally, there are 17,006 independent clients neither relying on optional financial advice nor delegating account management during our sample period. They execute a total of 114,047 independent transactions. While we think that our data set has many advantages, a natural limitation of our study is that all information is from one bank only. However, there are no obvious reasons to expect the bank to be different from other financial institutions in other countries in any fundamental way and we argue that the bank, its customers, and its advisors are representative: First, according to a recent survey by BlackRock (2013), individual investors’ reliance on advice provided by bank employees fluctuates roughly between 20% and 40% across a broad range of industrialized countries (Belgium, Canada, France, Germany, Italy, Netherlands, Switzerland, the United Kingdom, and the United States). Hence, financial advice is of similar importance across developed countries. Second, even though commission-based remuneration models have come under scrutiny more recently, according to a survey among purchasers of retail financial services in Europe, still less than 10% of clients pay directly for financial advice they receive (Chater, Inderst, and Huck 2010). Similar numbers apply for North America (see, e.g., Kiladze 2013; Zweig 2013). Moreover, even if financial advisors do not have direct financial incentives, career concerns are likely to provide indirect incentives to increase revenues with clients. Therefore, incentives of financial advisors are expected to be similar across different financial institutions. Finally, to analyze whether our clients and their trading behavior deviates from the trading behavior of individual investors in other samples, in unreported tests we replicate a number of studies on individual investors’ behavior that use data from a large U.S. brokerage house, including Barber and Odean (2000, 2001, 2002), Ivkovic, Sialm, and Weisbenner (2008), and Seasholes and Zhu (2010). We find that their results also hold in our data set. Thus, there is no reason to expect investors in our data set to behave differently from investors in other samples. 1.3 Descriptive statistics The data set provides information on quarterly revenues, expenses, and eventually profits generated with each client. This is an important and unique feature of our data, as focusing on revenues without taking into account costs allows only for an incomplete picture of the drivers of bank profitability. Moreover, we have detailed information on clients’ use of financial products, including individual positions in securities accounts, transaction data, savings, retirement savings, checking account holdings, mortgages, and loans. The data set also includes various investor characteristics such as gender, age, place of residence, the length of the bank relationship, and, for some clients in the sample, their education, employment, and a risk tolerance score assigned by the bank. Finally, the data set also includes sociodemographic information on the advisors, including gender, age, and seniority. All information was collected by the bank on the date of the account opening and updated when new information is provided either by clients or by advisors. Appendix A provides detailed descriptions of all variables used throughout the study. Table 1 reports descriptive statistics. Profit characteristics are presented in panel A. The bank generates average total revenues of CHF 718 (equivalent to about USD 540) per client and quarter. The revenue figure contains the securities account fee, the securities transaction income, the fee the client pays if account management is delegated to the bank, other commission and fee income, interest income, and a residual position for other revenues that can be assigned to a client. This compares with average total expenses of CHF 225 per client and quarter. The expense data are not further split up into subcategories. Expenses include all costs that can be attributed to a client such as labor costs of the financial advisor or costs the client generates in another department of the bank. Overall, the average quarterly profit the bank generates with each client over our investigation period from January 2002 to June 2005 amounts to CHF 492. Clients in the most profitable decile of customers generate about 57.4% of all profits. At the lower end of the distribution there are 2,874 (or 14.1% of all) clients generating losses to the bank on average over our investigation period. Table 1 Descriptive statistics Mean 10% Median 90% SD N A. Profit characteristics Avg. profit 492.34 –18.79 180.43 1,161.36 1,259.13 20,411 Avg. revenues 717.73 83.09 330.86 1,587.00 1,437.46 20,411 Avg. expenses 225.39 80.79 148.07 451.14 248.62 20,411 Avg. sec. account fee 83.00 3.71 35.50 193.14 167.62 20,411 Avg. sec. trans. inc. 183.30 0.00 38.71 416.54 587.17 20,411 Avg. mgmt. fee 40.58 0.00 0.00 0.00 305.38 20,411 Avg. other com./fee inc. 178.74 7.64 95.75 410.71 331.27 20,411 Avg. interest inc. 184.23 3.71 41.29 414.21 649.74 20,411 B. Portfolio characteristics Avg. bank wealth 307,187 70,689 165,881 584,769 702,225 20,411 Avg. securities account 234,321 15,664 111,403 496,614 586,646 20,411 Avg. savings account 37,645 0 11,439 100,873 79,025 20,411 Avg. retirement savings 5,225 0 0 13,932 18,422 20,411 Avg. checking account 29,995 –0 8,368 59,423 201,646 20,411 Avg. mortgage 31,645 0 0 40,000 201,989 20,411 Avg. loan 4,118 0 0 0 54,539 20,411 Avg. # trades 0.91 0.07 0.21 2.07 2.16 20,411 Avg. trading volume 25,785 171 5,457 56,793 107,875 20,411 C. Client characteristics Client male (d) 0.568 0.000 1.000 1.000 0.495 20,411 Client age (years) 58.88 37.00 60.00 79.00 15.60 20,406 Education (1–7) 3.74 3.00 3.00 7.00 1.54 4,335 Employed (d) 0.622 0.000 1.000 1.000 0.485 16,360 Retired (d) 0.321 0.000 0.000 1.000 0.467 16,360 Swiss (d) 0.724 0.000 1.000 1.000 0.447 20,411 Risk tolerance (1–3) 1.859 1.000 2.000 3.000 0.668 7,751 Length of rel. (years) 6.62 2.17 7.08 8.75 2.41 20,411 D. Advisor characteristics Advisor male (d) 0.590 0.000 1.000 1.000 0.492 461 Advisor age (years) 34.47 21.00 33.00 51.00 11.16 371 Senior (d) 0.440 0.000 0.000 1.000 0.497 461 Mean 10% Median 90% SD N A. Profit characteristics Avg. profit 492.34 –18.79 180.43 1,161.36 1,259.13 20,411 Avg. revenues 717.73 83.09 330.86 1,587.00 1,437.46 20,411 Avg. expenses 225.39 80.79 148.07 451.14 248.62 20,411 Avg. sec. account fee 83.00 3.71 35.50 193.14 167.62 20,411 Avg. sec. trans. inc. 183.30 0.00 38.71 416.54 587.17 20,411 Avg. mgmt. fee 40.58 0.00 0.00 0.00 305.38 20,411 Avg. other com./fee inc. 178.74 7.64 95.75 410.71 331.27 20,411 Avg. interest inc. 184.23 3.71 41.29 414.21 649.74 20,411 B. Portfolio characteristics Avg. bank wealth 307,187 70,689 165,881 584,769 702,225 20,411 Avg. securities account 234,321 15,664 111,403 496,614 586,646 20,411 Avg. savings account 37,645 0 11,439 100,873 79,025 20,411 Avg. retirement savings 5,225 0 0 13,932 18,422 20,411 Avg. checking account 29,995 –0 8,368 59,423 201,646 20,411 Avg. mortgage 31,645 0 0 40,000 201,989 20,411 Avg. loan 4,118 0 0 0 54,539 20,411 Avg. # trades 0.91 0.07 0.21 2.07 2.16 20,411 Avg. trading volume 25,785 171 5,457 56,793 107,875 20,411 C. Client characteristics Client male (d) 0.568 0.000 1.000 1.000 0.495 20,411 Client age (years) 58.88 37.00 60.00 79.00 15.60 20,406 Education (1–7) 3.74 3.00 3.00 7.00 1.54 4,335 Employed (d) 0.622 0.000 1.000 1.000 0.485 16,360 Retired (d) 0.321 0.000 0.000 1.000 0.467 16,360 Swiss (d) 0.724 0.000 1.000 1.000 0.447 20,411 Risk tolerance (1–3) 1.859 1.000 2.000 3.000 0.668 7,751 Length of rel. (years) 6.62 2.17 7.08 8.75 2.41 20,411 D. Advisor characteristics Advisor male (d) 0.590 0.000 1.000 1.000 0.492 461 Advisor age (years) 34.47 21.00 33.00 51.00 11.16 371 Senior (d) 0.440 0.000 0.000 1.000 0.497 461 This table presents descriptive statistics on profit characteristics (panel A), portfolio characteristics (panel B), client characteristics (panel C), and advisor characteristics (panel D). Profit and portfolio characteristics are denoted in Swiss Francs (except for the variable # trades). For time-varying variables, beginning-of-period values (Client age, Length of rel., Advisor age) or averages over the sample period from January 2002 to June 2005 are reported (all profit and portfolio characteristics). Appendix A provides detailed descriptions of all variables used throughout the study. Table 1 Descriptive statistics Mean 10% Median 90% SD N A. Profit characteristics Avg. profit 492.34 –18.79 180.43 1,161.36 1,259.13 20,411 Avg. revenues 717.73 83.09 330.86 1,587.00 1,437.46 20,411 Avg. expenses 225.39 80.79 148.07 451.14 248.62 20,411 Avg. sec. account fee 83.00 3.71 35.50 193.14 167.62 20,411 Avg. sec. trans. inc. 183.30 0.00 38.71 416.54 587.17 20,411 Avg. mgmt. fee 40.58 0.00 0.00 0.00 305.38 20,411 Avg. other com./fee inc. 178.74 7.64 95.75 410.71 331.27 20,411 Avg. interest inc. 184.23 3.71 41.29 414.21 649.74 20,411 B. Portfolio characteristics Avg. bank wealth 307,187 70,689 165,881 584,769 702,225 20,411 Avg. securities account 234,321 15,664 111,403 496,614 586,646 20,411 Avg. savings account 37,645 0 11,439 100,873 79,025 20,411 Avg. retirement savings 5,225 0 0 13,932 18,422 20,411 Avg. checking account 29,995 –0 8,368 59,423 201,646 20,411 Avg. mortgage 31,645 0 0 40,000 201,989 20,411 Avg. loan 4,118 0 0 0 54,539 20,411 Avg. # trades 0.91 0.07 0.21 2.07 2.16 20,411 Avg. trading volume 25,785 171 5,457 56,793 107,875 20,411 C. Client characteristics Client male (d) 0.568 0.000 1.000 1.000 0.495 20,411 Client age (years) 58.88 37.00 60.00 79.00 15.60 20,406 Education (1–7) 3.74 3.00 3.00 7.00 1.54 4,335 Employed (d) 0.622 0.000 1.000 1.000 0.485 16,360 Retired (d) 0.321 0.000 0.000 1.000 0.467 16,360 Swiss (d) 0.724 0.000 1.000 1.000 0.447 20,411 Risk tolerance (1–3) 1.859 1.000 2.000 3.000 0.668 7,751 Length of rel. (years) 6.62 2.17 7.08 8.75 2.41 20,411 D. Advisor characteristics Advisor male (d) 0.590 0.000 1.000 1.000 0.492 461 Advisor age (years) 34.47 21.00 33.00 51.00 11.16 371 Senior (d) 0.440 0.000 0.000 1.000 0.497 461 Mean 10% Median 90% SD N A. Profit characteristics Avg. profit 492.34 –18.79 180.43 1,161.36 1,259.13 20,411 Avg. revenues 717.73 83.09 330.86 1,587.00 1,437.46 20,411 Avg. expenses 225.39 80.79 148.07 451.14 248.62 20,411 Avg. sec. account fee 83.00 3.71 35.50 193.14 167.62 20,411 Avg. sec. trans. inc. 183.30 0.00 38.71 416.54 587.17 20,411 Avg. mgmt. fee 40.58 0.00 0.00 0.00 305.38 20,411 Avg. other com./fee inc. 178.74 7.64 95.75 410.71 331.27 20,411 Avg. interest inc. 184.23 3.71 41.29 414.21 649.74 20,411 B. Portfolio characteristics Avg. bank wealth 307,187 70,689 165,881 584,769 702,225 20,411 Avg. securities account 234,321 15,664 111,403 496,614 586,646 20,411 Avg. savings account 37,645 0 11,439 100,873 79,025 20,411 Avg. retirement savings 5,225 0 0 13,932 18,422 20,411 Avg. checking account 29,995 –0 8,368 59,423 201,646 20,411 Avg. mortgage 31,645 0 0 40,000 201,989 20,411 Avg. loan 4,118 0 0 0 54,539 20,411 Avg. # trades 0.91 0.07 0.21 2.07 2.16 20,411 Avg. trading volume 25,785 171 5,457 56,793 107,875 20,411 C. Client characteristics Client male (d) 0.568 0.000 1.000 1.000 0.495 20,411 Client age (years) 58.88 37.00 60.00 79.00 15.60 20,406 Education (1–7) 3.74 3.00 3.00 7.00 1.54 4,335 Employed (d) 0.622 0.000 1.000 1.000 0.485 16,360 Retired (d) 0.321 0.000 0.000 1.000 0.467 16,360 Swiss (d) 0.724 0.000 1.000 1.000 0.447 20,411 Risk tolerance (1–3) 1.859 1.000 2.000 3.000 0.668 7,751 Length of rel. (years) 6.62 2.17 7.08 8.75 2.41 20,411 D. Advisor characteristics Advisor male (d) 0.590 0.000 1.000 1.000 0.492 461 Advisor age (years) 34.47 21.00 33.00 51.00 11.16 371 Senior (d) 0.440 0.000 0.000 1.000 0.497 461 This table presents descriptive statistics on profit characteristics (panel A), portfolio characteristics (panel B), client characteristics (panel C), and advisor characteristics (panel D). Profit and portfolio characteristics are denoted in Swiss Francs (except for the variable # trades). For time-varying variables, beginning-of-period values (Client age, Length of rel., Advisor age) or averages over the sample period from January 2002 to June 2005 are reported (all profit and portfolio characteristics). Appendix A provides detailed descriptions of all variables used throughout the study. Securities transaction income (CHF 183), other commission and fee income (CHF 179), and interest income (CHF 184) account for 76.1% of total revenues. Securities transaction income consists mainly of commissions and fees that customers pay directly to our bank when trading securities regardless of whether the product was issued by the bank or by another financial institution, as well as transaction-related payments the bank receives from product providers. Other commission and fee income includes, among other things, recurring payments the bank gets from product providers as long as a client holds a security in the portfolio as well as fees for account keeping, payment transactions, and credit cards. Interest income includes the net income from savings accounts, retirement savings accounts, checking accounts, mortgages, and loans calculated according to the market interest rate method. The market interest rate method assumes that assets and liabilities are refinanced at current market conditions. Securities account fees generate CHF 83 (or 11.6% of total revenues) per client and quarter on average. These are the fees clients have to pay semiannually for their securities accounts. Management fees for accounts managed by our bank are also paid semiannually and amount to CHF 41 (5.7%) on average.11 To smooth the distributions of these semiannual variables, we spread the securities account fee and the management fee over the quarter preceding the payment and the quarter of the actual payment. Finally, other income contributes CHF 48 (6.7%) per client and quarter on average. Figure IA1 in the Internet Appendix presents average quarterly profits (panel A) and average quarterly revenues by profit center (panel B) over time between January 2002 and June 2005. Average quarterly revenues and profits per client are lowest at the trough of the dot-com crisis in the first quarter of 2003 and tend to increase again thereafter. Panel B of Table 1 reports portfolio characteristics. The average client holds CHF 307,187 (equivalent to about USD 230,000) in financial wealth at our bank. Hence, a large part of clients’ financial wealth appears to be represented in our data set and we can reasonably assume that the accounts at our bank typically are the clients’ main accounts rather than “play money” accounts.12 Securities accounts contribute CHF 234,321 (or 76.3% of total bank wealth), savings accounts CHF 37,645 (12.3%), retirement savings accounts CHF 5,225 (1.7%), and checking accounts CHF 29,995 (9.8%). Mortgages and loans are not netted against clients’ financial wealth. The average client has an outstanding mortgage balance of CHF 31,645 and a loan balance of CHF 4,118. On average, clients execute almost one trade per quarter amounting to an average quarterly trading volume of CHF 25,785. Panel C presents various sociodemographic variables on the clients. Males compose 56.8% of the clients in our sample. On average, clients are 58.9 years old as of January 2002. Education is based on the highest education a client received and measured by a count variable ranging from 1 to 7. Appendix A provides the detailed definitions. 76.2% of the clients in our sample completed a vocational education, 16.5% hold a university degree, and the remaining 7.3% are assigned to categories such as “unskilled,” “semiskilled,” “high school degree,” “higher vocational education,” or “technical college degree.” Employed individuals constitute 62.2% of the clients in our sample, 32.1% of the clients are retired, and 5.7% belong to other categories, such as “self-employed,” “housewives,” or “students.” The information on the clients’ education and employment status is only available for 4,335 and 16,360 customers, respectively. The vast majority of clients (72.4%) lives in Switzerland. Upon opening an account with our bank, clients’ risk tolerance is assessed in an in-person meeting with the advisor based on a set of predefined questions. It is measured by a count variable ranging from 1 to 3. The majority of clients report a medium risk tolerance (53.3%), 16.3% of clients are classified as having a high risk tolerance, and the remaining 30.4% report a low risk tolerance. This information is only available for about one-third of the total sample. The average client has been a customer of the bank for 6.6 years as of January 2002. Panel D reports advisor characteristics. About 59% of advisors are male. Advisors are on average 34.5 years old as of January 2002. Moreover, 44.0% of all advisors are senior advisors. 2. Empirical Analysis The unique structure of our data set allows us to perform five sets of novel tests: First, we analyze the determinants of bank profitability at the customer level and the impact of financial advice in this context (Section 2.1). Second, we investigate how trade characteristics and the characteristics of assets held in portfolios affect bank profits (Section 2.2). Third, we analyze whether financial advisors induce trades and implement portfolios which, based on the analysis in Section 2.2, would be expected to maximize bank profits (Section 2.3). In Section 2.4, we examine whether certain client or advisor characteristics or specific client-advisor matches drive our results. Finally, we shed light on whether reliance on advice is not only profitable to banks, but possibly also beneficial for clients, which would resolve the issue of potential conflicts of interest (Section 2.5). 