Retail Financial Advice: Does One Size Fit All? - USC

USC FBE FINANCE SEMINAR presented by Juhani Linnainmaa FRIDAY, Oct. 3, 2014 10:30 am – 12:00 pm, Room: JKP-102

Retail Financial Advice: Does One Size Fit All?∗ Stephen Foerster Juhani T. Linnainmaa Brian T. Melzer Alessandro Previtero September 2014

Abstract Using unique data on Canadian households, we assess financial advisors impact on their clients’ investment portfolios. We find that advisors induce their clients to take more risk, thereby raising clients’ expected investment returns. On the other hand, we find limited evidence that advisors personalize their recommendations: advisors direct clients into similar portfolios independent of their clients’ risk preferences and stage in the life cycle. An advisor’s own portfolio is a good predictor of what type of portfolios his or her clients hold even after controlling for investor attributes. This one-size-fits-all advice does not come cheap. The value-weighted client portfolio lags passive benchmarks by more than 2.5% per year net of fees, which suggests that the average investor gives up all of the equity premium gained through increased risk-taking.

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Stephen Foerster is with the Western University, Juhani Linnainmaa is with the University of Chicago Booth School of Business and NBER, Brian Melzer is with the Northwestern University, and Alessandro Previtero is with the Western University. We thank Shlomo Benartzi, Antonio Bernardo, Chuck Grace, Luigi Guiso, Markku Kaustia (discussant), Antoinette Schoar (discussant), Barry Scholnick, and Dick Thaler for valuable comments. We are also grateful for feedback given by seminar and conference participants at Nanyang Technological University, Singapore Management University, National University of Singapore, Rice University, Yale University, University of Washington, SUNY-Buffalo, Federal Reserve Bank of Cleveland, University of Chicago, American Economic Association 2014 meetings, University of Alberta, NBER Behavioral Economics Spring 2014 meetings, 2014 Helsinki Finance Summit, and 2014 European Household Finance Conference. We are especially grateful to the Household Finance Advisory Council members for donating data and giving generously of their time by helping us to better understand the complexity of the mutual fund industry. Zhou Chen and Brian Held provided helpful research assistance. Address correspondence to Alessandro Previtero, Western University, 1255 Western Road, London, Ontario N6G 0N1, Canada (email: [email protected]).

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Introduction

The life-cycle consumption-asset allocation problem is complex: choosing how much to consume today and how to allocate savings across risky assets requires, among other things, an understanding of risk preferences, investment horizon, and the properties of asset returns and the labor-income process. To help solve this problem, many households turn to investment advisors. In the United States, almost 40% of households that own mutual funds made purchases through an independent financial planner, and a similar proportion made purchases through a full-service investment broker (Investment Company Institute 2013). Likewise, among Canadian retail investors nearly 60% of assets are in accounts directed by financial advisors and an additional 20% of assets are managed by full-service brokers (Canadian Securities Administrators 2012). Despite widespread use of financial advisors, relatively little is known about how advisors shape their clients’ investment portfolios. In this paper, we take advantage of unique data on Canadian households to explore two dimensions through which advisors may add value: first, through their impact on clients’ willingness to take investment risk and, second, through their ability to tailor investment risk to clients’ particular circumstances as opposed to delivering one-size-fits-all portfolios. Our analysis begins by using a regulatory shock to the supply of Canadian financial advisors to measure advisors’ impact on risk-taking. Household survey data show a strong correlation between portfolio risk and use of an advisor in Canada—advised households allocate more of their portfolio to risky financial assets (at the expense of low-risk cash and bond holdings)—but also indicate substantial sample selection, as advised clients are older, more educated and earn higher salaries. Thus, the disparity in portfolio risk between advised and unadvised households likely provides a biased measure of advisors’ impact, one that is confounded by unobserved differences in, for example, risk tolerance. To resolve this selection problem, we exploit a 2001 regulatory change that imposed financial reporting and capital requirements on Canadian financial advisors operating outside of Quebec. The goal of our empirical strategy is to identify the causal role of financial advice by isolating a shock to the supply of financial advisors that is unrelated to demand for advice. Using a differencesin-differences model to compare affected households to those in Quebec, we find that the regulatory

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change reduced households’ likelihood of using an advisor by roughly 10%. Exploiting this variation within an instrumental variables model, we estimate that financial advisors increase the marginal households’ risky asset share by 40 percentage points. This estimate of advisors’ influence on risky asset shares exceeds that obtained from least squares regressions that control for observable differences. This finding suggests that advisors facilitate greater financial market participation and risk-taking, perhaps by reducing households’ uncertainty about future returns or by relieving households’ anxiety when taking financial risk. Next, we delve deeper into advisors’ impact on risk-taking by examining the portfolios held by advised households. Taking advantage of uniquely detailed and extensive data furnished by three large Canadian financial institutions, we ask whether advisors tailor their advice on portfolio risk. The data include transaction-level records on nearly 10,000 financial advisors and these advisors’ 750,000 clients, along with demographic information on both investors and advisors. Most importantly, the data include many variables—such as risk tolerance, age, investment horizon, income and financial knowledge—that one would expect to be of first-order importance in determining the appropriate allocation to risky assets. What determines cross-sectional variation in investors’ exposure to risk? In neoclassical portfolio theory, differences in risk aversion account entirely for variation in risky shares. In richer classes of models, many other factors also shape investors’ optimal risk exposures: an investor’s stage in the life cycle, volatility of labor income, correlation between labor income and the stock market, differences in beliefs about expected return, return predictability and risk, and so forth. A more risk-averse investor should invest less in risky assets than a less risk-averse investor; young investors should hold riskier portfolios (in most models); and investors whose labor income varies in tandem with the stock market should invest less in risky assets. Studying cross-sectional variation in risky shares across investors, we test whether advisors adjust portfolios in response to such factors. We find that advisors tailor portfolios based on client characteristics, with an investor’s risk tolerance and point in the life cycle being particularly important. As one would expect, more risk-tolerant clients hold riskier portfolios: the least risk tolerant allocate, on average, 50% of their portfolio to risky assets, while the most risk tolerant allocate 75%. Controlling for risk tolerance, the risky share also declines with age, peaking before age 40 and declining as retirement approaches. While risk-taking peaks at the same age as in a life-cycle fund, the risky share of advised clients 3