2.1 Financial advice and bank profits To investigate potential drivers of client profitability, we run panel regressions at the client-quarter level with different measures of bank profits as dependent variables. As independent variables we include the percentage of advised trades per quarter and a dummy variable that equals one if a client delegates account management to the bank in the respective quarter, and zero otherwise. As the percentage of advised trades is not defined in quarters without any transactions, we include a dummy variable that equals one for quarters with at least one trade, and zero otherwise.13 Moreover, regressions contain quarterly portfolio characteristics. All portfolio characteristics are denoted in Swiss Francs and are scaled by 1,000. To make sure that our findings are not driven by outliers, we winsorize profit and portfolio characteristics in this and all our following analyses at the 1% level and the 99% level. We also control for client and advisor characteristics. In addition, we include quarter fixed effects to control for unobserved heterogeneity which is constant across clients. As different quarterly observations on one advisor are not independent (within correlation), we use cluster-robust standard errors and treat each advisor as a cluster.14 Table 2 presents the results. In Column 1, the dependent variable is quarterly profits earned by the bank from the respective client. The coefficients on the percentage of advised trades and the managed account dummy variable are both positive and statistically significant, suggesting that optional-advice-driven trading and managed accounts are profitable for the bank. On average, clients execute about one trade per quarter. Thus, the coefficient estimate on the percentage of advised trades indicates that if a client executes this trade on advice rather than independently, this increases quarterly bank profits by about CHF 252 (55.7% of the average quarterly bank profit in the respective sample). Moreover, the coefficient estimate on the managed account dummy indicates that a switch from a self-managed to a bank-managed account is associated with an increase in the bank’s profit from this client of CHF 375 (82.8% of the average quarterly bank profit in the respective sample). Hence, our results document that bank profits increase substantially once the client follows optional financial advice and particularly if a client switches to a bank-managed account. Table 2 Determinants of profits Profit (CHF) Sec. account fee (CHF) Sec. trans. inc. (CHF) Mgmt. fee (CHF) Other com./fee inc. (CHF) Interest inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) (7) (8) (9) Advice characteristics % advised trades 252.016*** 237.301*** 0.956 153.059*** –9.341*** 20.017*** –4.886 –17.248*** (8.32) (10.89) (0.54) (7.83) (–3.32) (3.46) (–1.34) (–3.64) % advisor-initiated 270.796*** (6.98) Managed account (d) 374.920*** 382.703*** 379.074*** –118.806*** –145.605*** 895.724*** 122.069*** 0.584 305.791*** (10.39) (5.37) (5.31) (–9.11) (–2.76) (13.03) (4.67) (0.04) (17.79) Portfolio characteristics Securities account 1.065*** 0.798*** 0.796*** 0.286*** 0.246*** 0.178*** 0.234*** 0.084*** 0.162*** (40.41) (13.75) (13.71) (24.83) (5.59) (6.27) (15.04) (4.54) (13.01) Savings account 0.632*** 0.803*** 0.804*** 0.051*** 0.368*** –0.016 0.002 0.485*** 0.054*** (12.68) (8.49) (8.48) (5.13) (5.31) (–0.35) (0.09) (12.59) (2.79) Retirement savings –0.776*** –2.454*** –2.462*** –0.036 –0.180 –0.358*** –0.132 –1.460*** 0.173** (–7.09) (–8.28) (–8.33) (–1.29) (–0.96) (–2.97) (–1.28) (–9.03) (2.09) Checking account 1.825*** 1.801*** 1.804*** 0.031** 0.740*** 0.180*** 0.081** 0.829*** 0.108*** (14.95) (17.28) (17.24) (2.49) (7.15) (3.01) (2.21) (16.86) (4.94) Mortgage 2.142*** 1.713*** 1.711*** 0.019*** 0.120* –0.047 –0.027 1.398*** 0.142*** (47.59) (10.20) (10.20) (2.74) (1.90) (–1.05) (–1.26) (11.98) (5.59) Loan 1.487*** 0.811*** 0.811*** 0.017 –0.044 0.020 –0.079 0.963*** –0.007 (8.28) (3.40) (3.39) (0.70) (–0.26) (0.18) (–0.99) (6.40) (–0.20) At least one trade (d) 109.077*** 117.998*** 124.656*** 1.915*** 121.321*** –5.529*** 6.569*** –1.994* 74.477*** (8.47) (9.52) (9.54) (5.60) (11.68) (–2.65) (3.76) (–1.70) (40.22) Trading volume 3.980*** 3.431*** 3.446*** 0.013** 4.812*** 0.134*** –0.028 0.160*** 0.882*** (25.25) (21.99) (22.11) (2.59) (23.97) (3.77) (–1.31) (9.89) (31.01) Client characteristics Client male (d) 8.815* (1.90) log(client age) –32.060*** (–3.93) Swiss (d) –86.891*** (–6.61) log(length of rel.) 18.887** (2.32) Advisor characteristics Advisor male (d) 13.394 (1.56) log(advisor age) 16.803 (0.71) Senior (d) 13.087 (1.44) Client-advisor fixed effects No Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.685 0.775 0.774 0.875 0.713 0.635 0.590 0.877 0.691 N 262,835 273,044 273,044 273,044 273,044 273,044 273,044 273,044 273,044 Profit (CHF) Sec. account fee (CHF) Sec. trans. inc. (CHF) Mgmt. fee (CHF) Other com./fee inc. (CHF) Interest inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) (7) (8) (9) Advice characteristics % advised trades 252.016*** 237.301*** 0.956 153.059*** –9.341*** 20.017*** –4.886 –17.248*** (8.32) (10.89) (0.54) (7.83) (–3.32) (3.46) (–1.34) (–3.64) % advisor-initiated 270.796*** (6.98) Managed account (d) 374.920*** 382.703*** 379.074*** –118.806*** –145.605*** 895.724*** 122.069*** 0.584 305.791*** (10.39) (5.37) (5.31) (–9.11) (–2.76) (13.03) (4.67) (0.04) (17.79) Portfolio characteristics Securities account 1.065*** 0.798*** 0.796*** 0.286*** 0.246*** 0.178*** 0.234*** 0.084*** 0.162*** (40.41) (13.75) (13.71) (24.83) (5.59) (6.27) (15.04) (4.54) (13.01) Savings account 0.632*** 0.803*** 0.804*** 0.051*** 0.368*** –0.016 0.002 0.485*** 0.054*** (12.68) (8.49) (8.48) (5.13) (5.31) (–0.35) (0.09) (12.59) (2.79) Retirement savings –0.776*** –2.454*** –2.462*** –0.036 –0.180 –0.358*** –0.132 –1.460*** 0.173** (–7.09) (–8.28) (–8.33) (–1.29) (–0.96) (–2.97) (–1.28) (–9.03) (2.09) Checking account 1.825*** 1.801*** 1.804*** 0.031** 0.740*** 0.180*** 0.081** 0.829*** 0.108*** (14.95) (17.28) (17.24) (2.49) (7.15) (3.01) (2.21) (16.86) (4.94) Mortgage 2.142*** 1.713*** 1.711*** 0.019*** 0.120* –0.047 –0.027 1.398*** 0.142*** (47.59) (10.20) (10.20) (2.74) (1.90) (–1.05) (–1.26) (11.98) (5.59) Loan 1.487*** 0.811*** 0.811*** 0.017 –0.044 0.020 –0.079 0.963*** –0.007 (8.28) (3.40) (3.39) (0.70) (–0.26) (0.18) (–0.99) (6.40) (–0.20) At least one trade (d) 109.077*** 117.998*** 124.656*** 1.915*** 121.321*** –5.529*** 6.569*** –1.994* 74.477*** (8.47) (9.52) (9.54) (5.60) (11.68) (–2.65) (3.76) (–1.70) (40.22) Trading volume 3.980*** 3.431*** 3.446*** 0.013** 4.812*** 0.134*** –0.028 0.160*** 0.882*** (25.25) (21.99) (22.11) (2.59) (23.97) (3.77) (–1.31) (9.89) (31.01) Client characteristics Client male (d) 8.815* (1.90) log(client age) –32.060*** (–3.93) Swiss (d) –86.891*** (–6.61) log(length of rel.) 18.887** (2.32) Advisor characteristics Advisor male (d) 13.394 (1.56) log(advisor age) 16.803 (0.71) Senior (d) 13.087 (1.44) Client-advisor fixed effects No Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.685 0.775 0.774 0.875 0.713 0.635 0.590 0.877 0.691 N 262,835 273,044 273,044 273,044 273,044 273,044 273,044 273,044 273,044 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit (Columns 1 to 3), the quarterly securities account fee (Column 4), the quarterly securities transaction income (Column 5), the quarterly management fee (Column 6), the quarterly other commission and fee income (Column 7), the quarterly interest income (Column 8), or the quarterly expenses per client (Column 9). Portfolio characteristics are denoted in Swiss Francs and scaled by 1,000 (except for the variable At least one trade (d)). In Columns 2 to 9, client and advisor characteristics are captured by client-advisor fixed effects. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 2 Determinants of profits Profit (CHF) Sec. account fee (CHF) Sec. trans. inc. (CHF) Mgmt. fee (CHF) Other com./fee inc. (CHF) Interest inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) (7) (8) (9) Advice characteristics % advised trades 252.016*** 237.301*** 0.956 153.059*** –9.341*** 20.017*** –4.886 –17.248*** (8.32) (10.89) (0.54) (7.83) (–3.32) (3.46) (–1.34) (–3.64) % advisor-initiated 270.796*** (6.98) Managed account (d) 374.920*** 382.703*** 379.074*** –118.806*** –145.605*** 895.724*** 122.069*** 0.584 305.791*** (10.39) (5.37) (5.31) (–9.11) (–2.76) (13.03) (4.67) (0.04) (17.79) Portfolio characteristics Securities account 1.065*** 0.798*** 0.796*** 0.286*** 0.246*** 0.178*** 0.234*** 0.084*** 0.162*** (40.41) (13.75) (13.71) (24.83) (5.59) (6.27) (15.04) (4.54) (13.01) Savings account 0.632*** 0.803*** 0.804*** 0.051*** 0.368*** –0.016 0.002 0.485*** 0.054*** (12.68) (8.49) (8.48) (5.13) (5.31) (–0.35) (0.09) (12.59) (2.79) Retirement savings –0.776*** –2.454*** –2.462*** –0.036 –0.180 –0.358*** –0.132 –1.460*** 0.173** (–7.09) (–8.28) (–8.33) (–1.29) (–0.96) (–2.97) (–1.28) (–9.03) (2.09) Checking account 1.825*** 1.801*** 1.804*** 0.031** 0.740*** 0.180*** 0.081** 0.829*** 0.108*** (14.95) (17.28) (17.24) (2.49) (7.15) (3.01) (2.21) (16.86) (4.94) Mortgage 2.142*** 1.713*** 1.711*** 0.019*** 0.120* –0.047 –0.027 1.398*** 0.142*** (47.59) (10.20) (10.20) (2.74) (1.90) (–1.05) (–1.26) (11.98) (5.59) Loan 1.487*** 0.811*** 0.811*** 0.017 –0.044 0.020 –0.079 0.963*** –0.007 (8.28) (3.40) (3.39) (0.70) (–0.26) (0.18) (–0.99) (6.40) (–0.20) At least one trade (d) 109.077*** 117.998*** 124.656*** 1.915*** 121.321*** –5.529*** 6.569*** –1.994* 74.477*** (8.47) (9.52) (9.54) (5.60) (11.68) (–2.65) (3.76) (–1.70) (40.22) Trading volume 3.980*** 3.431*** 3.446*** 0.013** 4.812*** 0.134*** –0.028 0.160*** 0.882*** (25.25) (21.99) (22.11) (2.59) (23.97) (3.77) (–1.31) (9.89) (31.01) Client characteristics Client male (d) 8.815* (1.90) log(client age) –32.060*** (–3.93) Swiss (d) –86.891*** (–6.61) log(length of rel.) 18.887** (2.32) Advisor characteristics Advisor male (d) 13.394 (1.56) log(advisor age) 16.803 (0.71) Senior (d) 13.087 (1.44) Client-advisor fixed effects No Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.685 0.775 0.774 0.875 0.713 0.635 0.590 0.877 0.691 N 262,835 273,044 273,044 273,044 273,044 273,044 273,044 273,044 273,044 Profit (CHF) Sec. account fee (CHF) Sec. trans. inc. (CHF) Mgmt. fee (CHF) Other com./fee inc. (CHF) Interest inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) (7) (8) (9) Advice characteristics % advised trades 252.016*** 237.301*** 0.956 153.059*** –9.341*** 20.017*** –4.886 –17.248*** (8.32) (10.89) (0.54) (7.83) (–3.32) (3.46) (–1.34) (–3.64) % advisor-initiated 270.796*** (6.98) Managed account (d) 374.920*** 382.703*** 379.074*** –118.806*** –145.605*** 895.724*** 122.069*** 0.584 305.791*** (10.39) (5.37) (5.31) (–9.11) (–2.76) (13.03) (4.67) (0.04) (17.79) Portfolio characteristics Securities account 1.065*** 0.798*** 0.796*** 0.286*** 0.246*** 0.178*** 0.234*** 0.084*** 0.162*** (40.41) (13.75) (13.71) (24.83) (5.59) (6.27) (15.04) (4.54) (13.01) Savings account 0.632*** 0.803*** 0.804*** 0.051*** 0.368*** –0.016 0.002 0.485*** 0.054*** (12.68) (8.49) (8.48) (5.13) (5.31) (–0.35) (0.09) (12.59) (2.79) Retirement savings –0.776*** –2.454*** –2.462*** –0.036 –0.180 –0.358*** –0.132 –1.460*** 0.173** (–7.09) (–8.28) (–8.33) (–1.29) (–0.96) (–2.97) (–1.28) (–9.03) (2.09) Checking account 1.825*** 1.801*** 1.804*** 0.031** 0.740*** 0.180*** 0.081** 0.829*** 0.108*** (14.95) (17.28) (17.24) (2.49) (7.15) (3.01) (2.21) (16.86) (4.94) Mortgage 2.142*** 1.713*** 1.711*** 0.019*** 0.120* –0.047 –0.027 1.398*** 0.142*** (47.59) (10.20) (10.20) (2.74) (1.90) (–1.05) (–1.26) (11.98) (5.59) Loan 1.487*** 0.811*** 0.811*** 0.017 –0.044 0.020 –0.079 0.963*** –0.007 (8.28) (3.40) (3.39) (0.70) (–0.26) (0.18) (–0.99) (6.40) (–0.20) At least one trade (d) 109.077*** 117.998*** 124.656*** 1.915*** 121.321*** –5.529*** 6.569*** –1.994* 74.477*** (8.47) (9.52) (9.54) (5.60) (11.68) (–2.65) (3.76) (–1.70) (40.22) Trading volume 3.980*** 3.431*** 3.446*** 0.013** 4.812*** 0.134*** –0.028 0.160*** 0.882*** (25.25) (21.99) (22.11) (2.59) (23.97) (3.77) (–1.31) (9.89) (31.01) Client characteristics Client male (d) 8.815* (1.90) log(client age) –32.060*** (–3.93) Swiss (d) –86.891*** (–6.61) log(length of rel.) 18.887** (2.32) Advisor characteristics Advisor male (d) 13.394 (1.56) log(advisor age) 16.803 (0.71) Senior (d) 13.087 (1.44) Client-advisor fixed effects No Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.685 0.775 0.774 0.875 0.713 0.635 0.590 0.877 0.691 N 262,835 273,044 273,044 273,044 273,044 273,044 273,044 273,044 273,044 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit (Columns 1 to 3), the quarterly securities account fee (Column 4), the quarterly securities transaction income (Column 5), the quarterly management fee (Column 6), the quarterly other commission and fee income (Column 7), the quarterly interest income (Column 8), or the quarterly expenses per client (Column 9). Portfolio characteristics are denoted in Swiss Francs and scaled by 1,000 (except for the variable At least one trade (d)). In Columns 2 to 9, client and advisor characteristics are captured by client-advisor fixed effects. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. The coefficients on the size of the securities portfolio and the size of the savings account are both positive and statistically significant, suggesting that wealthier clients generate more profits for the bank. Retirement savings seem to generate losses for the bank, indicating that previously agreed-upon interest rates paid on these accounts are above current market conditions during our sample period. The coefficient on the size of the checking account is positive. Thus, clients with more funds on their checking accounts generate higher profits for the bank. Moreover, clients holding mortgages and loans are substantially more profitable for the bank than other clients. The coefficients on the dummy variable that equals one for client-quarters with at least one trade and on trading volume are both positive and statistically significant, meaning that securities transactions significantly increase bank profits. Turning to the impact of client characteristics, we find weak evidence that male clients generate higher bank profits than female clients. Younger clients are significantly more profitable than older clients and foreign clients are significantly more profitable for the bank than Swiss clients. Furthermore, clients who are with the bank for a longer period of time generate higher profits. Regarding the influence of advisor characteristics, we find that none of the coefficient estimates is statistically significant at conventional levels. While taking advantage of the panel structure of our data and controlling for time fixed effects is a first step towards establishing causality, there are still important endogeneity concerns we need to address: clients relying more heavily on advice may have poor financial skills and might have spent even more on expensive financial products (and eventually may have generated even higher profits for the bank) if they had not been advised. Hence, in Column 2, we add client-advisor fixed effects to our panel regression to control for unobservable client and advisor characteristics which are constant over time and for the match between the two. This allows us to isolate the within-person variation of the impact of advice on profits. In the fixed effects regressions, we exclude the (mostly time-invariant) client and advisor characteristics. The coefficient on the percentage of advised trades now captures the difference in quarterly profits between quarters with more and fewer advised transactions after controlling for the average profit from the respective client-advisor pair. Similarly, the coefficient on the managed account dummy variable now measures the change in quarterly profits when a client switches from a self-managed account to a bank-managed account or back. Results in Column 2 show that the coefficient on the percentage of advised trades becomes economically slightly weaker but gains statistical significance when compared to Column 1. The coefficient on the managed account dummy variable stays about the same with statistical significance remaining well above the 1% significance level. While client-advisor fixed effects should alleviate endogeneity concerns to a large extent, there is still one remaining problem even in this setting: clients could possibly approach their advisors more frequently with trading ideas that are particularly profitable for the bank. Therefore, the superior profitability of advised trades could be driven by the client’s initiative rather than the advisor’s initiative. Thus, in Column 3, we rerun the analysis only classifying trades as advised transactions if they follow an advisor-initiated contact. This approach allows us to investigate the profitability of trades that result from the advisor’s initiative. Results in Column 3 show that the coefficient estimate on the percentage of advised trades that follow advisor-initiated contacts is economically even stronger than the coefficient estimate on all advised trades in Column 2. Hence, advisors seem to actively approach clients with investment recommendations that are particularly profitable for the bank. One explanation for our findings is the commission-based remuneration model of our bank that incentivizes financial advisors to induce transactions that increase bank profits. While we have no information on the specific numerical details of the individual compensation contracts of advisors at our bank, we know that advisors usually earn a fixed salary as well as a bonus. The bonus depends on the overall performance of the bank, the performance of the branch, and the individual performance of the advisor. The performance is defined based on various key figures such as the inflow of new money and the commissions and fees generated with clients. To investigate the potential drivers of bank profits in more detail, we rerun our analysis for the various profit centers of the bank separately. In Columns 4 to 9, we again estimate panel regressions using our most conservative specification with client-advisor and time fixed effects from Column 2. However, now the dependent variable is the income from different profit centers and the incurred expenses of the bank, respectively: the securities account fee in Column 4, the securities transaction income in Column 5, the management fee in Column 6, the other commission and fee income in Column 7, the interest income in Column 8, and expenses in Column 9. The results in Column 4 show that, as expected, securities account fees are mainly driven by the size of the securities portfolio. Clients with managed accounts pay lower securities account fees, driven by the fact that the management fee already includes the account fee for these clients. In Column 5, we document that securities transaction income is most strongly influenced by whether a trade takes place in the respective quarter and the trading volume. The coefficient on the percentage of advised trades is positive and significant at the 1% level. Hence, advisors seem to generate significant transaction-related profits by either selling products that are more profitable than others or inducing customers to take larger positions. The semiannual fixed fee clients pay in managed accounts covers commissions, providing an explanation for why the transaction-related revenues decrease after delegating portfolio management to the bank, as indicated by the significantly negative impact of the managed account dummy. By definition, management fees come mainly from managed accounts (Column 6). The results in Column 7 show that the main drivers of other commission and fee income are the existence of a securities portfolio and a checking account. Other commission and fee income contains, among other things, recurring payments from mutual funds and fees for payment transactions. While the former is driven by mutual fund holdings in the securities portfolio, the latter is driven by the number of transactions in the checking account. Clients who rely on advice when trading and clients with managed accounts generate significantly higher other commission and fee income. Hence, advisors also seem to generate higher holding-related profits. In Column 8, we find that, as expected, interest income is driven by savings, retirement savings, checking account holdings, mortgages, and loans. Finally, in Column 9, higher values of all portfolio characteristics are associated with higher expenses for the bank (except for the provision of loans). Moreover, switching to a managed account also increases expenses for the bank significantly. However, the coefficient on the percentage of advised trades is negative and statistically significant. This finding suggests that advisors promote trades that are, ceteris paribus, associated with lower expenses than trades clients execute independently. Note that labor costs of financial advisors (that can be assigned to individual clients) are already included in the expense figure. These results highlight the importance of not only focusing on revenues but also taking into account the costs that accrue at the client level to get a precise view of the role of financial advice for the profit generation of banks. We run a number of robustness tests. Results are always based on variations of our baseline regression specification reported in Column 2 of Table 2. Table IA1 in the Internet Appendix presents the results. We first examine whether our results hold with a broader or narrower definition of advised trades. In the above analysis, we define trades as advised if they occur within the first five trading days after an advisory contact. In Column 1 of Table IA1, we rerun our main analysis but use all client-advisor contacts available in our data set to identify advised trades, which increases the fraction of advised clients substantially from 10.6% to 34.3%. In Column 2 (3), we classify trades as advised transactions if they take place anytime within four weeks after (on the exact day of) the advisory contact. Next, we investigate whether the skewed distribution of some of our variables influences our results. In all of our analyses, we winsorize profit and portfolio characteristics at the 1% level and the 99% level to reduce the influence of outliers. In Column 4, we take a more conservative approach and winsorize profit and portfolio characteristics at the 10% level and the 90% level. Moreover, in Column 5, we run a median regression instead of an ordinary least squares (OLS) regression. In all these tests, our results remain qualitatively unchanged. 2.2 The determinants of transaction- and holding-related bank profits In this section, we analyze which transactions and which holdings are most profitable to shed light on how advisors can influence bank profitability. We start by analyzing transaction-related profits. We do not have information on the revenues and expenses generated by each individual trade. Hence, to investigate the profitability of transactions, we focus on quarters with exactly one trade. Moreover, we restrict our sample to self-managed accounts. We use panel regression specifications similar to those in Table 2 and focus on those profit centers that we have shown to be significantly influenced by transactions: profits, the securities transaction income, and expenses. The set of explanatory variables includes dummy variables for trades in different asset classes: we include dummies for foreign bond trades, Swiss stock trades, foreign stock trades, trades in the bank’s own mutual funds, trades in mutual funds of partner firms that have an explicit distribution agreement with our bank, other mutual fund trades, derivative transactions, and structured product trades. The omitted base case in the regressions is the transactions in Swiss bonds. Moreover, we include all portfolio characteristics as control variables (except for the dummy variable that takes the value one in quarters with at least one trade as there is one trade per quarter in all specifications by definition). We also include the percentage of advised trades, which by construction becomes a dummy variable in this setting. The coefficients on these control variables are not tabulated for space reasons. Table 3 presents the results. In Column 1, we use profits as dependent variable. We document that quarterly bank profits from clients executing a trade in a mutual fund of our bank are on average CHF 145 higher (31.2% of the average quarterly bank profit in the respective sample) compared to the average quarterly profits from our base case of a trade in Swiss bonds of the same client-advisor pair. Moreover, trades in structured products increase average quarterly profits by CHF 92 (19.9% of the average quarterly bank profit in the respective sample). Trades in foreign bonds are also associated with significantly positive transaction-related profits. The results in Columns 2 and 3 show that the higher profitability of these transactions is almost entirely driven by higher securities transaction income, as the type of the transaction does not cause much variation in expenses for the bank. One explanation for higher securities transaction income is transaction-related payments paid by the department that manages the bank’s own mutual funds as well as by structured product providers. As our bank does not issue its own structured products, higher bank profits cannot be driven directly by the issue premium of structured products documented in the literature (e.g., Burth, Kraus, and Wohlwend 2001; Wallmeier and Diethelm 2009; Henderson and Pearson 2011). Table 3 Determinants of transaction-related profits Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) Foreign bond (d) 135.122*** 101.382*** 27.764*** 25.930 18.339 24.350*** (5.33) (8.03) (4.37) (0.67) (0.84) (3.05) Swiss stock (d) –11.183 5.612 2.447 –60.134 –48.572** 3.406 (–0.48) (0.34) (0.38) (–1.63) (–2.11) (0.41) Foreign stock (d) –17.736 10.893 –1.598 –61.026 –16.225 4.018 (–0.59) (0.79) (–0.18) (–1.28) (–0.67) (0.35) Bank’s own fund (d) 144.591** 192.341*** –2.405 39.421 6.288 –10.704 (2.12) (5.17) (–0.12) (0.31) (0.12) (–0.40) Partner fund (d) –28.714 –44.480** –7.859 –35.139 –37.310 –9.736 (–1.07) (–2.47) (–1.20) (–0.82) (–1.49) (–1.25) Other fund (d) 18.526 –2.857 –17.004* –52.308 –59.065** –13.081 (0.66) (–0.17) (–1.83) (–1.07) (–2.03) (–1.03) Derivative (d) –191.184*** –165.848*** 19.108** –97.615** –69.364*** 21.878** (–7.60) (–11.10) (2.57) (–2.52) (–3.04) (2.54) Structured product (d) 92.262*** 121.017*** 4.738 108.483** 50.468* 7.489 (3.02) (5.23) (0.53) (2.33) (1.86) (0.59) Swiss bonds 3.391*** 3.373*** 0.053 (3.77) (5.90) (0.46) Foreign bonds 5.366*** 4.765*** 0.119* (16.51) (33.64) (1.73) Swiss stocks 9.271*** 9.478*** 0.055 (12.50) (21.34) (0.33) Foreign stocks 10.962*** 9.768*** –0.210 (5.35) (19.80) (–0.65) Bank’s own funds 9.149** 11.959*** 0.389 (2.33) (7.61) (0.95) Partner funds 4.194*** 3.722*** 0.123** (9.04) (15.63) (2.38) Other funds 6.320*** 5.833*** –0.067 (6.52) (10.46) (–0.39) Derivatives 17.122*** 14.041*** –0.754 (3.94) (6.59) (–0.61) Structured products 3.981*** 6.253*** –0.010 (3.46) (9.67) (–0.04) Portfolio characteristics Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Adj. R2 0.675 0.424 0.547 0.713 0.637 0.547 N 34,360 34,360 34,360 34,360 34,360 34,360 Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) Foreign bond (d) 135.122*** 101.382*** 27.764*** 25.930 18.339 24.350*** (5.33) (8.03) (4.37) (0.67) (0.84) (3.05) Swiss stock (d) –11.183 5.612 2.447 –60.134 –48.572** 3.406 (–0.48) (0.34) (0.38) (–1.63) (–2.11) (0.41) Foreign stock (d) –17.736 10.893 –1.598 –61.026 –16.225 4.018 (–0.59) (0.79) (–0.18) (–1.28) (–0.67) (0.35) Bank’s own fund (d) 144.591** 192.341*** –2.405 39.421 6.288 –10.704 (2.12) (5.17) (–0.12) (0.31) (0.12) (–0.40) Partner fund (d) –28.714 –44.480** –7.859 –35.139 –37.310 –9.736 (–1.07) (–2.47) (–1.20) (–0.82) (–1.49) (–1.25) Other fund (d) 18.526 –2.857 –17.004* –52.308 –59.065** –13.081 (0.66) (–0.17) (–1.83) (–1.07) (–2.03) (–1.03) Derivative (d) –191.184*** –165.848*** 19.108** –97.615** –69.364*** 21.878** (–7.60) (–11.10) (2.57) (–2.52) (–3.04) (2.54) Structured product (d) 92.262*** 121.017*** 4.738 108.483** 50.468* 7.489 (3.02) (5.23) (0.53) (2.33) (1.86) (0.59) Swiss bonds 3.391*** 3.373*** 0.053 (3.77) (5.90) (0.46) Foreign bonds 5.366*** 4.765*** 0.119* (16.51) (33.64) (1.73) Swiss stocks 9.271*** 9.478*** 0.055 (12.50) (21.34) (0.33) Foreign stocks 10.962*** 9.768*** –0.210 (5.35) (19.80) (–0.65) Bank’s own funds 9.149** 11.959*** 0.389 (2.33) (7.61) (0.95) Partner funds 4.194*** 3.722*** 0.123** (9.04) (15.63) (2.38) Other funds 6.320*** 5.833*** –0.067 (6.52) (10.46) (–0.39) Derivatives 17.122*** 14.041*** –0.754 (3.94) (6.59) (–0.61) Structured products 3.981*** 6.253*** –0.010 (3.46) (9.67) (–0.04) Portfolio characteristics Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Adj. R2 0.675 0.424 0.547 0.713 0.637 0.547 N 34,360 34,360 34,360 34,360 34,360 34,360 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit (Columns 1 and 4), the quarterly securities transaction income (Columns 2 and 5), or the quarterly expenses per client (Columns 3 and 6). We restrict the sample to self-managed accounts and to quarters with only one trade. The omitted base category is the transactions in Swiss bonds. In Columns 4 to 6, we additionally include the actual size of transactions denoted in Swiss Francs and scaled by 1,000. The variables % advised trades, Securities account, Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 3 Determinants of transaction-related profits Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) Foreign bond (d) 135.122*** 101.382*** 27.764*** 25.930 18.339 24.350*** (5.33) (8.03) (4.37) (0.67) (0.84) (3.05) Swiss stock (d) –11.183 5.612 2.447 –60.134 –48.572** 3.406 (–0.48) (0.34) (0.38) (–1.63) (–2.11) (0.41) Foreign stock (d) –17.736 10.893 –1.598 –61.026 –16.225 4.018 (–0.59) (0.79) (–0.18) (–1.28) (–0.67) (0.35) Bank’s own fund (d) 144.591** 192.341*** –2.405 39.421 6.288 –10.704 (2.12) (5.17) (–0.12) (0.31) (0.12) (–0.40) Partner fund (d) –28.714 –44.480** –7.859 –35.139 –37.310 –9.736 (–1.07) (–2.47) (–1.20) (–0.82) (–1.49) (–1.25) Other fund (d) 18.526 –2.857 –17.004* –52.308 –59.065** –13.081 (0.66) (–0.17) (–1.83) (–1.07) (–2.03) (–1.03) Derivative (d) –191.184*** –165.848*** 19.108** –97.615** –69.364*** 21.878** (–7.60) (–11.10) (2.57) (–2.52) (–3.04) (2.54) Structured product (d) 92.262*** 121.017*** 4.738 108.483** 50.468* 7.489 (3.02) (5.23) (0.53) (2.33) (1.86) (0.59) Swiss bonds 3.391*** 3.373*** 0.053 (3.77) (5.90) (0.46) Foreign bonds 5.366*** 4.765*** 0.119* (16.51) (33.64) (1.73) Swiss stocks 9.271*** 9.478*** 0.055 (12.50) (21.34) (0.33) Foreign stocks 10.962*** 9.768*** –0.210 (5.35) (19.80) (–0.65) Bank’s own funds 9.149** 11.959*** 0.389 (2.33) (7.61) (0.95) Partner funds 4.194*** 3.722*** 0.123** (9.04) (15.63) (2.38) Other funds 6.320*** 5.833*** –0.067 (6.52) (10.46) (–0.39) Derivatives 17.122*** 14.041*** –0.754 (3.94) (6.59) (–0.61) Structured products 3.981*** 6.253*** –0.010 (3.46) (9.67) (–0.04) Portfolio characteristics Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Adj. R2 0.675 0.424 0.547 0.713 0.637 0.547 N 34,360 34,360 34,360 34,360 34,360 34,360 Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) Profit (CHF) Sec. trans. inc. (CHF) Expenses (CHF) (1) (2) (3) (4) (5) (6) Foreign bond (d) 135.122*** 101.382*** 27.764*** 25.930 18.339 24.350*** (5.33) (8.03) (4.37) (0.67) (0.84) (3.05) Swiss stock (d) –11.183 5.612 2.447 –60.134 –48.572** 3.406 (–0.48) (0.34) (0.38) (–1.63) (–2.11) (0.41) Foreign stock (d) –17.736 10.893 –1.598 –61.026 –16.225 4.018 (–0.59) (0.79) (–0.18) (–1.28) (–0.67) (0.35) Bank’s own fund (d) 144.591** 192.341*** –2.405 39.421 6.288 –10.704 (2.12) (5.17) (–0.12) (0.31) (0.12) (–0.40) Partner fund (d) –28.714 –44.480** –7.859 –35.139 –37.310 –9.736 (–1.07) (–2.47) (–1.20) (–0.82) (–1.49) (–1.25) Other fund (d) 18.526 –2.857 –17.004* –52.308 –59.065** –13.081 (0.66) (–0.17) (–1.83) (–1.07) (–2.03) (–1.03) Derivative (d) –191.184*** –165.848*** 19.108** –97.615** –69.364*** 21.878** (–7.60) (–11.10) (2.57) (–2.52) (–3.04) (2.54) Structured product (d) 92.262*** 121.017*** 4.738 108.483** 50.468* 7.489 (3.02) (5.23) (0.53) (2.33) (1.86) (0.59) Swiss bonds 3.391*** 3.373*** 0.053 (3.77) (5.90) (0.46) Foreign bonds 5.366*** 4.765*** 0.119* (16.51) (33.64) (1.73) Swiss stocks 9.271*** 9.478*** 0.055 (12.50) (21.34) (0.33) Foreign stocks 10.962*** 9.768*** –0.210 (5.35) (19.80) (–0.65) Bank’s own funds 9.149** 11.959*** 0.389 (2.33) (7.61) (0.95) Partner funds 4.194*** 3.722*** 0.123** (9.04) (15.63) (2.38) Other funds 6.320*** 5.833*** –0.067 (6.52) (10.46) (–0.39) Derivatives 17.122*** 14.041*** –0.754 (3.94) (6.59) (–0.61) Structured products 3.981*** 6.253*** –0.010 (3.46) (9.67) (–0.04) Portfolio characteristics Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Adj. R2 0.675 0.424 0.547 0.713 0.637 0.547 N 34,360 34,360 34,360 34,360 34,360 34,360 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit (Columns 1 and 4), the quarterly securities transaction income (Columns 2 and 5), or the quarterly expenses per client (Columns 3 and 6). We restrict the sample to self-managed accounts and to quarters with only one trade. The omitted base category is the transactions in Swiss bonds. In Columns 4 to 6, we additionally include the actual size of transactions denoted in Swiss Francs and scaled by 1,000. The variables % advised trades, Securities account, Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. In Columns 4 to 6, we add the actual size of the respective trade to the specifications from Columns 1 to 3 to investigate the effect of trade size on profitability. These variables are denoted in Swiss Francs and scaled by 1,000. The results show that larger trades in any asset class are associated with higher bank profits (Column 4). In Column 5, we document that larger trades in any asset class are also associated with higher securities transaction income. At the same time, expenses are unrelated to the trade size for most asset classes (Column 6). Overall, these findings show that the bank has a strong incentive to induce trades in the bank’s own mutual funds, foreign bonds, and structured products as well as larger trades. Focusing on quarters with exactly one trade makes it easy to determine the average profitability of trades. However, quarters with only one trade might be fundamentally different from quarters with multiple trades. Therefore, in Table IA2 in the Internet Appendix, we estimate regression specifications similar to those in Table 3 but use all quarters with at least one transaction rather than only quarters with precisely one trade. In Columns 1 to 3, variables that capture the number of trades in each asset class are used as main independent variables. Consistent with our findings in Table 3, we find coefficient estimates to be largest for the bank’s own mutual funds, foreign bonds, and structured products, confirming that these trades are most profitable for the bank. In Columns 4 to 6, we use variables that capture the trading volume in each asset class as main explanatory variables. All coefficients are significantly positive, indicating that profitability increases with trading volume, which is again consistent with our findings from Table 3. In Table IA3 in the Internet Appendix, we run two additional robustness tests to make sure that our results are not driven by outliers: First, we winsorize all profit and portfolio characteristics at the 10% level and the 90% level rather than at the 1% level and the 99% level. Second, we run a median regression instead of an OLS regression. In both tests, we find transactions in the bank’s own mutual funds, foreign bonds, and structured products to be most profitable from the bank’s point of view. Next, we focus on holding-related profits. To separate these profits from transaction-related profits, we investigate the drivers of profitability in quarters without any transactions. As before, we focus on self-managed account quarters. The regression specifications are again similar to those in Table 2. We focus on those profit centers that have been shown to be significantly influenced by securities holdings in Table 2: profits, the other commission and fee income, and expenses. The independent variables of interest are the holdings in different asset classes. Holdings are denoted in Swiss Francs and scaled by 1,000. All regressions also include the full set of portfolio characteristics (except for the dummy variable that takes the value one in quarters with at least one trade and the trading volume as we focus on quarters without any transactions). For space reasons, we only report the coefficients on the holdings in the different asset classes. Table 4 shows the results. In Column 1, we document that larger holdings in any asset class are associated with higher profits. However, the effect is by far strongest for mutual funds. Within the category of mutual funds, holdings in the bank’s own mutual funds are about 50% more profitable than holdings in mutual funds of partner firms. Specifically, we find that holding CHF 100,000 in own-bank mutual funds (mutual funds of partner firms) results in an average quarterly profit of CHF 206 (CHF 136) for our bank (93.5% and 61.7% of the average quarterly bank profit in the respective sample). The results in Columns 2 and 3 show the sources of these profits. Larger holdings in the bank’s own mutual funds and mutual funds of partner firms are associated with higher other commission and fee income, while there is not much variation in holding-related expenses across asset classes. The substantial other commission and fee income the bank generates with its own mutual funds and mutual funds of partner firms is due to payments made by mutual funds while clients hold these securities in their portfolios. In summary, these findings suggest that the advisors have incentives to promote mutual funds, and within this asset class particularly the bank’s own mutual funds, if they are motivated to maximize profits for the bank. Table 4 Determinants of holding-related profits Profit (CHF) Other com./fee inc. (CHF) Expenses (CHF) (1) (2) (3) Swiss bonds 0.156* 0.135*** 0.294*** (1.85) (5.62) (13.35) Foreign bonds 0.212*** –0.031 0.206*** (3.59) (–1.49) (13.22) Swiss stocks 0.258*** 0.139*** 0.257*** (4.03) (3.64) (10.85) Foreign stocks 0.615*** –0.070 0.278*** (3.72) (–0.82) (6.03) Bank’s own funds 2.060*** 1.421*** 0.400** (4.18) (6.70) (2.19) Partner funds 1.359*** 1.258*** 0.248*** (21.58) (35.03) (14.71) Other funds 0.613*** 0.387*** 0.215*** (5.48) (5.72) (5.49) Derivatives 1.469*** 1.022*** 0.652*** (3.80) (3.83) (7.50) Structured products 0.294* –0.118* 0.252*** (1.79) (–1.84) (7.48) Portfolio characteristics Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Time fixed effects Yes Yes Yes Adj. R2 0.717 0.584 0.522 N 200,444 200,444 200,444 Profit (CHF) Other com./fee inc. (CHF) Expenses (CHF) (1) (2) (3) Swiss bonds 0.156* 0.135*** 0.294*** (1.85) (5.62) (13.35) Foreign bonds 0.212*** –0.031 0.206*** (3.59) (–1.49) (13.22) Swiss stocks 0.258*** 0.139*** 0.257*** (4.03) (3.64) (10.85) Foreign stocks 0.615*** –0.070 0.278*** (3.72) (–0.82) (6.03) Bank’s own funds 2.060*** 1.421*** 0.400** (4.18) (6.70) (2.19) Partner funds 1.359*** 1.258*** 0.248*** (21.58) (35.03) (14.71) Other funds 0.613*** 0.387*** 0.215*** (5.48) (5.72) (5.49) Derivatives 1.469*** 1.022*** 0.652*** (3.80) (3.83) (7.50) Structured products 0.294* –0.118* 0.252*** (1.79) (–1.84) (7.48) Portfolio characteristics Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Time fixed effects Yes Yes Yes Adj. R2 0.717 0.584 0.522 N 200,444 200,444 200,444 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit (Column 1), the quarterly other commission and fee income (Column 2), or the quarterly expenses per client (Column 3). We restrict the sample to self-managed accounts and to quarters without any trades. Portfolio characteristics are denoted in Swiss Francs and scaled by 1,000. The variables Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 4 Determinants of holding-related profits Profit (CHF) Other com./fee inc. (CHF) Expenses (CHF) (1) (2) (3) Swiss bonds 0.156* 0.135*** 0.294*** (1.85) (5.62) (13.35) Foreign bonds 0.212*** –0.031 0.206*** (3.59) (–1.49) (13.22) Swiss stocks 0.258*** 0.139*** 0.257*** (4.03) (3.64) (10.85) Foreign stocks 0.615*** –0.070 0.278*** (3.72) (–0.82) (6.03) Bank’s own funds 2.060*** 1.421*** 0.400** (4.18) (6.70) (2.19) Partner funds 1.359*** 1.258*** 0.248*** (21.58) (35.03) (14.71) Other funds 0.613*** 0.387*** 0.215*** (5.48) (5.72) (5.49) Derivatives 1.469*** 1.022*** 0.652*** (3.80) (3.83) (7.50) Structured products 0.294* –0.118* 0.252*** (1.79) (–1.84) (7.48) Portfolio characteristics Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Time fixed effects Yes Yes Yes Adj. R2 0.717 0.584 0.522 N 200,444 200,444 200,444 Profit (CHF) Other com./fee inc. (CHF) Expenses (CHF) (1) (2) (3) Swiss bonds 0.156* 0.135*** 0.294*** (1.85) (5.62) (13.35) Foreign bonds 0.212*** –0.031 0.206*** (3.59) (–1.49) (13.22) Swiss stocks 0.258*** 0.139*** 0.257*** (4.03) (3.64) (10.85) Foreign stocks 0.615*** –0.070 0.278*** (3.72) (–0.82) (6.03) Bank’s own funds 2.060*** 1.421*** 0.400** (4.18) (6.70) (2.19) Partner funds 1.359*** 1.258*** 0.248*** (21.58) (35.03) (14.71) Other funds 0.613*** 0.387*** 0.215*** (5.48) (5.72) (5.49) Derivatives 1.469*** 1.022*** 0.652*** (3.80) (3.83) (7.50) Structured products 0.294* –0.118* 0.252*** (1.79) (–1.84) (7.48) Portfolio characteristics Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Time fixed effects Yes Yes Yes Adj. R2 0.717 0.584 0.522 N 200,444 200,444 200,444 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit (Column 1), the quarterly other commission and fee income (Column 2), or the quarterly expenses per client (Column 3). We restrict the sample to self-managed accounts and to quarters without any trades. Portfolio characteristics are denoted in Swiss Francs and scaled by 1,000. The variables Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. We perform the same two robustness tests for holding-related profits as for transaction-related profits: First, we winsorize profit and portfolio characteristics at the 10% level and the 90% level. Second, we run a median regression. Table IA4 in the Internet Appendix presents the results of these tests confirming our previous findings. 