otherwise differs substantially from the pattern in a life-cycle fund: among advised clients, the risky share peaks at 75% among investors aged 35 to 39 and declines by 5 to 10 percentage points as investors reach retirement age, whereas in life-cycle funds, the risky share peaks at 90% and declines by 40 to 50 percentage points for those of retirement age. The most striking finding from our analysis of portfolio allocations, however, is that clients’ observable characteristics jointly explain only 11% of the variation in risky share in the crosssection of investors. That is, although differences in risk tolerance and age translate into significant differences in average risky shares, a remarkable amount of variation in portfolio risk remains unexplained. In contrast, we find that advisor fixed effects have substantial explanatory power. Advisor fixed effects raise the model’s R2 threefold, from 11% to 31%, meaning that advisor fixed effects explain almost twice as much variation in risky share as explained by the full set of client characteristics. The advisor effects are also economically significant: controlling for investor attributes, a one standard deviation movement in the advisor distribution corresponds to a 17-percentage point change in the risky share. One interpretation of this finding is that, instead of customizing, advisors build very similar portfolios for all of their clients. Another interpretation is that matching between investors and advisors leads to common variation in risky share among investors of the same advisor; in that case, advisor fixed effects stand in for omitted client characteristics that are common across investors of the same advisor. We find little support for the latter hypothesis. Our data include investor identifiers that allow us to track investors that switch advisors. For this subset of investors (and their associated advisors) we can estimate both advisor fixed effects and investor fixed effects, the latter controlling flexibly for any unobserved persistent differences across investors and the former capturing advisor-specific input. In these models, advisor fixed effects continue to be pivotal, as they explain an equally large share of the variation in risky share as do investor fixed effects. If advisors do not base their advice on investor characteristics, what explains variation in portfolios across advisors? We find that advisors may project their own preferences and beliefs onto their clients. A unique feature of our data is that we observe the portfolio allocations for advisors who maintain investment portfolios at their own firm (nearly 60% of advisors in our sample do so). For these advisors, we find that their own risk-taking is far and away the strongest predictor of the 4

risk taken in their clients’ portfolios even after controlling for advisor and client characteristics. The picture that emerges here is that no matter what a client looks like, the advisor views the client as sharing his/her preferences and life situation. We find, in addition, that older advisors recommend substantially riskier portfolios. Although there may be other explanations for this pattern, one explanation is that agency conflicts can worsen as career horizons shorten. Given that advisors do not provide completely customized advice, the puzzle in this market is the high cost of advice. Including all management fees and loads paid to advisors and mutual funds, we find that the average client pays at least 2.5% per year. Since advisors do not add value through superior investment recommendations—there is no evidence of skill in the distribution of gross alphas—investors’ net underperformance equals the fees they pay. Accounting for an equity premium of, say, 6% per year and our earlier finding that advisors raise their clients’ allocation to risky assets by 40 percentage points, we estimate that households gain 2.4% per year, on average, from using an advisor. That is, the average investor gives up her entire incremental investment return through fees paid for advice and money management. This finding is reminiscent of Berk and Green’s (2004) competitive mutual fund industry, in which capital flows in and out mutual funds exactly to such an extent that even though managers have skill, in expectation none of that skill gets passed on to investors in the form of positive alphas. To be clear, our result does not imply that clients do not benefit from using financial advisors, only that investment advice alone does not seem to justify the fees paid to advisors. In particular, our analysis does not account for value provided through broader financial planning, including retirement, tax and estate planning. Our paper contributes to the empirical literature on the quality of financial advice. Bergstresser, Chalmers, and Tufano (2009) provide indirect evidence that advised clients earn poor returns by documenting substantial underperformance of mutual funds sold exclusively by brokers or advisors. Yet their study lacks specific data on financial advisory portfolios, which prevents them from precisely quantifying the underperformance of advised clients and examining the extent to which advisors’ shape their clients portfolios in response to factors such as risk tolerance and stage in the lifecycle. Mullainathan, Noeth, and Schoar (2012) evaluate the quality of advisors’ recommendations in the context of a field experiment, and find evidence of poor advice; advisors encourage “return chasing” and also direct clients toward higher cost, actively managed funds. Finally, Chalmers and Reuter (2013) find that Oregon University retirement plan participants opting for 5

financial advice underperform both passive investment benchmarks and the returns of self-directed plan participants. Qualitatively, our findings are similar to these studies, but an important contribution of our analysis is to provide evidence on a large and representative group of advised clients. The rest of the paper proceeds as follows. Section 2 provides background on the Canadian retail investment industry. In Section 3 we use survey data to investigate advisors’ effect on their clients’ risk-taking. Sections 4 and 5 describe our administrative data on client accounts, and present analysis of risk-taking, investment performance and the cost of advice. Section 6 concludes.