2.3 Do financial advisors promote trades and implement portfolios that are expected to maximize bank profits? In the next step, we analyze whether financial advisors act in a way that, based on the findings in the previous section, would be expected to maximize bank profits or whether advisors are mainly concerned about the suitability of recommended products to their clients. We follow two approaches to do so: First, we investigate what trades clients execute following optional financial advice (Section 2.3.1). Second, we examine how advisors restructure portfolios after the delegation of account management to the bank (Section 2.3.2). 2.3.1 Optional financial advice We start by comparing advised and independent trades. If advisors recommend transactions that are most profitable for the bank, we expect advised trades to be larger than independently executed transactions and to be more likely to involve foreign bonds, and particularly the bank’s own mutual funds, mutual funds of partner firms, and structured products. For this analysis, we restrict the sample to clients who never delegate account management to the bank during our investigation period. Moreover, we focus on purchases of securities. Sales differ from purchases to the extent that selling a security requires that a client holds it in the portfolio since short sales are not allowed by our bank. Thus, focusing on purchases allows for a cleaner analysis of trade motivations. Table 5 presents univariate comparisons of trade characteristics of advised and independent trades. Consistent with our conjecture, we find that advised purchases are on average about 50% larger than independently executed purchases. Moreover, about 2.5% of advised buys involve the bank’s own mutual funds while only about 0.8% of independent trades are trades in own-bank mutual funds. In addition, 18.3% (7.8%) of advised (independent) trades are trades in structured products.15 Advised trades are also significantly more likely to involve foreign bonds and foreign stocks. Table 5 Univariate comparisons of advised and independent trades Advised Independent Difference t-value N Trade size (CHF) 45,130 30,808 14,321*** 16.64 83,567 Swiss bond (d) 0.017 0.016 0.000 0.21 83,567 Foreign bond (d) 0.249 0.163 0.087*** 16.82 83,567 Swiss stock (d) 0.171 0.307 –0.137*** –21.69 83,567 Foreign stock (d) 0.159 0.147 0.012** 2.50 83,567 Bank’s own fund (d) 0.025 0.008 0.017*** 12.64 83,567 Partner fund (d) 0.140 0.148 –0.008 –1.54 83,567 Other fund (d) 0.026 0.041 –0.015*** –5.48 83,567 Derivative (d) 0.015 0.071 –0.056*** –16.33 83,567 Structured product (d) 0.183 0.078 0.105*** 27.40 83,567 Advised Independent Difference t-value N Trade size (CHF) 45,130 30,808 14,321*** 16.64 83,567 Swiss bond (d) 0.017 0.016 0.000 0.21 83,567 Foreign bond (d) 0.249 0.163 0.087*** 16.82 83,567 Swiss stock (d) 0.171 0.307 –0.137*** –21.69 83,567 Foreign stock (d) 0.159 0.147 0.012** 2.50 83,567 Bank’s own fund (d) 0.025 0.008 0.017*** 12.64 83,567 Partner fund (d) 0.140 0.148 –0.008 –1.54 83,567 Other fund (d) 0.026 0.041 –0.015*** –5.48 83,567 Derivative (d) 0.015 0.071 –0.056*** –16.33 83,567 Structured product (d) 0.183 0.078 0.105*** 27.40 83,567 This table presents univariate comparisons of trade characteristics of advised and independent trades. We restrict the sample to purchases of clients who never delegate account management to the bank. Appendix A provides detailed descriptions of all variables used throughout the study. Means of the subgroups are tested for equality using a standard t-test. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 5 Univariate comparisons of advised and independent trades Advised Independent Difference t-value N Trade size (CHF) 45,130 30,808 14,321*** 16.64 83,567 Swiss bond (d) 0.017 0.016 0.000 0.21 83,567 Foreign bond (d) 0.249 0.163 0.087*** 16.82 83,567 Swiss stock (d) 0.171 0.307 –0.137*** –21.69 83,567 Foreign stock (d) 0.159 0.147 0.012** 2.50 83,567 Bank’s own fund (d) 0.025 0.008 0.017*** 12.64 83,567 Partner fund (d) 0.140 0.148 –0.008 –1.54 83,567 Other fund (d) 0.026 0.041 –0.015*** –5.48 83,567 Derivative (d) 0.015 0.071 –0.056*** –16.33 83,567 Structured product (d) 0.183 0.078 0.105*** 27.40 83,567 Advised Independent Difference t-value N Trade size (CHF) 45,130 30,808 14,321*** 16.64 83,567 Swiss bond (d) 0.017 0.016 0.000 0.21 83,567 Foreign bond (d) 0.249 0.163 0.087*** 16.82 83,567 Swiss stock (d) 0.171 0.307 –0.137*** –21.69 83,567 Foreign stock (d) 0.159 0.147 0.012** 2.50 83,567 Bank’s own fund (d) 0.025 0.008 0.017*** 12.64 83,567 Partner fund (d) 0.140 0.148 –0.008 –1.54 83,567 Other fund (d) 0.026 0.041 –0.015*** –5.48 83,567 Derivative (d) 0.015 0.071 –0.056*** –16.33 83,567 Structured product (d) 0.183 0.078 0.105*** 27.40 83,567 This table presents univariate comparisons of trade characteristics of advised and independent trades. We restrict the sample to purchases of clients who never delegate account management to the bank. Appendix A provides detailed descriptions of all variables used throughout the study. Means of the subgroups are tested for equality using a standard t-test. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. We corroborate these univariate findings by running panel OLS and logit regressions on the individual trade level with trade size and dummy variables for trades in the different asset classes as dependent variables. To account for the skewness of trade size, we use the natural logarithm of this variable. The main explanatory variable in these regressions is a dummy variable that equals one for advised trades, and zero otherwise. All regressions also include the full set of portfolio characteristics (except for the dummy variable that takes the value one in quarters with at least one trade and the trading volume since we focus on individual trades). When investigating the determinants of trade size, we additionally include the dummies for trades in the different asset classes as control variables. Thereby, we make sure that results for trade size are not driven by trade size varying across asset classes. For space reasons, we do not report the coefficient estimates on the control variables. All regressions also contain client-advisor and time fixed effects. Panel A of Table 6 presents the results. In Column 1, we use trade size as dependent variable. The coefficient estimate suggests that advised transactions are on average 18.4% larger than independent trades (significant at the 1% level). Table 6 Do financial advisors promote trades that are expected to maximize bank profits? log(trade size) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) A. Advised trades Advised (d) 0.184*** –0.048 0.055 0.096 0.119 0.568*** –0.261*** –0.541*** –1.680*** 0.515*** (5.37) (–0.31) (0.55) (0.79) (1.09) (2.69) (–2.89) (–2.90) (–5.63) (3.92) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.641 Pseudo R2 0.043 0.011 0.008 0.006 0.213 0.020 0.082 0.079 0.080 N 81,935 10,014 32,898 48,373 36,142 8,747 27,590 18,751 26,949 27,590 B. Advisor-initiated advised trades Advisor-initiated (d) 0.190*** –0.179 –0.120 –0.195 0.084 0.476* –0.142 –0.706* –1.798*** 0.984*** (3.61) (–0.88) (–0.73) (–1.19) (0.35) (1.77) (–0.90) (–1.77) (–7.43) (6.28) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.640 Pseudo R2 0.041 0.012 0.008 0.006 0.210 0.019 0.086 0.073 0.085 N 78,440 9,515 30,338 46,079 34,601 7,809 25,500 17,822 26,033 25,795 log(trade size) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) A. Advised trades Advised (d) 0.184*** –0.048 0.055 0.096 0.119 0.568*** –0.261*** –0.541*** –1.680*** 0.515*** (5.37) (–0.31) (0.55) (0.79) (1.09) (2.69) (–2.89) (–2.90) (–5.63) (3.92) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.641 Pseudo R2 0.043 0.011 0.008 0.006 0.213 0.020 0.082 0.079 0.080 N 81,935 10,014 32,898 48,373 36,142 8,747 27,590 18,751 26,949 27,590 B. Advisor-initiated advised trades Advisor-initiated (d) 0.190*** –0.179 –0.120 –0.195 0.084 0.476* –0.142 –0.706* –1.798*** 0.984*** (3.61) (–0.88) (–0.73) (–1.19) (0.35) (1.77) (–0.90) (–1.77) (–7.43) (6.28) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.640 Pseudo R2 0.041 0.012 0.008 0.006 0.210 0.019 0.086 0.073 0.085 N 78,440 9,515 30,338 46,079 34,601 7,809 25,500 17,822 26,033 25,795 This table presents the results from OLS and logit regressions with client-advisor and time fixed effects for advised trades (panel A) and advisor-initiated advised trades (panel B). The dependent variable is the logarithm of trade size (Column 1) or a dummy variable that equals one for trades in different asset classes (Columns 2 to 10). In panel A, we restrict the sample to purchases of clients who never delegate account management to the bank. In panel B, we restrict the sample to advisor-initiated advised purchases and independent purchases (dropping client-initiated advised purchases) of clients who never delegate account management to the bank. The variables Securities account, Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Column 1 contains the dummy variables for trades in different asset classes as additional controls (not reported). Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 6 Do financial advisors promote trades that are expected to maximize bank profits? log(trade size) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) A. Advised trades Advised (d) 0.184*** –0.048 0.055 0.096 0.119 0.568*** –0.261*** –0.541*** –1.680*** 0.515*** (5.37) (–0.31) (0.55) (0.79) (1.09) (2.69) (–2.89) (–2.90) (–5.63) (3.92) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.641 Pseudo R2 0.043 0.011 0.008 0.006 0.213 0.020 0.082 0.079 0.080 N 81,935 10,014 32,898 48,373 36,142 8,747 27,590 18,751 26,949 27,590 B. Advisor-initiated advised trades Advisor-initiated (d) 0.190*** –0.179 –0.120 –0.195 0.084 0.476* –0.142 –0.706* –1.798*** 0.984*** (3.61) (–0.88) (–0.73) (–1.19) (0.35) (1.77) (–0.90) (–1.77) (–7.43) (6.28) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.640 Pseudo R2 0.041 0.012 0.008 0.006 0.210 0.019 0.086 0.073 0.085 N 78,440 9,515 30,338 46,079 34,601 7,809 25,500 17,822 26,033 25,795 log(trade size) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) A. Advised trades Advised (d) 0.184*** –0.048 0.055 0.096 0.119 0.568*** –0.261*** –0.541*** –1.680*** 0.515*** (5.37) (–0.31) (0.55) (0.79) (1.09) (2.69) (–2.89) (–2.90) (–5.63) (3.92) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.641 Pseudo R2 0.043 0.011 0.008 0.006 0.213 0.020 0.082 0.079 0.080 N 81,935 10,014 32,898 48,373 36,142 8,747 27,590 18,751 26,949 27,590 B. Advisor-initiated advised trades Advisor-initiated (d) 0.190*** –0.179 –0.120 –0.195 0.084 0.476* –0.142 –0.706* –1.798*** 0.984*** (3.61) (–0.88) (–0.73) (–1.19) (0.35) (1.77) (–0.90) (–1.77) (–7.43) (6.28) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.640 Pseudo R2 0.041 0.012 0.008 0.006 0.210 0.019 0.086 0.073 0.085 N 78,440 9,515 30,338 46,079 34,601 7,809 25,500 17,822 26,033 25,795 This table presents the results from OLS and logit regressions with client-advisor and time fixed effects for advised trades (panel A) and advisor-initiated advised trades (panel B). The dependent variable is the logarithm of trade size (Column 1) or a dummy variable that equals one for trades in different asset classes (Columns 2 to 10). In panel A, we restrict the sample to purchases of clients who never delegate account management to the bank. In panel B, we restrict the sample to advisor-initiated advised purchases and independent purchases (dropping client-initiated advised purchases) of clients who never delegate account management to the bank. The variables Securities account, Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Column 1 contains the dummy variables for trades in different asset classes as additional controls (not reported). Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. In Table IA5 in the Internet Appendix, we use the specification from Column 1, but additionally include interaction terms of the advised trade dummy and the asset-class dummies. This procedure allows us to determine whether the result from Column 1 in Table 6 is driven by specific asset classes. For all interaction terms, we report positive coefficient estimates, except for derivatives where the coefficient is negative but close to zero. Advisors recommend trades that are between 2.4% (other funds) and 29.8% (foreign stock) larger than transactions carried out independently. In Columns 2 to 10 of panel A of Table 6, we use asset-class dummies as dependent variables. We find that advised trades are significantly more likely to involve the bank’s own mutual funds (Column 6) and structured products (Column 10), that is, trades that are more profitable from the bank’s point of view as shown in the previous section. We also document that advised trades are significantly less frequently trades in funds of partner firms (Column 7) and trades in other funds (Column 8). This finding suggests that advisors particularly promote own-bank funds instead of the funds of partner firms and other funds. Advised trades are also significantly less likely to be in derivatives (Column 9), the least profitable asset class in terms of transaction-related profits. Logit regressions with client-advisor fixed effects do not allow us to estimate marginal effects. To examine the economic significance in a multivariate setting, we thus run the same set of regressions as in panel A of Table 6 but without client-advisor fixed effects. Instead, we include client and advisor characteristics as additional controls. Table IA6 in the Internet Appendix reports the results of these alternative regression specifications. The coefficient estimates suggest that the probability of an advised trade being a trade in an own-bank mutual fund is 0.6 percentage points higher compared to independent trades (Column 6) and the probability of an advised trade being a trade in a structured product is 7.4 percentage points higher compared to independent trades (Column 10). Given the percentage of own-bank trades among independently executed transactions of 0.8% and the percentage of structured product trades among independent trades of 7.8%, the documented effects are economically meaningful, showing that both probabilities nearly double. Even though trades in the bank’s own funds are among the most profitable, the number of transactions in own-bank funds as a percentage of all trades appears relatively low. The reason for this is that the bank’s offering of own funds is narrow. The bank set up its first own fund only shortly before the start of our investigation period. However, it issues a number of new funds during our sample period, most likely motivated by the substantial transaction- and holding-related profits generated by the bank’s own funds. As a result, the number of trades in own-bank mutual funds increases over time relative to the number of trades in other asset classes. Trades in structured products are particularly attractive for the bank not only because they are highly profitable upon execution but also because most structured products expire after a rather short period of time, while stocks and mutual funds usually do not have a fixed maturity and bonds typically only mature after several years. In our sample, the average time to maturity of structured products is 1.3 years. Hence, an advisor recommending a structured product knows that after about a year the client will probably trade again either to sell the underlying (if the product delivered the underlying at maturity) or to buy a new asset (when the product was settled in cash), eventually leading to additional transaction-related income. To mitigate concerns that our findings are driven by clients contacting their advisors only in the case of certain types of trades, for example, large trades or trades in specific product categories, we reevaluate our results based on the subset of trades that follow advisor-initiated contacts. Panel B of Table 6 reports the results of this analysis. Results remain similar to those in panel A. In Column 7, when examining whether advisors promote funds of partner firms, the coefficient estimate becomes statistically insignificant. This pattern suggests that trades in funds of partner firms are as frequent among advised trades as they are among independent trades and advisors mainly promote own funds instead of funds from third-party providers. Taken together, these findings indicate that advisors actively contact their clients with the intention of inducing larger trades, trades in the bank’s own mutual funds, and trades in structured products, that is, the kinds of trades that lead to the largest profits. 