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The Retail Investment Industry in Canada

Canadian households purchase investment products and services through five main channels, three of which involve financial advice and two of which are client-directed. By far the most common choice is to invest with the help of an advisor: out of $876 billion of retail investment assets as of year-end 2010, roughly 80% are in accounts directed by an advisor (Canadian Securities Administrators 2012).1 Non-bank financial advisors, which are the subject of our study, account for the largest portion of retail assets—$390 billion, or 44% of total assets.2 Among advisors the services vary, but the core services offered by all advisors are financial planning and investment advice. As part of financial planning, advisors help clients formulate retirement and education savings plans, first and foremost, but also arrange mortgage loans and provide insurance and estate planning in some cases. Within the scope of investment advice, advisors offer guidance on asset allocation and investment selection, and execute trades on their clients’ behalf. For the accounts in our sample, discretionary trading by the advisor is not permitted; each trade must be initiated or approved by the client. The range of investment products sold by an advisor depends on his securities licenses. Our analysis focuses exclusively on advisors that are licensed as mutual fund dealers, a designation which permits sale of mutual fund and deposit products, but precludes sale of individual securities 1

The two non-advisory channels, bank branch sales and self-directed accounts (including discount brokerage and mutual fund direct sales), account for 11% and 7% of retail investment assets, respectively. 2 The other two types of advisors, for which we do not have data, are full-service brokers, who oversee 20% of retail assets, and financial advisors within bank branches, who oversee 17% of retail assets.

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and derivatives.3 In addition to being licensed to sell mutual funds, some financial advisors in our sample also have licenses to sell segregated funds, labor funds, and principal protected notes.4 Financial advisors who are licensed to distribute mutual funds in Canada do so through one of two self-regulatory organizations. The first, Mutual Fund Dealers Association (MFDA), supervised 80,132 advisors at the year-end 2010 and these advisors had a combined $271 billion in assets under advisement. The second regulator, Investment Industry Regulatory Organization of Canada (IIROC), supervised 28,598 advisors. The combined number of financial advisors in Canada licensed to distribute mutual funds is thus 108,730. Under Canadian securities legislation, advisors have a duty to make suitable investment recommendations, based on their clients’ investment goals and risk tolerance. To that end, advisors are required to conduct “Know Your Client” surveys with each client at account origination and annually thereafter. The extent of advisors’ fiduciary duty, however, is a gray area; it is not clear that they are required to put the client’s interests before their own, though they are legally mandated to deal fairly, honestly and in good faith with their clients (Canadian Securities Administrators, 2012). This gray area is important, given the potential for agency conflicts between advisors and their clients. Agency conflicts are a concern due to the compensation scheme for advisors. Most commonly, clients pay no direct compensation to advisors for their services. Rather, the advisor earns commissions from the investment funds in which his client invests, raising the possibility that their investment recommendations are biased toward funds that pay larger commissions without providing clients better investment returns. The size and source of these commission payments vary depending on the asset class and load structure of the mutual fund purchased by the client. Commission payments are lowest on money market funds and highest on balanced funds and equity funds, which potentially skews advisor recommendations toward riskier funds. Across load structures, commissions are lower, on average, for no-load funds and higher for load funds. For no-load funds, which are so-named because the investor pays no explicit commission on purchases and redemptions, the advisor still collects a 3 Full-service brokers, who are not represented in our sample, offer access to the widest range of investment products—individual securities as well as mutual funds and deposit products—by virtue of being licensed as investment dealers and mutual fund dealers. 4 Segregated funds are variable life insurance contracts that reimburse capital upon death. Labor funds are funds that direct (venture capital) investments to small non-public firms.

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trailing commission of up to 1% per year from the mutual fund as long as the client remains invested. For funds with a back-end load, the investor pays a fee to the mutual fund company at the time of redemption—typically, the redemption fee declines with horizon, starting at 6% within one year of purchase and declining to zero after 5 to 7 years. The advisor, in turn, is paid by the mutual fund company in the form of a payment at the time of purchase (typically 5% of the purchase amount) as well as a trailing commission (typically 0.5% annualized) as long as the client remains invested. Finally, on purchases of front-end load funds, the advisor receives an upfront sales commission directly from the client (up to 5% of the purchase amount, but negotiable between the investor and advisor), along with a trailing commission paid by the fund company (up to 1% per year while the client remains invested). While the exact source of the commissions varies, ultimately these payments are funded by clients, whether directly or indirectly through management and operating expenses deducted from their fund investments. After summing up these commission payments and deducting the typical share of commissions (20%) that go to their employer, the average advisor in our sample earns revenue of $80,000 to $120,000 per year.

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The Effect of Financial Advisors on Risk-taking: Evidence from Survey Data

In this section, we use the Canadian Financial Monitor (CFM), a household survey covering both advised and unadvised households, to evaluate the impact of financial advisors on their clients’ risk-taking. Ipsos-Reid, a survey and market research firm, designed the CFM survey and collected the data through monthly interviews of approximately 1,000 households per month between January 1999 and June 2013.5 In addition to providing a wealth of demographic information, each interview measures households’ stock and mutual fund holdings, and their asset allocation and savings decisions. Most importantly for our analysis, the survey collects also information on the use of financial advisors. Table 1 displays descriptive statistics for Canadian households, stratified by use of a financial 5 The data are structured as a repeated cross-section, but some households do participate repeatedly, so the total number of observations (175,000) exceeds the number of unique households (79,600).

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advisor. Advised households are on average four years older (56.3 vs. 52.4), 7.5 percentage points more likely to be retired (34.7 vs. 27.2), and 14 percentage points more likely to have either a college or graduate degree (69.0% vs. 54.9%). From a financial standpoint, advised household also have higher average incomes (CND $74,900 vs. 52,400), substantially higher net worth (CND $472,300 vs. 212,500) and more financial assets (CND $221,100 vs. 71,600). Last, households that use financial advisors invest more in equity (15% vs. 6.9% of financial assets), more in mutual funds (39.7% vs. 12.7%) and less in fixed income products (45.2% vs. 80.4%). These summary statistics indicate that advised households shift their portfolio allocation away from safer cash and fixed income assets to riskier equity and mutual fund assets. Given the substantial differences in other characteristics, such as income and wealth, however, it is unclear whether these differences arise due to client preference or due to advisor input. This is a fundamental challenge in measuring the impact of financial advisors: the demand for advisory services will depend on the outcomes of interest, such as the participation in risky asset markets. For example, if riskaverse individuals perceive less benefit from investment advice on mutual funds because they prefer not to own risky assets, they will be less likely to work with an advisor. This pattern in advisor selection would cause a downward bias when estimating advisors’ impact on risky asset shares by comparing advised and unadvised households. We address this identification issue by using a regulatory change in the early 2000s that reduced the supply of financial advisors. Specifically, as of February 2001 mutual fund dealers and their agents, such as financial advisors, were required to register with the Mutual Fund Dealers Association of Canada (MFDA) and follow the rules and regulations of the MFDA. The introduction of this registration requirement meant that dealers who wished to remain in business were now subject to more stringent regulatory standards, including minimum capital levels as well as audit and financial reporting requirements. For the underlying advisors, the registration requirement also mandated securities training and established a basic standard of conduct.6 The draft rules and bylaws were originally posted for comment on June 16, 2000. An overview of public comments given by dealers and advisors in response to the draft proposal reveals particular concern about costs imposed by the requirement, including compliance costs associated with financial reporting 6

The standard of conduct is quite broad, prescribing that advisors “deal fairly, honestly and in good faith” with clients, “observe high standards of ethics” in their business transactions and not engage in conduct detrimental to the public interest.

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and capital costs created by minimum capital standards. To the extent that these changes reduced the supply of advisors, they are useful in identifying a change in households’ use of advisors that is unrelated to their demand for advisory services. Importantly, the regulatory change did not apply to dealers and advisors in the province of Quebec, allowing us to use Quebec residents as a baseline from which to measure the impact of the registration requirement over time. We assess the impact of the registration requirement through the following differences-indifferences model: yipt = α + βRegisterp ∗ Postt + γRegisterp + δPostt + θXit + εipt ,

(1)

in which subscripts i, p, and t index households, provinces, and months between January 1999 and January 2004, respectively. The variable Post is an indicator that takes the value of one for dates after June 2000, when the registration requirement was announced and draft rules were published for comment. Register is an indicator variable that takes the value of one for households located in provinces that faced the registration requirement. Through β, the coefficient on the interaction of Register and Post, we measure the impact of the registration requirement over time, taking changes in Quebec as a baseline from which to measure this effect. The vector Xit contains household-level controls for income, education, age and retirement status, each of which is predictive of household demand for advisory services.7 In some versions of the model we include province and month fixed effects to control more flexibly for differences over time and across provinces. To estimate the model we use weighted least squares, incorporating survey weights from the CFM to provide regression estimates that reflect a nationally representative sample, and cluster the observations by province in calculating Huber-White standard errors. First, we estimate the impact of the registration requirement on households’ use of financial advisors. Table 2 Panel A reports the regression estimates from three models in which the dependent variable is an indicator variable that takes the value of one for households who use a financial advisor at the time of the survey. The baseline probability of using an advisor in these 1999-2004 surveys is 7 Ipsos-Reid codes household income as a categorical variable, and we use indicator variables that represent these categories as controls. We control flexibly for the age of the head of household with indicator variables for 16 fiveyear age bins covering ages 20 to 100. We code education based on the maximum level of education of the head of household and spouse, and include indicators for each of four categories: high school diploma or less, some college, college degree, and graduate degree.