2.3.2 Managed accounts Next, we investigate how the bank and its advisors restructure portfolios when a client decides to fully delegate account management to the bank. We expect the portfolio turnover to increase at the time of the switch as implementing the desired portfolio allocation will typically require restructuring of the existing portfolio. Clients in general do not pay trading commissions in managed accounts as those are already covered by the fixed management fee. Still, we predict portfolio turnover to increase also in the long run as financial advisors receive payments from product providers upon execution of certain transactions. Because of these payments, we also conjecture that the portfolio shares of own-bank mutual funds, mutual funds of partner firms, and structured products increase in managed accounts. In contrast, we do not expect the fraction of foreign bonds to rise significantly even though they are one of the most profitable asset classes from the bank’s perspective. The reason is that profitability of individual trades in foreign bonds is driven by high commissions charged by the bank and that these are covered by the fixed management fee. We employ a regression specification similar to that in Column 2 of Table 2. The dependent variable is either the quarterly portfolio turnover ratio or the portfolio share in different asset classes at the end of the respective quarter. Following Barber and Odean (2002), we define the quarterly portfolio turnover ratio as one-half the sum of purchases and sales in a given quarter divided by the beginning of the quarter portfolio value. The explanatory variable of interest is a dummy variable that equals one if a client delegates account management to the bank in the respective quarter, and zero otherwise. All regressions contain the full set of portfolio characteristics. However, when investigating portfolio turnover we do not include the dummy that equals one for quarters with at least one trade and the trading volume as these variables are highly correlated with the turnover variable. Coefficients on portfolio characteristics are not reported for space reasons. Every regression also contains client-advisor and time fixed effects. Consequently, the results are driven by clients who switch at least once from a self-managed to a bank-managed account or vice versa. Table 7 reports the results. The coefficient estimate on the managed account dummy in Column 1 shows that quarterly turnover increases by 10.4 percentage points on average upon delegation of portfolio management to the bank. This effect is economically sizeable given that the average portfolio turnover ratio in self-managed accounts amounts to 3.7% per quarter. To investigate whether the increase in turnover is due to a temporary surge in trading activity around the switch or whether trading activity increases permanently after the switch, we reestimate the regression from Column 1 but exclude the quarter of the switch as well as the three subsequent quarters (results not reported). Even if we start a full year after the switch, we still find the turnover ratio to be 3.8 percentage points higher after the switch, indicating that trading activity is permanently elevated by a factor of two when the bank assumes management of the client’s portfolio. Table 7 Do financial advisors implement portfolios that are expected to maximize bank profits? Turnover (%) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Managed account (d) 10.437*** –2.203*** –4.272 –5.008*** –1.353*** 5.756*** –9.909*** 15.064*** –0.228*** 2.597*** (11.37) (–3.30) (–1.50) (–6.30) (–4.27) (10.59) (–3.83) (8.33) (–5.07) (4.55) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.172 0.846 0.895 0.910 0.894 0.471 0.916 0.800 0.554 0.487 N 253,212 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 Turnover (%) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Managed account (d) 10.437*** –2.203*** –4.272 –5.008*** –1.353*** 5.756*** –9.909*** 15.064*** –0.228*** 2.597*** (11.37) (–3.30) (–1.50) (–6.30) (–4.27) (10.59) (–3.83) (8.33) (–5.07) (4.55) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.172 0.846 0.895 0.910 0.894 0.471 0.916 0.800 0.554 0.487 N 253,212 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly portfolio turnover (Column 1) or the portfolio share in different asset classes at the end of the respective quarter (Columns 2 to 10). The variables % advised trades, Securities account, Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Columns 2 to 10 contain the variables At least one trade (d) and Trading volume as additional controls (not reported). Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 7 Do financial advisors implement portfolios that are expected to maximize bank profits? Turnover (%) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Managed account (d) 10.437*** –2.203*** –4.272 –5.008*** –1.353*** 5.756*** –9.909*** 15.064*** –0.228*** 2.597*** (11.37) (–3.30) (–1.50) (–6.30) (–4.27) (10.59) (–3.83) (8.33) (–5.07) (4.55) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.172 0.846 0.895 0.910 0.894 0.471 0.916 0.800 0.554 0.487 N 253,212 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 Turnover (%) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Managed account (d) 10.437*** –2.203*** –4.272 –5.008*** –1.353*** 5.756*** –9.909*** 15.064*** –0.228*** 2.597*** (11.37) (–3.30) (–1.50) (–6.30) (–4.27) (10.59) (–3.83) (8.33) (–5.07) (4.55) Portfolio characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adj. R2 0.172 0.846 0.895 0.910 0.894 0.471 0.916 0.800 0.554 0.487 N 253,212 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 255,722 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly portfolio turnover (Column 1) or the portfolio share in different asset classes at the end of the respective quarter (Columns 2 to 10). The variables % advised trades, Securities account, Savings account, Retirement savings, Checking account, Mortgage, and Loan are included as controls in all regressions, but not reported. Columns 2 to 10 contain the variables At least one trade (d) and Trading volume as additional controls (not reported). Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Panel A of Figure 2 graphically illustrates the results. The figure shows quarterly turnover ratios around the switch to a bank-managed account. Consistent with our findings from the regression analysis, turnover is at about 5% or less in the six quarters prior to the switch. In the quarter of the switch and the subsequent quarter, turnover surges to over 20%. Average turnover decreases thereafter but the turnover ratio remains well above the ratio prior to the switch.16 Figure 2 View largeDownload slide Turnover and asset allocation around switches to managed accounts This figure shows the average quarterly portfolio turnover ratio per client (panel A) and the average asset allocation per client at the end of the respective quarter (panel B) around switches to managed accounts. Clients delegate account management to the bank in quarter $$t = 0$$. During our investigation period from January 2002 to June 2005, 371 clients switch to a managed account. Figure 2 View largeDownload slide Turnover and asset allocation around switches to managed accounts This figure shows the average quarterly portfolio turnover ratio per client (panel A) and the average asset allocation per client at the end of the respective quarter (panel B) around switches to managed accounts. Clients delegate account management to the bank in quarter $$t = 0$$. During our investigation period from January 2002 to June 2005, 371 clients switch to a managed account. Panel B of Figure 2 shows graphically how the asset allocation changes around the switch. Before the switch, portfolios are dominated by foreign bonds (40.4%), mutual funds of partner firms (34.1%), and Swiss stocks (11.1%). After the switch, the portfolio share of these asset classes declines from over 85% to about 60%. A substantial fraction of portfolios is now allocated to other mutual funds (22.3%), own-bank mutual funds (8.0%), and structured products (7.4%). We also examine changes in portfolio composition in a more formal way. In Columns 2 to 10 of Table 7, we analyze the changes to the portfolio that occur after the switch based on regression analysis. To do so, we use the portfolio share in different asset classes as the dependent variable. We find that the share of own-bank mutual funds increases by about 5.8 percentage points after the switch to a managed account (Column 6) and the share in structured products rises by 2.6 percentage points (Column 10). These effects are not driven by the issuance of new own-bank funds or new structured products during our investigation period as all our regressions contain time fixed effects that control for potential time trends in general ownership patterns. In Column 8, we also find that switchers have a 15 percentage point higher exposure to mutual funds of third-party providers. The portfolio share of all other asset classes is reduced upon delegation of portfolio management to the bank. Overall, the analysis of switches from self-managed to bank-managed accounts (and vice versa) provides additional evidence that financial advisors implement portfolios that are expected to maximize bank profits. Even though our analysis in the previous section shows that the bank’s own funds and funds of partner firms are more profitable for the bank than funds of third-party providers, financial advisors substantially reduce the share of mutual funds of partner firms in clients’ portfolios after taking over responsibility. While puzzling at first, this is most likely due to partner fund firms not covering all fund categories. Figure IA2 in the Internet Appendix shows that the composition of mutual funds in the portfolios changes substantially around switches to bank-managed accounts. Thus, if a fund category is not covered by partner firms, the bank is forced to invest in funds of third-party providers. 2.4 Which clients, advisors, and client-advisor matches drive the results? In this section, we analyze which client and advisor characteristics, and which matches between these two are responsible for advised trades being particularly profitable for the bank. To this end, we regress quarterly bank profits per client on the percentage of advised trades this client conducts in the respective quarter, the percentage of advised trades interacted with various client and advisor characteristics, and all portfolio characteristics. The interaction terms tell us which advised trades are particularly profitable. Regressions also contain client-advisor and quarter fixed effects. Since client and advisor characteristics are (mostly) time-invariant, the base effects of client and advisor characteristics are picked up by the client-advisor fixed effects.17 Hence, the regression setup corresponds to that in Column 2 of Table 2, but is augmented by the additional interaction terms. Table 8 presents the results. In Column 1, we start by interacting the percentage of advised trades with clients’ bank wealth. We expect bank wealth to have a positive effect on the profitability of advised trades as clients who are wealthier are more likely to execute larger trades. Consistent with our expectations, we find a strong positive coefficient estimate for the impact of the interaction between the percentage of advised trades and bank wealth. Table 8 Which clients, advisors, and client-advisor matches drive the results? Profit (CHF) (1) (2) (3) (4) % advised trades $$\times$$ log(bank wealth) 113.714*** 115.749*** 111.371*** 110.261*** (7.44) (7.46) (6.56) (6.41) % advised trades $$\times$$ Risk tolerance = 3 (d) 68.232** (2.32) % advised trades $$\times$$ log(client age) –29.378 –111.832 (–0.50) (–1.45) % advised trades $$\times$$ log(advisor age) 93.215 144.598* (1.12) (1.86) % advised trades $$\times$$ Different age (d) 82.539* (1.86) % advised trades $$\times$$ Client older (d) 127.381** (2.53) % advised trades $$\times$$ Advisor older (d) –65.276 (–0.87) Portfolio characteristics Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Adj. R2 0.775 0.764 0.776 0.776 N 273,044 103,443 262,835 262,835 Profit (CHF) (1) (2) (3) (4) % advised trades $$\times$$ log(bank wealth) 113.714*** 115.749*** 111.371*** 110.261*** (7.44) (7.46) (6.56) (6.41) % advised trades $$\times$$ Risk tolerance = 3 (d) 68.232** (2.32) % advised trades $$\times$$ log(client age) –29.378 –111.832 (–0.50) (–1.45) % advised trades $$\times$$ log(advisor age) 93.215 144.598* (1.12) (1.86) % advised trades $$\times$$ Different age (d) 82.539* (1.86) % advised trades $$\times$$ Client older (d) 127.381** (2.53) % advised trades $$\times$$ Advisor older (d) –65.276 (–0.87) Portfolio characteristics Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Adj. R2 0.775 0.764 0.776 0.776 N 273,044 103,443 262,835 262,835 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit per client. The variables % advised trades, Managed account (d), log(bank wealth), Securities account, Savings account, Retirement savings, Checking account, Mortgage, Loan, At least one trade (d), and Trading volume are included as controls in all regressions, but not reported. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 8 Which clients, advisors, and client-advisor matches drive the results? Profit (CHF) (1) (2) (3) (4) % advised trades $$\times$$ log(bank wealth) 113.714*** 115.749*** 111.371*** 110.261*** (7.44) (7.46) (6.56) (6.41) % advised trades $$\times$$ Risk tolerance = 3 (d) 68.232** (2.32) % advised trades $$\times$$ log(client age) –29.378 –111.832 (–0.50) (–1.45) % advised trades $$\times$$ log(advisor age) 93.215 144.598* (1.12) (1.86) % advised trades $$\times$$ Different age (d) 82.539* (1.86) % advised trades $$\times$$ Client older (d) 127.381** (2.53) % advised trades $$\times$$ Advisor older (d) –65.276 (–0.87) Portfolio characteristics Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Adj. R2 0.775 0.764 0.776 0.776 N 273,044 103,443 262,835 262,835 Profit (CHF) (1) (2) (3) (4) % advised trades $$\times$$ log(bank wealth) 113.714*** 115.749*** 111.371*** 110.261*** (7.44) (7.46) (6.56) (6.41) % advised trades $$\times$$ Risk tolerance = 3 (d) 68.232** (2.32) % advised trades $$\times$$ log(client age) –29.378 –111.832 (–0.50) (–1.45) % advised trades $$\times$$ log(advisor age) 93.215 144.598* (1.12) (1.86) % advised trades $$\times$$ Different age (d) 82.539* (1.86) % advised trades $$\times$$ Client older (d) 127.381** (2.53) % advised trades $$\times$$ Advisor older (d) –65.276 (–0.87) Portfolio characteristics Yes Yes Yes Yes Client-advisor fixed effects Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Adj. R2 0.775 0.764 0.776 0.776 N 273,044 103,443 262,835 262,835 This table presents the results from panel regressions with client-advisor and time fixed effects. The dependent variable is the quarterly profit per client. The variables % advised trades, Managed account (d), log(bank wealth), Securities account, Savings account, Retirement savings, Checking account, Mortgage, Loan, At least one trade (d), and Trading volume are included as controls in all regressions, but not reported. Appendix A provides detailed descriptions of all variables used throughout the study. Standard errors are clustered at the advisor level. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. In Column 2, we add a variable capturing the risk tolerance of clients to our baseline specification from Column 1. Specifically, we create a dummy variable that equals one for the 16.3% of clients with the highest level of risk tolerance as assessed by financial advisors in an in-person meeting when clients set up their accounts with the bank. The information on clients’ risk tolerance is only available for about one-third of the total sample, which substantially reduces the sample size in Column 2. To examine the impact of risk tolerance on the profitability of advised trades, we interact the percentage of advised trades with the risk tolerance dummy. We expect risk tolerance to have a positive effect on the profitability of advised trades as clients who are more risk tolerant are more likely to conduct trades in more risky asset classes such as the bank’s own funds and structured products.18 Consistent with this conjecture, we find a significantly positive coefficient on the interaction term of the percentage of advised trades with risk tolerance. In the next set of analyses, we zoom-in on the impact of the match between clients and advisors to analyze whether more similar matches along demographic characteristics generate more or less profitable advised trades.19 While we are not aware of any study in finance that investigates the effect of demographic customer-employee matches on profitability, considerable attention has been devoted to this question in the marketing and organization literature.20 Several related theories suggest that the match between customer and employee demographics can improve sales performance.21 However, empirical research has yielded mixed results. While some studies find that customer-employee matches have a positive impact on sales performance (e.g., Avery et al. 2012; Richard et al. 2017), others document that mismatches enhance performance (e.g., Dwyer, Richard, and Shepherd 1998), and again others find no effect of demographic matches on performance (e.g., Ayers and Siegelman 1995; Leonard, Levine, and Joshi 2004). In our analysis, we focus on age matches between clients and advisors.22 To do so, we create a dummy variable that equals one for client-advisor pairs with an age difference of more than 10 years, and zero otherwise.23 To analyze the impact of age-matching on the profitability of advised trades, we then add an interaction term between the percentage of advised trades and the different-age dummy variable to our model from Column 1. This regression additionally includes the percentage of advised trades interacted with clients’ age and advisors’ age to control for the effect of age on the profitability of advised trades. Results are shown in Column 3. The coefficient on the interaction between the percentage of advised trades and the different-age dummy is positive and significant (t-statistic of 1.86), suggesting that age mismatches enhance the profitability of advised trades. In Column 4, we use a more refined definition of age matches. Specifically, we form three age-matching groups: (1) clients are of similar age to their advisors, that is, the age difference is not more than 10 years; (2) clients are more than 10 years older than their advisors; and (3) clients are more than 10 years younger than their advisors. We again augment the model in Column 1 with these additional variables. We omit the dummy variable for the same-age group, that is, the age-matching dummies show the incremental effect of the specific match as compared to the base case of a same-age match. We find a strong positive impact of the client being older than the advisor on the profitability of advised trades, that is, advisors that are relatively younger than their clients seem to generate trades that are more profitable for the bank. One potential explanation for this finding is that it is easier for advisors to exploit elderly clients than younger ones. Moreover, younger advisors might have stronger incentives to exploit clients due to greater career concerns.24 Finally, to examine why advised trades of wealthier clients, more risk-tolerant clients, and clients who are substantially older than advisors are more profitable, we split clients into two groups according to these characteristics and compare their advised purchases. We focus on the trade characteristics that are associated with higher profits from the bank’s point of view and that are thus strongly promoted by financial advisors as shown in the previous section. Table IA7 in the Internet Appendix reports the results. We find that advised trades of these types of clients are more profitable because these clients are more likely to execute larger trades, trades in own-bank mutual funds, and trades in structured products. Taken together, we find strong evidence that clients’ bank wealth and their risk tolerance affect the profitability of advised trades. Some evidence also suggests that age matching between clients and advisors matters for profitability. 2.5 Is advice beneficial for clients? So far, our results show that advisors promote trades and implement portfolios that eventually maximize bank profits. These findings suggest that they put their own interest and their employer’s interest, not their clients’ interest, first. However, there still may be a win-win situation in the sense that while advisors promote trades and implement portfolios that maximize bank profits, these trades and restructured portfolios are also beneficial for clients. Such a situation would alleviate the issue of potential conflicts of interest. To analyze this possibility, we follow two approaches: First, we investigate whether clients who follow optional financial advice experience superior overall portfolio performance compared to independently acting investors and whether optional-advice-driven trades in structured products perform better than independently executed transactions (Section 2.5.1). Second, we explore whether portfolios perform better after the switch to a bank-managed account (Section 2.5.2). 