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0.38. The estimates in the three models, which differ in terms of the inclusion of household controls and fixed effects, suggest that the registration requirement had both a statistically and economically significant effect on the use of financial advisors. The point estimates in the three models place the marginal effect between −0.043 and −0.040, which translate into a proportional decrease of approximately 11% in the use of financial advisors. In the first model, which excludes household controls, the coefficient on the registration-requirement indicator is positive and marginally significant at the 10% level, indicating that before the law change residents of Quebec are less likely to use advisors than their counterparts in provinces subject to the registration requirement. However, this disparity is entirely explained by differences in income and demographics; the coefficient on Register is very close to zero once household-level controls are added to the model. This evidence helps support our premise that, after controlling for observable differences, Quebec residents can serve as an reasonable baseline from which to measure the change in advisor usage. The substantial increase in R2 induced by the inclusion of these controls shows that income, education, age, and retirement status indeed substantially correlate with the demand for advisory services. Next, we use the variation documented above to estimate the effect that financial advisors on households’ financial choices in a two-stage least squares model: Use Advisoript = α + βRegisterp ∗ Postt + ηp + Ψt + θXit + εipt ,

(2)

Advisoript + ηp0 + Ψ0t + θ0 Xit + ε0ipt . yipt = α0 + β 0 Use\

(3)

Each regression includes both household-level controls as well as province and month fixed effects. The first stage provides an estimate of each household’s predicted probability of using an advisor (Use\ Advisoript ), allowing for variation due to the Register -Post instrumental variable, and the second stage uses this predicted probability to provide an estimate of advisors’ impact on risktaking and, in a income. The estimates from this instrument variables analysis, which are shown in Table 2 Panel B, are consistent with financial advisors affecting risk-taking. A household’s likelihood of owning any risky assets (stocks and mutual funds) increases by 0.67, or 67 percentage points, with the use of an advisor, and the proportion of risky assets in the portfolio increases by 0.39. In each case, the IV estimate exceeds the OLS estimate, which suggests downward bias in the OLS estimate, perhaps

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because individuals that are comfortable holding risky assets are less likely to solicit an advisor’s input. To provide validation that the registration requirement captures a supply shock that is unrelated to demand for advisors, we also test for a correlation between household income and use of an advisor. OLS analysis reveals that high-income households are significantly more likely to use financial advisors: there is a large positive OLS coefficient in regression of log income on Use Advisor ).8 Since there is no obvious channel through which financial advisors should causally influence household earnings, this correlation likely stems from differences in demand for advisors. Once we instrument for use of an advisor with the registration requirement, we indeed find no significant relationship between log-income and households’ use of financial advisors, which provides further comfort that the registration requirement leads to changes the supply of advisors while leaving key demand-side factors unchanged.

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Analysis of Dealer Data: Portfolio Choices

4.1

Description of the data

In the balance of the paper we use detailed, transaction-level data on the portfolios of advised clients to measure the extent to which advisors shape their clients’ portfolio choices over and above investor demographics, and to measure the costs of financial advice. Three large Canadian financial advisory firms supplied the data for our study. Each firm provided a full history of client transactions over a 10-year period, from 2001 to 2010, along with background information on clients and advisors. The total value of assets under advice at the end of this period was $30.9 billion, representing 11% of the assets of Mutual Fund Dealers (MFDs). Key summary statistics of these data are provided in Table 3. Table 3 Panel A describes the investor side of the sample and shows that our data cover a broad swath of different types of investors both in terms of their demographics, the length of the investor-advisor relationship, risk tolerance, financial knowledge, income, and wealth. Across the entire sample, we have data on 748,287 investors with 1.5 million accounts; 86% of these investors were active as of year-end 2010. Men and women are equally presented in the data. The median 8

This specification excludes the income controls.

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investor in the data is 49 years old, and the 10th and 90th percentiles of the age distribution are 32 and 68 years. The data display considerable heterogeneity with respect to how long an investor has known his or her advisor. At the end of the sample period, over 10 percent of investors had been in the client-advisor relation for less than a year (row “investor known since”); and at the end other end of the spectrum, investors in the 90th percentile of the distribution had known their advisors for at least 7 years. The first two blocks at the bottom of Panel A detail the distributions of account types and investment horizons. One-fifth of the accounts are unrestricted general-purpose accounts; 71% of accounts are classified as either retirement savings or retirement income accounts that receive favorable tax treatment comparable to the 401(k) plans in the U.S.; 4% of the accounts are education savings plans; and the remaining 4% are tax-exempt accounts that face restrictions on how much money can funds can be invested and withdrawn. The data include both open and closed accounts. As of the end-year 2010, 44% of the accounts were active; the others were either inactive or had been closed. Panel C’s bottom block tabulates the self-reported time horizons of the accounts. The typical investment horizon—reported for 63% of the accounts for which this information is supplied—is six to ten years. One-fifth of accounts are associated with reported investment horizons greater than ten years, and the remaining 15% of accounts have shorter investment horizons. There are some very-short-term accounts as well. Some 3% of the accounts—or just over 30,000 accounts— are associated with an investment horizon shorter than a year. The remaining blocks at the bottom of Panel A describe investors’ responses to questions about their risk tolerance, financial knowledge, net worth, and income from “Know Your Client” forms. Financial advisors collect this information at the start of the advisor-client relationship and at the time they create new accounts for their existing clients. These investor attributes display considerable heterogeneity. The most common level of risk tolerance is the second highest at 61%. Just 13% of investors tolerate low or very low levels of risk. Two-fifths of investors report low financial knowledge and only 6% of investors report having high financial knowledge. More than half of investors report a net worth of over $200k while 20% report a net worth of $50k or less. Annual salaries display dispersion similar to that observed in net worth: 27% of investors report an annual salary of less than $30k, and 10% of investors earn more than $100k per year. Table 3 Panel B shows summary statistics for the advisors in our sample. The median advisor’s 13