2.5.1 Optional financial advice We first compare the portfolio performance of advised and independent clients. To do so, we compute monthly net returns of an aggregate calendar-time portfolio consisting of all individual portfolios of advised clients and monthly net returns of an aggregate calendar-time portfolio consisting of all individual portfolios of independent clients. We explain our approach to calculating portfolio performance in detail in Appendix B. We then determine monthly alphas of these portfolios using a seven-factor model that contains a Swiss and an international equity market factor, the investment style factors of Fama and French (1993) and Carhart (1997), a government bond factor, and a corporate bond factor.25 Panel A of Table 9 reports the results of the performance analysis for advised and independent clients. To save space, we only report the intercept (alpha), but not the estimated risk factor loadings. In Column 1 (2), the alpha of the aggregate portfolio of advised (independent) clients is $$-$$0.061% (0.020%) per month and statistically insignificant. Column 3 reports results for the difference portfolio between the advised clients’ and the independent clients’ portfolios. The alpha of the difference portfolio is significantly negative at the 5% level and amounts to $$-$$0.081% per month, that is, advised clients significantly underperform independent clients by approximately 1.0% p.a. Table 9 Portfolio performance A. Portfolio performance of advised and independent clients Monthly portfolio excess return net of transaction costs (%) Advised clients Independent clients Difference portfolio (Advised - Independent) (1) (2) (3) Alpha (%) –0.061 0.020 –0.081** (–1.27) (0.33) (–2.14) Adj. R2 0.987 0.977 0.496 N 42 42 42 B. Portfolio performance of clients around switches to managed accounts Monthly portfolio excess return net of transaction costs (%) Before switch After switch Difference portfolio (After - Before) (1) (2) (3) Alpha (%) 0.093 –0.167 –0.260** (1.14) (–1.37) (–2.06) Adj. R2 0.954 0.917 0.041 N 36 36 36 A. Portfolio performance of advised and independent clients Monthly portfolio excess return net of transaction costs (%) Advised clients Independent clients Difference portfolio (Advised - Independent) (1) (2) (3) Alpha (%) –0.061 0.020 –0.081** (–1.27) (0.33) (–2.14) Adj. R2 0.987 0.977 0.496 N 42 42 42 B. Portfolio performance of clients around switches to managed accounts Monthly portfolio excess return net of transaction costs (%) Before switch After switch Difference portfolio (After - Before) (1) (2) (3) Alpha (%) 0.093 –0.167 –0.260** (1.14) (–1.37) (–2.06) Adj. R2 0.954 0.917 0.041 N 36 36 36 This table presents the risk-adjusted portfolio performance of advised and independent clients (panel A) and the risk-adjusted portfolio performance of clients around switches to managed accounts (panel B). In panel A, the dependent variable is the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of advised clients (Column 1), the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of independent clients (Column 2), or the monthly return difference between these two aggregate portfolios (Column 3). In panel B, the dependent variable is the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of clients who eventually switch to a managed account before the switch (Column 1), the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of clients who switched to a managed account after the switch (Column 2), or the monthly return difference between these two aggregate portfolios (Column 3). Individual portfolios are value-weighted. In panel A, returns are net of commissions and bid-ask spreads. In panel B, returns are net of commissions and bid-ask spreads before the switch and net of the management fee and bid-ask spreads after the switch. Monthly alphas are estimated based on a multi-factor model that includes the Swiss Performance Index (SPI) and the MSCI World Index, the Swiss SMB factor, the Swiss HML factor, and the Swiss momentum factor from Andrea Frazzini’s data library, the Swiss Bond Index (SBI), and the iBoxx Euro Corporate Bond Index. Factor returns are in excess of the Swiss 3-month LIBOR (except for the Swiss SMB factor, the Swiss HML factor, and the Swiss momentum factor). Appendix B provides further details on how we calculate the portfolio performance of advised and independent clients and the portfolio performance of clients around switches to managed accounts. Standard errors are adjusted for heteroscedasticity. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. Table 9 Portfolio performance A. Portfolio performance of advised and independent clients Monthly portfolio excess return net of transaction costs (%) Advised clients Independent clients Difference portfolio (Advised - Independent) (1) (2) (3) Alpha (%) –0.061 0.020 –0.081** (–1.27) (0.33) (–2.14) Adj. R2 0.987 0.977 0.496 N 42 42 42 B. Portfolio performance of clients around switches to managed accounts Monthly portfolio excess return net of transaction costs (%) Before switch After switch Difference portfolio (After - Before) (1) (2) (3) Alpha (%) 0.093 –0.167 –0.260** (1.14) (–1.37) (–2.06) Adj. R2 0.954 0.917 0.041 N 36 36 36 A. Portfolio performance of advised and independent clients Monthly portfolio excess return net of transaction costs (%) Advised clients Independent clients Difference portfolio (Advised - Independent) (1) (2) (3) Alpha (%) –0.061 0.020 –0.081** (–1.27) (0.33) (–2.14) Adj. R2 0.987 0.977 0.496 N 42 42 42 B. Portfolio performance of clients around switches to managed accounts Monthly portfolio excess return net of transaction costs (%) Before switch After switch Difference portfolio (After - Before) (1) (2) (3) Alpha (%) 0.093 –0.167 –0.260** (1.14) (–1.37) (–2.06) Adj. R2 0.954 0.917 0.041 N 36 36 36 This table presents the risk-adjusted portfolio performance of advised and independent clients (panel A) and the risk-adjusted portfolio performance of clients around switches to managed accounts (panel B). In panel A, the dependent variable is the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of advised clients (Column 1), the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of independent clients (Column 2), or the monthly return difference between these two aggregate portfolios (Column 3). In panel B, the dependent variable is the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of clients who eventually switch to a managed account before the switch (Column 1), the monthly excess return of an aggregate calendar-time portfolio consisting of all individual portfolios of clients who switched to a managed account after the switch (Column 2), or the monthly return difference between these two aggregate portfolios (Column 3). Individual portfolios are value-weighted. In panel A, returns are net of commissions and bid-ask spreads. In panel B, returns are net of commissions and bid-ask spreads before the switch and net of the management fee and bid-ask spreads after the switch. Monthly alphas are estimated based on a multi-factor model that includes the Swiss Performance Index (SPI) and the MSCI World Index, the Swiss SMB factor, the Swiss HML factor, and the Swiss momentum factor from Andrea Frazzini’s data library, the Swiss Bond Index (SBI), and the iBoxx Euro Corporate Bond Index. Factor returns are in excess of the Swiss 3-month LIBOR (except for the Swiss SMB factor, the Swiss HML factor, and the Swiss momentum factor). Appendix B provides further details on how we calculate the portfolio performance of advised and independent clients and the portfolio performance of clients around switches to managed accounts. Standard errors are adjusted for heteroscedasticity. t-statistics are provided in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels. In Table IA8 in the Internet Appendix, we perform a set of robustness tests on these results: First, we replicate the analysis from panel A of Table 9 using two alternative factor models. We employ a single-factor CAPM and augment our seven-factor model by a call option and a put option factor to account for the nonlinear payoff profiles of derivatives and structured products.26 Second, we use monthly gross portfolio returns rather than net returns. Third, we use equal-weighting rather than value-weighting when forming aggregate calendar-time portfolios from individual client portfolios. Results from these tests broadly confirm our findings from panel A of Table 9. So far, we focus on portfolio performance rather than the performance of individual transactions as this allows us to capture the overall allocation effect of financial advice. Moreover, forming calendar-time portfolios helps in addressing the problem of cross-sectional correlation of individual transactions (e.g., Barber, Lyon, and Tsai 1999; Odean 1999; Seasholes and Zhu 2010). However, focusing on portfolio performance also has some limitations as many clients who are classified as advised clients regularly conduct trades on their own as well as trades that follow advice. Thus, the inferior portfolio performance of advised clients could be driven by the poor performance of the trades conducted by these clients independently rather than by trades executed on advice. To address this concern, we additionally compare the performance of advised and independent trades of the same client. We focus on trades in structured products as structured products are an asset class heavily promoted by financial advisors and for which we have sufficient data to calculate trade performance. We compute the performance of structured product trades as described in Appendix B. Table IA9 in the Internet Appendix reports the results. In Column 1, we find that advised trades in structured products underperform independently executed transactions, although the difference is not statistically significant. The effect gets markedly stronger once we focus on trades following advisor-initiated contacts (Columns 2 and 3). Using the same data set, Hoechle et al. (2017) compare the performance of advised and independent stock trades. They find that advised stock picks significantly underperform independent stock picks. However, as shown in the previous section, direct equity investments are not the focus of profit-maximizing financial advice. Overall, the trade-level analysis confirms our findings from the portfolio-level analysis that advisor interventions tend to have an adverse effect on investor performance. 2.5.2 Managed accounts Another way to identify the effect of advisor interventions on performance is to investigate the change in portfolio performance from before to after delegation of portfolio management. To do so, we calculate monthly net returns of an aggregate calendar-time portfolio consisting of all individual portfolios of investors who eventually switch to a managed account before the switch and monthly net returns of an aggregate calendar-time portfolio of these investors’ individual portfolios after the switch. Appendix B provides the details of the performance calculation. To determine monthly alphas, we use the seven-factor model described above. Panel B of Table 9 presents the results. While the alpha before the switch and the alpha after the switch are both statistically insignificantly different from zero, the alpha decreases substantially from 0.093% per month before the switch (Column 1) to $$-$$0.167% per month after the switch (Column 2). The resultant difference in alphas amounts to 0.260% per month, or about 3.1% p.a. (Column 3), and is statistically significant at the 5% level, confirming our previous findings that advisor interventions have a negative effect on clients’ overall portfolio performance. In Table IA10 in the Internet Appendix, we perform the same set of robustness tests as for the performance analysis of advised and independent client portfolios in Table IA8. In all tests, we find the alpha after the switch to be lower than the alpha before the switch, indicating that the pattern we observe is robust to alternative specifications. Taken together, our findings suggest that clients relying on financial advice achieve a worse after-cost performance than clients acting independently. This result is consistent with a number of empirical studies investigating the value of financial advice to individual investors (e.g., Hackethal, Haliassos, and Jappelli 2012; Chalmers and Reuter 2015; Foerster et al. 2017; Hoechle et al. 2017). Overall, our evidence points to financial advisors’ aiming at maximizing bank profits and thereby putting the interest of their employer before the interest of their clients. This result does not rule out the possibility that there might be other benefits advisors deliver for their clients, like helping them to feel confident about participating in financial markets (Gennaioli, Shleifer, and Vishny 2015). 3. Conclusion In this paper, we provide new insights into the profit generation process of banks by analyzing how financial advisors generate profits with individual investors. We find that transactions executed based on optional financial advice are generally associated with higher bank profits than independently executed transactions of the same client. We also document that bank profits increase significantly upon full delegation of portfolio management to financial advisors. Our results further show that trades in own-bank mutual funds and structured products generate the highest transaction- and holding-related profits for the bank. Moreover, bank profits tend to increase with trade size. Consistent with the view that advisors mainly promote those trades that are more profitable to the bank, we find advised trades to be significantly larger and more likely to be trades in the bank’s own mutual funds and structured products compared to independently executed trades of the same client. Similarly, trading activity and the share of own-bank funds and structured products significantly increase when clients switch from a self-managed to a bank-managed account. Finally, we show that our findings are not consistent with a win-win situation for clients and the bank: the after-cost performance of advised clients is worse than that of independent clients. Consistently, the portfolio performance of clients who switch to bank-managed accounts drops significantly after the switch. These findings suggest that financial advisors tend to put their employer’s interest rather than their clients’ interest first. To the best of our knowledge, our paper is the first to document empirically that advisors induce transactions that are associated with above-average profits to banks. Thus, our results are of particular importance in light of the ongoing debate on commissions and fees paid in the financial services industry. As discussed in the Introduction, regulatory steps taken so far might not be sufficient to address conflicts of interest of financial advisors employed at banks. We would like to thank an anonymous bank for providing the data. We are grateful to Itay Goldstein (the editor); two anonymous referees; Marc Arnold, Martin Brown, Claire Célérier, John Chalmers, Jay Choi, Anthony Cookson, Christian Ehm, Thomas Etheber, Andre Guettler, Luigi Guiso, Matti Keloharju, Samuli Knüpfer, Peter Limbach, Veronika Pool, Alessandro Previtero, Jean-Charles Rochet, Petra Vokatá, Ingo Walter, Youchang Wu; conference participants at the 1st Research in Behavioral Finance Conference in Rotterdam, the 11th European Winter Finance Summit (Skinance) in Schladming, the 8th Swiss Winter Conference on Financial Intermediation in Lenzerheide, the 18th annual conference of the Swiss Society for Financial Market Research (SGF) in Zurich, the 3rd European Retail Investment Conference (ERIC) in Stuttgart, the 10th annual conference of the Financial Intermediation Research Society (FIRS) in Reykjavik, the 6th Helsinki Finance Summit, the 22nd annual meeting of the German Finance Association (DGF) in Leipzig, the 26th Annual Conference on Financial Economics and Accounting (CFEA) in New Brunswick, the 76th annual meeting of the American Finance Association (AFA) in San Francisco, the 43rd annual meeting of the European Finance Association (EFA) in Oslo, the WU Gutmann Center Symposium on “Financial Advice and Asset Management” in Vienna; and seminar participants at the University of Mannheim, Karlsruhe Institute of Technology, the University of Luxembourg, and the University of Bern for helpful comments. Part of this research was undertaken while Schaub was a visiting researcher at the UCLA Anderson School of Management and Schmid was a visiting researcher at NYU Stern School of Business. Financial support from the Swiss National Science Foundation (SNF) is gratefully acknowledged. Supplementary data can be found on The Review of Financial Studies Web site. Appendix A. Variable Descriptions This appendix defines the variables used throughout the study. The appendix also provides the source of the data and the observation frequency of the variable (in parentheses). Client and advisor characteristics are time-invariant as they were collected by the bank on the date of the account opening and overwritten if new information is provided either by clients or by advisors. Variable Description Source (frequency) Profit characteristics Profit Profit the bank generates with each individual client defined as revenues minus expenses (in Swiss Francs) Bank (quarterly) Revenues Contains all revenues that can be assigned to a client (in Swiss Francs). This includes the securities account fee, the securities transaction income, the management fee, other commission and fee income, interest income, and other income Bank (quarterly) Expenses Contains all costs that can be assigned to a client (in Swiss Francs). This includes labor costs of financial advisors and third-party charges Bank (quarterly) Securities account fee Contains the fees the client pays for the securities account (in Swiss Francs) Bank (quarterly) Securities transaction income Contains all commissions and fees from securities transactions (in Swiss Francs). This includes the commissions and fees directly charged by the bank and transaction-related payments the bank receives from product providers Bank (quarterly) Management fee Contains the fees the client pays when delegating account management to the bank (in Swiss Francs) Bank (quarterly) Other commission and fee income Contains all commissions and fees other than the securities account fee, the securities transaction income, and the management fee (in Swiss Francs). This includes recurring payments the bank receives from product providers, fees for account keeping, fees for payment transactions, and fees for credit cards Bank (quarterly) Interest income Contains the net interest income from savings, mortgages, and loans calculated according to the market interest rate method (in Swiss Francs). The market interest rate method assumes that assets and liabilities are refinanced at current market conditions Bank (quarterly) Advice characteristics Advised (d) Dummy variable that equals one for trades executed within five days of an advisory contact, that is, between t = 0 and t = 4, and zero otherwise Bank (daily) Advisor-initiated (d) Dummy variable that equals one for advised trades that follow a contact that was initiated by the advisor, and zero otherwise Bank (daily) % advised trades Number of advised trades per quarter / Total number of trades per quarter Bank (quarterly) % advisor-initiated