age equals that of the median investor at 50 years, and the 10th and 90th percentiles are 36 years and 63 years. Advisors differ significantly from each other in terms of their experience, the number of clients they advise, and how many and what types of licenses they have. Over 10% of advisors have been in the job for less than a year, and another 10% have at least 8 years of experience. While the median advisor advices 18 clients with a total of 29 accounts, these numbers are very different at the 10th and 90th percentiles: advisors in the bottom decile have just one client with two accounts; those in the top decile have over 200 clients with more than 400 accounts. Over a quarter of advisors have just one license—either the mutual fund or dual (mutual fund and life insurance) license— and just over 10% have three or more licenses. The most common non-mutual fund license is the segregated-funds license. This license allows the advisor to sell variable life insurance contracts.9 Just every tenth and every fifth advisor have licenses to sell principal protected notes and laborsponsored funds, the latter of which are mutual fund-style vehicles investing primarily in private companies.

4.2

Portfolio choice, investor attributes, and advisor fixed effects

In this section we study cross-sectional variation in investors’ portfolios and measure the extent to which advisors adjust their clients’ portfolios in response to these factors. The dealer data contain information on many variables that one would expect to be of first-order importance in explaining cross-sectional differences in investors’ willingness to assume equity risk. For example, although many factors may influence portfolio decisions, we would still expect risk tolerance to play a dominant role. Our analysis here proceeds in three stages. We first estimate regressions that explain cross-sectional variation in investors’ portfolios with investor attributes and advisor fixed effects. We then focus on a subset of investors who move across advisors to estimate models with both investor and advisor fixed effects. These regressions speak to the possible role of any omitted investor attributes as well as to the nature of the mechanism that matches investors and advisors. After establishing that advisor fixed effects are important determinants of portfolio choice over and above all investor-specific effects, we ask whether advisor attributes such as age and gender explain why some advisors give their clients far riskier or safer portfolios than others. 9

The name “segregated fund” comes from the requirement in the Canadian law that the funds invested in these vehicles to be separated from the company’s general investment funds.

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Table 4 presents estimates from regressions that explain cross-sectional variation in investors’ risky shares (Panel A) and home bias (Panel B). Risky share is the fraction of wealth invested in equity and home bias is the fraction of equity invested in Canadian companies.10 The first column reports estimates from a panel regression against year fixed effects and a large swath of demographic variables summarized in Table 3. The unit of observation in these regressions is an investor-year and we cluster errors by advisor to account for arbitrary correlations in errors over time and between investors who share an advisor. The intercept of this regression, 43.2%, is the average risky share of such an investor at the year-end of 1999 who is in the lowest (omitted) category for every variable—that is, an investor between 21 and 24 years of age, male, very low risk tolerance, and so forth. We exclude from the analysis clients who are advisors themselves—we describe and utilize this information in Section 4.5. Risk tolerance stands out in the first regression for its statistical and economic significance in explaining cross-sectional variation in risky shares. Investors in the second lowest risk-tolerance category invest 10 percentage points more in equities than those in the lowest category, and those in the top two categories hold between 24 and 27 percentage points higher risky shares. Many other regressors are also statistically highly significant with signs typical to the literature. The age profile, for example, is hump-shaped, with investors aged between 35 and 39 having the highest risky shares. Figure 1 illustrates the age and risk tolerance profiles in the data. The thick line shows, for reference, the age profile used in Vanguard’s target-date funds—but all firms’ reference target-date portfolios are very similar. These target-date funds invest 90% in equities for investors up to the 40-year mark and then approximately linearly decrease equity exposure so that it falls to around 50% around the expected retirement date of 65. The risky-share profiles in the dealer data are very different from those of target-date funds. All investors, independent of their risk tolerance, assume too little equity exposure relative to the Vanguard benchmark when they are young and too much when they are old. The remaining regressors in Table 4 show that women’s risky shares—controlling for other demographics such as risk tolerance—are on average 9 percentage points below those of men. 10 We assume that an all-equity fund invests 100% in equities, a balanced fund invests 50% in equities, and a fixed-income fund invests nothing in equities. We compute each investor’s risky share and home bias by taking the market value-weighted average of the funds the investor holds. We set the home-bias measure to missing for those observations in which the investor has no equity exposure.