Number of trades that follow advisor-initiated contacts per quarter / Total number of trades per quarter Bank (quarterly) Managed account (d) Dummy variable that equals one for client-quarters in which clients delegate account management to the bank, and zero otherwise Bank (quarterly) Portfolio characteristics Bank wealth Total wealth a client holds at our bank (in Swiss Francs). This position is not netted against mortgages and loans Bank (quarterly) log(bank wealth) Natural logarithm of bank wealth Bank (quarterly) Securities account Value of securities portfolio of a client (in Swiss Francs) Bank (quarterly) Savings account Amount of money a client holds in the savings account (in Swiss Francs). This does not include retirement savings Bank (quarterly) Retirement savings Amount of money a client holds in the retirement savings account (in Swiss Francs). The Swiss pension system is based on three pillars: the state pension system, occupational pension provisions, and private pension provisions. Private pension provisions typically take the form of retirement saving accounts that offer higher interest rates than normal savings accounts as well as tax benefits Bank (quarterly) Checking account Amount of money a client holds in the checking account (in Swiss Francs) Bank (quarterly) Mortgage Outstanding mortgage balance of a client (in Swiss Francs) Bank (quarterly) Loan Outstanding loan balance of a client (in Swiss Francs) Bank (quarterly) At least one trade (d) Dummy variable that equals one for client-quarters in which clients execute at least one trade, and zero otherwise Bank (quarterly) # trades Number of trades a client executes per quarter Bank (quarterly) Trading volume Value of all transactions executed by a client in a specific quarter (in Swiss Francs) Bank (quarterly) Turnover $$\frac{1}{2}$$ (Value of all purchases executed by a client in a specific quarter + Value of all sales executed by a client in a specific quarter) / Value of securities portfolio of a client at the beginning of the quarter Bank (quarterly) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Value of securities in the respective asset class (in Swiss Francs; Table 4) Bank (quarterly) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products Value of securities in the respective asset class / Value of securities portfolio of a client (Table 7) Bank (quarterly) Client characteristics Client male (d) Dummy variable that equals one for male clients and zero for female clients Bank (time-invariant) Client age Client’s age (in years) Bank (yearly) log(client age) Natural logarithm of client age Bank (yearly) Education Client’s education (1, unskilled; 2, semiskilled; 3, apprenticeship/ vocational education; 4, high school degree; 5, higher vocational education; 6: technical college degree; and 7, university degree) Bank (time-invariant) Employed (d) Dummy variable that equals one for employed clients, and zero otherwise Bank (time-invariant) Retired (d) Dummy variable that equals one for retired clients, and zero otherwise Bank (time-invariant) Swiss (d) Dummy variable that equals one for clients living in Switzerland and zero for clients living abroad Bank (time-invariant) Risk tolerance Client’s risk tolerance (1, low; 2, medium; and 3, high). The risk tolerance is assessed in an in-person meeting with the advisor at the start of the bank relationship based on a set of predefined questions Bank (time-invariant) Risk tolerance = 3 (d) Dummy variable that equals one for clients with the highest level of risk tolerance, and zero otherwise Bank (time-invariant) Length of relationship Number of years since account was opened (in years). This variable is missing for some clients in our sample that opened their account before December 1995. We assume that these customers opened their account in December 1995 Bank (yearly) log(length of relationship) Natural logarithm of length of relationship Bank (yearly) Advisor characteristics Advisor male (d) Dummy variable that equals one for male advisors and zero for female advisors Bank (time-invariant) Advisor age Advisor’s age (in years) Bank (yearly) log(advisor age) Natural logarithm of advisor age Bank (yearly) Senior (d) Dummy variable that equals one for advisors that have reached a certain hierarchical level within the bank, and zero otherwise Bank (time-invariant) Client-advisor matching characteristics Different age (d) Dummy variable that equals one for client-advisor pairs with an age difference of more than 10 years, and zero otherwise Bank (yearly) Client older (d) Dummy variable that equals one for client-advisor pairs where the client is more than 10 years older than the advisor, and zero otherwise Bank (yearly) Advisor older (d) Dummy variable that equals one for client-advisor pairs where the advisor is more than 10 years older than the client, and zero otherwise Bank (yearly) Trade characteristics Trade size Trade size (in Swiss Francs) Bank (daily) log(trade size) Natural logarithm of trade size Bank (daily) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) Dummy variable that equals one for transactions in the respective asset class, and zero otherwise (Tables 3, 5, and 6) Bank (daily) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Size of transactions in the respective asset class (in Swiss Francs; Table 3) Bank (daily) Variable Description Source (frequency) Profit characteristics Profit Profit the bank generates with each individual client defined as revenues minus expenses (in Swiss Francs) Bank (quarterly) Revenues Contains all revenues that can be assigned to a client (in Swiss Francs). This includes the securities account fee, the securities transaction income, the management fee, other commission and fee income, interest income, and other income Bank (quarterly) Expenses Contains all costs that can be assigned to a client (in Swiss Francs). This includes labor costs of financial advisors and third-party charges Bank (quarterly) Securities account fee Contains the fees the client pays for the securities account (in Swiss Francs) Bank (quarterly) Securities transaction income Contains all commissions and fees from securities transactions (in Swiss Francs). This includes the commissions and fees directly charged by the bank and transaction-related payments the bank receives from product providers Bank (quarterly) Management fee Contains the fees the client pays when delegating account management to the bank (in Swiss Francs) Bank (quarterly) Other commission and fee income Contains all commissions and fees other than the securities account fee, the securities transaction income, and the management fee (in Swiss Francs). This includes recurring payments the bank receives from product providers, fees for account keeping, fees for payment transactions, and fees for credit cards Bank (quarterly) Interest income Contains the net interest income from savings, mortgages, and loans calculated according to the market interest rate method (in Swiss Francs). The market interest rate method assumes that assets and liabilities are refinanced at current market conditions Bank (quarterly) Advice characteristics Advised (d) Dummy variable that equals one for trades executed within five days of an advisory contact, that is, between t = 0 and t = 4, and zero otherwise Bank (daily) Advisor-initiated (d) Dummy variable that equals one for advised trades that follow a contact that was initiated by the advisor, and zero otherwise Bank (daily) % advised trades Number of advised trades per quarter / Total number of trades per quarter Bank (quarterly) % advisor-initiated Number of trades that follow advisor-initiated contacts per quarter / Total number of trades per quarter Bank (quarterly) Managed account (d) Dummy variable that equals one for client-quarters in which clients delegate account management to the bank, and zero otherwise Bank (quarterly) Portfolio characteristics Bank wealth Total wealth a client holds at our bank (in Swiss Francs). This position is not netted against mortgages and loans Bank (quarterly) log(bank wealth) Natural logarithm of bank wealth Bank (quarterly) Securities account Value of securities portfolio of a client (in Swiss Francs) Bank (quarterly) Savings account Amount of money a client holds in the savings account (in Swiss Francs). This does not include retirement savings Bank (quarterly) Retirement savings Amount of money a client holds in the retirement savings account (in Swiss Francs). The Swiss pension system is based on three pillars: the state pension system, occupational pension provisions, and private pension provisions. Private pension provisions typically take the form of retirement saving accounts that offer higher interest rates than normal savings accounts as well as tax benefits Bank (quarterly) Checking account Amount of money a client holds in the checking account (in Swiss Francs) Bank (quarterly) Mortgage Outstanding mortgage balance of a client (in Swiss Francs) Bank (quarterly) Loan Outstanding loan balance of a client (in Swiss Francs) Bank (quarterly) At least one trade (d) Dummy variable that equals one for client-quarters in which clients execute at least one trade, and zero otherwise Bank (quarterly) # trades Number of trades a client executes per quarter Bank (quarterly) Trading volume Value of all transactions executed by a client in a specific quarter (in Swiss Francs) Bank (quarterly) Turnover $$\frac{1}{2}$$ (Value of all purchases executed by a client in a specific quarter + Value of all sales executed by a client in a specific quarter) / Value of securities portfolio of a client at the beginning of the quarter Bank (quarterly) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Value of securities in the respective asset class (in Swiss Francs; Table 4) Bank (quarterly) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products Value of securities in the respective asset class / Value of securities portfolio of a client (Table 7) Bank (quarterly) Client characteristics Client male (d) Dummy variable that equals one for male clients and zero for female clients Bank (time-invariant) Client age Client’s age (in years) Bank (yearly) log(client age) Natural logarithm of client age Bank (yearly) Education Client’s education (1, unskilled; 2, semiskilled; 3, apprenticeship/ vocational education; 4, high school degree; 5, higher vocational education; 6: technical college degree; and 7, university degree) Bank (time-invariant) Employed (d) Dummy variable that equals one for employed clients, and zero otherwise Bank (time-invariant) Retired (d) Dummy variable that equals one for retired clients, and zero otherwise Bank (time-invariant) Swiss (d) Dummy variable that equals one for clients living in Switzerland and zero for clients living abroad Bank (time-invariant) Risk tolerance Client’s risk tolerance (1, low; 2, medium; and 3, high). The risk tolerance is assessed in an in-person meeting with the advisor at the start of the bank relationship based on a set of predefined questions Bank (time-invariant) Risk tolerance = 3 (d) Dummy variable that equals one for clients with the highest level of risk tolerance, and zero otherwise Bank (time-invariant) Length of relationship Number of years since account was opened (in years). This variable is missing for some clients in our sample that opened their account before December 1995. We assume that these customers opened their account in December 1995 Bank (yearly) log(length of relationship) Natural logarithm of length of relationship Bank (yearly) Advisor characteristics Advisor male (d) Dummy variable that equals one for male advisors and zero for female advisors Bank (time-invariant) Advisor age Advisor’s age (in years) Bank (yearly) log(advisor age) Natural logarithm of advisor age Bank (yearly) Senior (d) Dummy variable that equals one for advisors that have reached a certain hierarchical level within the bank, and zero otherwise Bank (time-invariant) Client-advisor matching characteristics Different age (d) Dummy variable that equals one for client-advisor pairs with an age difference of more than 10 years, and zero otherwise Bank (yearly) Client older (d) Dummy variable that equals one for client-advisor pairs where the client is more than 10 years older than the advisor, and zero otherwise Bank (yearly) Advisor older (d) Dummy variable that equals one for client-advisor pairs where the advisor is more than 10 years older than the client, and zero otherwise Bank (yearly) Trade characteristics Trade size Trade size (in Swiss Francs) Bank (daily) log(trade size) Natural logarithm of trade size Bank (daily) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) Dummy variable that equals one for transactions in the respective asset class, and zero otherwise (Tables 3, 5, and 6) Bank (daily) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Size of transactions in the respective asset class (in Swiss Francs; Table 3) Bank (daily) Variable Description Source (frequency) Profit characteristics Profit Profit the bank generates with each individual client defined as revenues minus expenses (in Swiss Francs) Bank (quarterly) Revenues Contains all revenues that can be assigned to a client (in Swiss Francs). This includes the securities account fee, the securities transaction income, the management fee, other commission and fee income, interest income, and other income Bank (quarterly) Expenses Contains all costs that can be assigned to a client (in Swiss Francs). This includes labor costs of financial advisors and third-party charges Bank (quarterly) Securities account fee Contains the fees the client pays for the securities account (in Swiss Francs) Bank (quarterly) Securities transaction income Contains all commissions and fees from securities transactions (in Swiss Francs). This includes the commissions and fees directly charged by the bank and transaction-related payments the bank receives from product providers Bank (quarterly) Management fee Contains the fees the client pays when delegating account management to the bank (in Swiss Francs) Bank (quarterly) Other commission and fee income Contains all commissions and fees other than the securities account fee, the securities transaction income, and the management fee (in Swiss Francs). This includes recurring payments the bank receives from product providers, fees for account keeping, fees for payment transactions, and fees for credit cards Bank (quarterly) Interest income Contains the net interest income from savings, mortgages, and loans calculated according to the market interest rate method (in Swiss Francs). The market interest rate method assumes that assets and liabilities are refinanced at current market conditions Bank (quarterly) Advice characteristics Advised (d) Dummy variable that equals one for trades executed within five days of an advisory contact, that is, between t = 0 and t = 4, and zero otherwise Bank (daily) Advisor-initiated (d) Dummy variable that equals one for advised trades that follow a contact that was initiated by the advisor, and zero otherwise Bank (daily) % advised trades Number of advised trades per quarter / Total number of trades per quarter Bank (quarterly) % advisor-initiated Number of trades that follow advisor-initiated contacts per quarter / Total number of trades per quarter Bank (quarterly) Managed account (d) Dummy variable that equals one for client-quarters in which clients delegate account management to the bank, and zero otherwise Bank (quarterly) Portfolio characteristics Bank wealth Total wealth a client holds at our bank (in Swiss Francs). This position is not netted against mortgages and loans Bank (quarterly) log(bank wealth) Natural logarithm of bank wealth Bank (quarterly) Securities account Value of securities portfolio of a client (in Swiss Francs) Bank (quarterly) Savings account Amount of money a client holds in the savings account (in Swiss Francs). This does not include retirement savings Bank (quarterly) Retirement savings Amount of money a client holds in the retirement savings account (in Swiss Francs). The Swiss pension system is based on three pillars: the state pension system, occupational pension provisions, and private pension provisions. Private pension provisions typically take the form of retirement saving accounts that offer higher interest rates than normal savings accounts as well as tax benefits Bank (quarterly) Checking account Amount of money a client holds in the checking account (in Swiss Francs) Bank (quarterly) Mortgage Outstanding mortgage balance of a client (in Swiss Francs) Bank (quarterly) Loan Outstanding loan balance of a client (in Swiss Francs) Bank (quarterly) At least one trade (d) Dummy variable that equals one for client-quarters in which clients execute at least one trade, and zero otherwise Bank (quarterly) # trades Number of trades a client executes per quarter Bank (quarterly) Trading volume Value of all transactions executed by a client in a specific quarter (in Swiss Francs) Bank (quarterly) Turnover $$\frac{1}{2}$$ (Value of all purchases executed by a client in a specific quarter + Value of all sales executed by a client in a specific quarter) / Value of securities portfolio of a client at the beginning of the quarter Bank (quarterly) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Value of securities in the respective asset class (in Swiss Francs; Table 4) Bank (quarterly) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products Value of securities in the respective asset class / Value of securities portfolio of a client (Table 7) Bank (quarterly) Client characteristics Client male (d) Dummy variable that equals one for male clients and zero for female clients Bank (time-invariant) Client age Client’s age (in years) Bank (yearly) log(client age) Natural logarithm of client age Bank (yearly) Education Client’s education (1, unskilled; 2, semiskilled; 3, apprenticeship/ vocational education; 4, high school degree; 5, higher vocational education; 6: technical college degree; and 7, university degree) Bank (time-invariant) Employed (d) Dummy variable that equals one for employed clients, and zero otherwise Bank (time-invariant) Retired (d) Dummy variable that equals one for retired clients, and zero otherwise Bank (time-invariant) Swiss (d) Dummy variable that equals one for clients living in Switzerland and zero for clients living abroad Bank (time-invariant) Risk tolerance Client’s risk tolerance (1, low; 2, medium; and 3, high). The risk tolerance is assessed in an in-person meeting with the advisor at the start of the bank relationship based on a set of predefined questions Bank (time-invariant) Risk tolerance = 3 (d) Dummy variable that equals one for clients with the highest level of risk tolerance, and zero otherwise Bank (time-invariant) Length of relationship Number of years since account was opened (in years). This variable is missing for some clients in our sample that opened their account before December 1995. We assume that these customers opened their account in December 1995 Bank (yearly) log(length of relationship) Natural logarithm of length of relationship Bank (yearly) Advisor characteristics Advisor male (d) Dummy variable that equals one for male advisors and zero for female advisors Bank (time-invariant) Advisor age Advisor’s age (in years) Bank (yearly) log(advisor age) Natural logarithm of advisor age Bank (yearly) Senior (d) Dummy variable that equals one for advisors that have reached a certain hierarchical level within the bank, and zero otherwise Bank (time-invariant) Client-advisor matching characteristics Different age (d) Dummy variable that equals one for client-advisor pairs with an age difference of more than 10 years, and zero otherwise Bank (yearly) Client older (d) Dummy variable that equals one for client-advisor pairs where the client is more than 10 years older than the advisor, and zero otherwise Bank (yearly) Advisor older (d) Dummy variable that equals one for client-advisor pairs where the advisor is more than 10 years older than the client, and zero otherwise Bank (yearly) Trade characteristics Trade size Trade size (in Swiss Francs) Bank (daily) log(trade size) Natural logarithm of trade size Bank (daily) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) Dummy variable that equals one for transactions in the respective asset class, and zero otherwise (Tables 3, 5, and 6) Bank (daily) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Size of transactions in the respective asset class (in Swiss Francs; Table 3) Bank (daily) Variable Description Source (frequency) Profit characteristics Profit Profit the bank generates with each individual client defined as revenues minus expenses (in Swiss Francs) Bank (quarterly) Revenues Contains all revenues that can be assigned to a client (in Swiss Francs). This includes the securities account fee, the securities transaction income, the management fee, other commission and fee income, interest income, and other income Bank (quarterly) Expenses Contains all costs that can be assigned to a client (in Swiss Francs). This includes labor costs of financial advisors and third-party charges Bank (quarterly) Securities account fee Contains the fees the client pays for the securities account (in Swiss Francs) Bank (quarterly) Securities transaction income Contains all commissions and fees from securities transactions (in Swiss Francs). This includes the commissions and fees directly charged by the bank and transaction-related payments the bank receives from product providers Bank (quarterly) Management fee Contains the fees the client pays when delegating account management to the bank (in Swiss Francs) Bank (quarterly) Other commission and fee income Contains all commissions and fees other than the securities account fee, the securities transaction income, and the management fee (in Swiss Francs). This includes recurring payments the bank receives from product providers, fees for account keeping, fees for payment transactions, and fees for credit cards Bank (quarterly) Interest income Contains the net interest income from savings, mortgages, and loans calculated according to the market interest rate method (in Swiss Francs). The market interest rate method assumes that assets and liabilities are refinanced at current market conditions Bank (quarterly) Advice characteristics Advised (d) Dummy variable that equals one for trades executed within five days of an advisory contact, that is, between t = 0 and t = 4, and zero otherwise Bank (daily) Advisor-initiated (d) Dummy variable that equals one for advised trades that follow a contact that was initiated by the advisor, and zero otherwise Bank (daily) % advised trades Number of advised trades per quarter / Total number of trades per quarter Bank (quarterly) % advisor-initiated Number of trades that follow advisor-initiated contacts per quarter / Total number of trades per quarter Bank (quarterly) Managed account (d) Dummy variable that equals one for client-quarters in which clients delegate account management to the bank, and zero otherwise Bank (quarterly) Portfolio characteristics Bank wealth Total wealth a client holds at our bank (in Swiss Francs). This position is not netted against mortgages and loans Bank (quarterly) log(bank wealth) Natural logarithm of bank wealth Bank (quarterly) Securities account Value of securities portfolio of a client (in Swiss Francs) Bank (quarterly) Savings account Amount of money a client holds in the savings account (in Swiss Francs). This does not include retirement savings Bank (quarterly) Retirement savings Amount of money a client holds in the retirement savings account (in Swiss Francs). The Swiss pension system is based on three pillars: the state pension system, occupational pension provisions, and private pension provisions. Private pension provisions typically take the form of retirement saving accounts that offer higher interest rates than normal savings accounts as well as tax benefits Bank (quarterly) Checking account Amount of money a client holds in the checking account (in Swiss Francs) Bank (quarterly) Mortgage Outstanding mortgage balance of a client (in Swiss Francs) Bank (quarterly) Loan Outstanding loan balance of a client (in Swiss Francs) Bank (quarterly) At least one trade (d) Dummy variable that equals one for client-quarters in which clients execute at least one trade, and zero otherwise Bank (quarterly) # trades Number of trades a client executes per quarter Bank (quarterly) Trading volume Value of all transactions executed by a client in a specific quarter (in Swiss Francs) Bank (quarterly) Turnover $$\frac{1}{2}$$ (Value of all purchases executed by a client in a specific quarter + Value of all sales executed by a client in a specific quarter) / Value of securities portfolio of a client at the beginning of the quarter Bank (quarterly) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Value of securities in the respective asset class (in Swiss Francs; Table 4) Bank (quarterly) % Swiss bonds % foreign bonds % Swiss stocks % foreign stocks % bank’s own funds % partner funds % other funds % derivatives % structured products Value of securities in the respective asset class / Value of securities portfolio of a client (Table 7) Bank (quarterly) Client characteristics Client male (d) Dummy variable that equals one for male clients and zero for female clients Bank (time-invariant) Client age Client’s age (in years) Bank (yearly) log(client age) Natural logarithm of client age Bank (yearly) Education Client’s education (1, unskilled; 2, semiskilled; 3, apprenticeship/ vocational education; 4, high school degree; 5, higher vocational education; 6: technical college degree; and 7, university degree) Bank (time-invariant) Employed (d) Dummy variable that equals one for employed clients, and zero otherwise Bank (time-invariant) Retired (d) Dummy variable that equals one for retired clients, and zero otherwise Bank (time-invariant) Swiss (d) Dummy variable that equals one for clients living in Switzerland and zero for clients living abroad Bank (time-invariant) Risk tolerance Client’s risk tolerance (1, low; 2, medium; and 3, high). The risk tolerance is assessed in an in-person meeting with the advisor at the start of the bank relationship based on a set of predefined questions Bank (time-invariant) Risk tolerance = 3 (d) Dummy variable that equals one for clients with the highest level of risk tolerance, and zero otherwise Bank (time-invariant) Length of relationship Number of years since account was opened (in years). This variable is missing for some clients in our sample that opened their account before December 1995. We assume that these customers opened their account in December 1995 Bank (yearly) log(length of relationship) Natural logarithm of length of relationship Bank (yearly) Advisor characteristics Advisor male (d) Dummy variable that equals one for male advisors and zero for female advisors Bank (time-invariant) Advisor age Advisor’s age (in years) Bank (yearly) log(advisor age) Natural logarithm of advisor age Bank (yearly) Senior (d) Dummy variable that equals one for advisors that have reached a certain hierarchical level within the bank, and zero otherwise Bank (time-invariant) Client-advisor matching characteristics Different age (d) Dummy variable that equals one for client-advisor pairs with an age difference of more than 10 years, and zero otherwise Bank (yearly) Client older (d) Dummy variable that equals one for client-advisor pairs where the client is more than 10 years older than the advisor, and zero otherwise Bank (yearly) Advisor older (d) Dummy variable that equals one for client-advisor pairs where the advisor is more than 10 years older than the client, and zero otherwise Bank (yearly) Trade characteristics Trade size Trade size (in Swiss Francs) Bank (daily) log(trade size) Natural logarithm of trade size Bank (daily) Swiss bond (d) Foreign bond (d) Swiss stock (d) Foreign stock (d) Bank’s own fund (d) Partner fund (d) Other fund (d) Derivative (d) Structured product (d) Dummy variable that equals one for transactions in the respective asset class, and zero otherwise (Tables 3, 5, and 6) Bank (daily) Swiss bonds Foreign bonds Swiss stocks Foreign stocks Bank’s own funds Partner funds Other funds Derivatives Structured products Size of transactions in the respective asset class (in Swiss Francs; Table 3) Bank (daily) Appendix B. Performance Calculation This appendix provides details of our approach to calculating the portfolio performance of advised and independent clients (B.1), the performance of structured product trades (B.2), and the portfolio performance of clients around switches to managed accounts (B.3). B.1 Portfolio performance of advised and independent clients To calculate the monthly portfolio performance net of trading costs, we apply the methodology of Barber and Odean (2000). We collect monthly (gross) return data on individual securities from Thomson Reuters Datastream. If no data are available from Datastream, we rely on the return information provided by our bank. For each transaction, we estimate the transaction costs as the sum of the commissions and the bid-ask spread component. While the bank provides us with the commissions, we have to estimate the bid-ask spread component. We determine the bid-ask spread component as the percentage difference between the security’s closing price on the day of the transaction and the actual transaction price. We then adjust the monthly gross returns of individual securities using the estimated transaction costs. The monthly net portfolio returns are calculated as the value-weighted average of monthly net returns of individual securities held in the portfolio using beginning-of-month portfolio holdings. Finally, we build an aggregate calendar-time portfolio consisting of all individual portfolios of advised clients and an aggregate calendar-time portfolio consisting of all individual portfolios of independent clients. Individual portfolios are value-weighted in the aggregate portfolios. When constructing the aggregate portfolio of advised clients, we only include clients as of the point in time of the first trade on advice so as to not introduce a look-ahead bias. B.2 Performance of structured product trades To determine net returns of structured product trades, we first compute the percentage change between the purchase price and the sales price. If we do not observe the sale, we assume that the client holds the product until maturity and use the last quoted price before maturity as the sales price. If the product matures outside of our investigation period, we use the last price quoted within our sample period to compute the performance. We obtain quoted prices on structured products from the SIX Swiss Exchange. If structured products pay coupons, we add accrued interest to the trade performance. We then adjust the trade performance using the commissions paid upon purchase and sale. If we do not observe the sale of a product, we assume the commissions paid when the position is closed to be the same as the commissions paid upon purchase.27 Finally, we annualize net returns of structured product trades to make the performance comparable across different holding periods. B.3 Portfolio performance of clients around switches to managed accounts To determine the monthly portfolio performance around switches to managed accounts, we employ an approach similar to that of Barber and Odean (2002), who analyze the change in performance of individual investors who switch to online trading. We use the group of 371 clients who switch to a managed account during our sample period. Since the first investors switch in the first quarter of 2002 and the last in the second quarter of 2005, our sample spans the time period from April 2002 to March 2005. Before switching, monthly net portfolio returns are calculated using the approach described in Appendix B.1. After switching, trade commissions are usually covered by the fixed management fee clients pay to the bank. Thus, to determine monthly portfolio returns net of trading costs after switching, we deduct the management fee instead of commissions.28 We then construct an aggregate calendar-time portfolio consisting of all individual portfolios of investors that eventually switch to a managed account before the switch and an aggregate calendar-time portfolio of these individual investors’ portfolios after the switch. Individual portfolios are value-weighted when forming aggregate portfolios. Footnotes 1 See Bank for International Settlements (2008) for an overview of fiduciary duties and legal requirements imposed on financial advisors across jurisdictions and across different types of financial advice. 2 Even if one expects that financial advisors put the interest of their employer first, it is an empirical question whether providing advice leads to more profits for banks, as providing advice is also costly. Banks may need to provide clients with access to financial advice in order to be able to attract customers in a competitive environment. However, if clients choose to actually take advantage of financial advice, this could still generate more costs than revenues for the bank. Our internal managerial accounting data, including the costs arising from financial advice, allow us to empirically analyze this question. 3 The extent to which financial advisors can be held liable for not meeting fiduciary or legal standards remains largely unresolved to this date. In 2012, the Swiss Federal Supreme Court ruled that a client with a managed account at a large Swiss bank is entitled to claim back payments the bank received from product providers (see, e.g., Aboulian 2012). Following this verdict, some smaller banks proactively reimbursed their managed account clients. However, larger banks argued that the vast majority of clients waived the right to receive payments from product providers in client contracts. This argument has not yet been challenged in court. 4 Our paper also provides an important micro-foundation for experimental studies. Mullainathan, Noeth, and Schoar (2012) run a field experiment and document that financial advisors promote actively managed funds that have high fees. Anagol, Cole, and Sarkar (2017) also conduct a field experiment and show that indirectly remunerated agents in the Indian life insurance market recommend products that provide high commissions to the agents. 5 In June 2005, at the end of our investigation period, the market for structured products in Switzerland amounted to CHF 172 billion (equivalent to roughly USD 134 billion), of which 46.3% were held by retail investors. 6 Shortly after the European Union, Switzerland, which is covered by our study, issued a draft of a new regulation for the financial services industry that is largely consistent with MiFID II. 7 To maintain confidentiality, the bank did not provide information on its complete customer base. 8 Hoechle et al. (2017) use a subset of the data used in this study to compare the stock-picking skills of financial advisors with the stock-picking skills of individual investors. 9 In a survey among retail customers, Chater, Inderst, and Huck (2010) document that 68% of purchasers of retail financial products act within the first three days of receiving advice and 88% act on advice received within the first two weeks. 10 Our classification of advised trades could be misleading if clients meet with advisors but then do not follow the advice they get but rather execute trades in other securities. However, Hoechle et al. (2017), using data from the same bank, show that this does not seem to be the case. 11 Management fees are fees clients pay for the management of managed accounts. They should not be confused with mutual fund management fees. If our clients hold mutual funds and have to pay management fees for them, the respective expenses are directly deducted from the fund investment. 12 In December 2003, the average net wealth of a Swiss resident subject to taxation with net wealth above CHF 50,000 is CHF 529,011 (Swiss Federal Statistic Office 2012). This also includes nonfinancial wealth. 13 Results are similar if we restrict our sample to quarters with at least one transaction. 14 Clustering standard errors at the client level results in substantially higher t-statistics, while clustering at the quarter level yields t-statistics that are similar to the t-statistics obtained from clustering at the advisor level. Even if we double-cluster at the client-advisor level, the advisor-quarter level, or the client-quarter level, our following main results remain unaffected. 15 This result is not due to own-bank mutual funds and structured products only being available for sale through bank advisors. Every security in our sample can be purchased both on advice and independently. 16 Advisors’ incentives to maximize profits provide one possible explanation for the increase in trading activity following the switch to a bank-managed account. However, the surge in trading activity also could be driven by financial advisors trading to look active and thereby trying to justify management fees (e.g., Dow and Gorton 1997; Chakrabarty, Moulton, and Trzcinka 2017). 17 Results are similar if we replicate the analysis including client and advisor characteristics rather than client-advisor fixed effects. 18 About half of the bank’s own funds are all-equity funds, and the other half are balanced funds that invest approximately 50% in equities and 50% in fixed-income securities. 19 We would like to thank an anonymous referee for suggesting this set of analyses to us. 20 In a recent study, Fisman, Paravisini, and Vig (2017) provide evidence that demographic matches between lenders and borrowers increase the likelihood that a transaction takes place and reduce the probability of default. 21 Important examples include the social identity theory (Tajfel and Turner 1986), the similarity-attraction hypothesis (Byrne 1971), the access-and-legitimacy perspective (Thomas and Ely 1996), and Becker’s (1957) theory of customer discrimination. 22 In unreported tests, we also examine the effect of gender matches on the profitability of advised trades. However, we do not find much evidence that gender matches affect profitability. 23 Results are similar (albeit weaker) when using an age difference of five years rather than 10 years to classify one party as older or younger, respectively. 24 Given these findings, it is natural to ask whether the bank actively matches older clients to younger advisors in order to maximize bank profits. However, in unreported tests, we do not find much evidence that the bank engages in age matching. Other criteria, such as clients’ wealth, clients’ past profitability, and advisors’ availability, seem to be more relevant when assigning clients to advisors. 25 We use the Swiss Performance Index (SPI) and the MSCI World Index as proxies for the equity market factor, the Swiss SMB factor, the Swiss HML factor, and the Swiss momentum factor from Andrea Frazzini’s data library (http://people.stern.nyu.edu/afrazzin/data_library.htm), the Swiss Bond Index (SBI) as proxy for the government bond market factor, and the iBoxx Euro Corporate Bond Index as proxy for the corporate bond market factor. 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