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Investors with longer investment horizons assume substantially more equity risk than those with short horizons. Investors who report higher levels of financial knowledge hold slightly higher risky shares—between 3 and 4 percentage more than low-knowledge investors. Investors with higher levels of income and wealth have higher risky shares relative to investors in the lowest categories, but these effects are not economically as impressive as those on the other regressors. That is, after accounting for all other investor attributes, wealth and income contribute only modestly in explaining cross-sectional variation in risky shares. The most striking fact about the first regression is that all the regressors in the model—there are 28 variables in the model in all if not counting the year fixed effects—jointly explain just onetenth of the cross-sectional variation in risky shares. That is, although differences in risk tolerance translate to significant differences in average risky shares, a remarkable amount of variation remains unexplained. The low explanatory power is even sharper in Panel B’s home-bias regressions. The same set of regressors yields an adjusted R2 of just 1.7% and, although some coefficients are statistically significant in isolation, no clear age, risk-tolerance, or investment-horizon patterns are apparent in the data. The lack of explanatory power in this regression is perhaps not surprising. Unlike the optimal risky share, the optimal mix of domestic and international equities should be invariant to investor-level attributes.11 Any variation in home bias must arise from differences in beliefs, transaction costs, or other frictions. The second regression in Table 4 modifies the first by adding advisor fixed effects for the 4,984 distinct advisors who serve the 765,483 investors in the data. The inclusion of these fixed effects addresses the possibility that advisors have an influence on investors’ portfolio choices over and above the variation induced by heterogeneity in (observable) investor attributes. That is, this regression measures the extent to which some advisors give systematically higher risky shares to all their clients while other advisors give their clients lower risky shares. The data reveal remarkably powerful advisor effects. The adjusted R2 in Panel A nearly triples from 11.0% to 30.8% as we add the advisor fixed effects. In Panel B’s home-bias regression the adjusted R2 increases from 1.7% to 26.4%! 11

In a model in which labor income correlates with asset returns, the optimal mix of domestic and international equities would vary in the cross-section of investors if there is variation in how investors’ labor incomes correlate with domestic and international equities. The analysis in section 4.4 addresses the role of any omitted variables such as this correlation.

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Figure 2 plots the distributions of the advisor fixed effects from the regressions presented in column 2 of Panels A and B. The standard deviations of these distributions are 17.1% and 24.6%. These distributions illustrate that, in addition to being statistically very important in explaining cross-sectional variation in portfolio choices, the advisor fixed effects are economically important. To put this result into perspective, for the share of risky assets we observe that a one-standard deviation movement in the advisor distribution is approximately equal to changing an investor’s risk tolerance from low to high! It is important to emphasize that the fixed-effect estimates are orthogonal to the investor attributes of column 2. That is, they measure differences in risky share and home bias after accounting for variation originating from differences in age, gender, risk tolerance, and so forth.

4.3

Interpreting advisor fixed effects

How should we interpret our finding that advisor fixed effects explain cross-sectional variation in portfolio choices? We can delineate two potential explanations. First, advisors may have idiosyncratic “tastes” in portfolio allocation. These tastes may reflect advisors’ personal beliefs—for example, “equities are relatively safe in the long run and offer a very attractive return-to-risk trade-off”—or they may arise from agency conflicts—some advisors may respond more to financial incentives, recommending higher-commission equity funds over cheaper fixed-income funds. Second, advisor fixed effects may appear to be important because of matching between advisors and investors. If investors match with advisors who share their beliefs and preferences, then advisor fixed effects will capture common variation in portfolio choices due to shared beliefs among clients rather than the advisor’s common influence across clients. We test directly for the importance of omitted investor attributes in section 4.4. Before describing that analysis, however, we should first observe that the results in Table 4 cast some doubt on the matching explanation. First, although it is possible that we are missing out on some determinants of optimal portfolio choice—we, for example, lack data on the correlation between labor income and market returns— we measure and control for a number of important attributes. If some investor attributes are to explain differences in equity allocation, we would expect risk tolerance, financial knowledge, age, and wealth to be at the top of the list. Nevertheless, these variables jointly explain just one-tenth 17

of the variation in risk shares and less than 2% of the variation in home bias. We would need to find variables that are important determinants of portfolio choice and that are also substantial drivers of the advisor-investor match. Second, when we include advisor fixed effects, moving from the first regression to the second in Table 4, we estimate similar coefficients on the investor attributes and we estimate those coefficients with markedly more precision. These findings imply little colinearity between investor attributes and advisor fixed effects, which means that if investors and advisors are matched by shared attributes that determine portfolio allocations, these attributes must be largely unrelated to age, gender, risk tolerance, and financial knowledge. If the matching relates, at least in part, to the variables included in the model, then the advisor fixed effects—perfect proxies for the shared link—would kill the statistical significance of the imperfect empirical proxy such as age or gender. This argument is intuitive if we think of running the regression in two stages. Suppose that we first “clean” the data by regressing risky share only on advisor fixed effects. Column 2’s estimates show that if we now collect the residuals from such a first-stage regression and run them against investor attributes, many attributes are statistically more significant in the residual data relative to the raw data. That is, the variation in risky shares that emanates from advisor fixed effects is mostly noise when studied from the vantage point of investor attributes. The attenuation of the income and net worth dummies when moving from the first regression to the second offers some limited evidence that these variables may influence both portfolio choices and advisor-investor matching—the advisory market appears to be segmented based on client wealth. Overall, however, the slope estimates are remarkably similar between the regressions in columns 1 and 2. Figure 3 illustrates this point by plotting the marginal effects associated with the age and risk-tolerance categories with and without advisor fixed effects. Although these results do not rule out the possibility of important omitted variables that drive both the portfolio choice and the investor-advisor match, they substantially narrow down the set of potential variables that could be at work. Third, the last two regressions in Table 4 show that advisor fixed effects are equally important whether an advisor serves a diverse or an homogeneous group of clients. We divide advisors into high- and low-dispersion groups based on the estimated client-base heterogeneity. We measure heterogeneity each year by recording the predicted values from the first column’s regression and 18

then computing within-advisor standard deviations of these predicted values. Advisors in the lowdispersion group have homogeneous client bases. By construction their clients have such observable attributes that the first column’s model predicts similar portfolio allocations. Advisors in the highdispersion group, by contrast, have more heterogeneous client bases for which the first column’s model predicts substantial variation among portfolios. If the increase in adjusted R2 when we add the advisor fixed effects is due to omitted variables, we would expect advisor fixed effects to play a far smaller role in the sample of high-dispersion advisors. In the data, however, the overall explanatory power of the model is largely insensitive to this grouping; the change in the adjusted R2 when adding advisor fixed effects is similar for both homogeneous and diverse client groups.

4.4

Selection of new funds with two-way fixed effects

The results of Table 4 suggest that advisors have approximately twice as large an effect on the risky share as the set of investor characteristics. In terms of the home bias, the effect is even larger: the explanatory power of the model increases from close to zero to one-quarter when we add advisor fixed effects. Moreover, we find little evidence that advisor fixed effects relflect clientadvisor matching on observable characteristics. The possibility remains, however, that the model omits an important characteristic. To address concerns about such unobservable characteristics, we use a subset of the data to control for unobserved heterogeneity among investors and thereby disentangle investor effects from advisor effects. We estimate panel regressions of the form, yiat = µi + µa + µt + εiat ,

(4)

in which yiat is investor i’s risky share or home bias in year t and µi , µa , and µt represent investor, advisor, and year fixed effects. To identify separate investor and advisor fixed effects, we must observe portfolio choices for investors who use multiple advisors during the sample period. If each investor uses a single advisor during the sample period, such an investor alone cannot be used to identify separately the investor and advisor fixed effects. But if we have a sufficient number of investors who move across advisors, we can use these movers to identify fixed effects in both dimensions. The presence of such movers, however, makes also non-movers useful: we can make

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inferences about a non-mover’s fixed effect if that investor is paired with an advisor who is associated with at least one mover. This estimation approach originates in Abowd, Kramarz, and Margolis (1999), who use it to disentangle the firm and employee effects on wages. Graham, Li, and Qiu (2012) bring it to the finance literature to disentangle the roles that firm and manager effects play in executive compensation.12 We construct the sample for estimating regression (4) by first identifying investors who change advisors at least once during the sample period. We exclude cases in which the investor initiates the switch and instead focus on the subset of switches caused by the initial advisor’s disappearance from our sample due to retirement, death, or withdrawal from the advisory business. We infer these disappearances by recording an investor’s move from advisor A to advisor B only if advisor A stops advising all of his or her clients within one year of the date of the move. That is, we require that advisor A has to disappear from the data completely. After generating a list of investors who complete at least one move from advisor to another, we create another list of all advisors who are ever associated with these investors. In the final stage of sample construction we collect data also on those non-movers who are, at any point, associated with any of the advisors on this second list. Instead of studying portfolio-level risky share and home bias within this sample—as we did in Table 4—we study the average risky share and home bias for the flow of new investments made while paired with the current advisor. The concern with the portfolio-level measures is that the stock of assets changes slowly: when an investor moves from one advisor to another, that advisor may not “reset” the investor’s portfolio overnight. An investor, for example, may be “locked in” to some investments through back-end loads on redemptions within seven years of purchase. Focusing instead on the flow of new investments allows us to judge more clearly the current advisor’s input to the portfolio. After computing the average risky share and home bias of new funds for each investor-advisor pair, we regress those outcomes on investor and advisor fixed effects. The first two columns in Table 5 replicate the regressions from Table 4 using this alternative sample. The coefficient patterns are similar, which reassures us that this subset of investors does not differ from the main sample. The decrease in sample size, of course, reduces the precision of 12 The early research on models with high-dimensional fixed effects, such as Abowd, Kramarz, and Margolis (1999), relied on approximate solutions due to constraints imposed by substantial memory requirements. Modern techniques for solving these models use memory-saving iterative techniques that can be iterated arbitrarily close to the exact solution. These techniques are now pre-packaged for the major statistical softwares. In Stata, for example, felsdvreg and reg2hdfe are available for efficiently solving models with two high-dimensional fixed effects.

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the slope estimates. The explanatory power of the model without advisor fixed effects is similar to that in Table 4. In Table 5 Panel A the adjusted R2 is 10.4%, whereas in Table 4’s full-sample regression with portfolio-level risky share that number is 11.0%. In Panel B the explanatory power is 2.0% versus 1.7% in Table 4. As in the earlier analysis, the model’s explanatory power increases substantially when we include advisor fixed effects. Table 5’s rightmost regression replaces observable investor attributes with investor fixed effects. Although investor age varies over the sample period, we omit age because it is not possible to identify year, investor, and age effects. Intuitively, investor fixed effects reveal—among all other things!—each investor’s birth year, and the birth year together with year fixed effects equal age.13 The explanatory power of the model is invariant to whether we include year fixed effects or the full set of age indicator variables. In the risky-share regression, the explanatory power of the model increases from 40.7% to 49.0% as we swap out observable investor attributes for investor fixed effects. The estimates in the last column of Table 5 suggest that advisor effects contribute significantly to the model’s explanatory power. First, the model’s adjusted R2 doubles from 24.6% 49.0% when advisor fixed effects are included in addition to investor fixed effects. Although investor fixed effects certainly add explanatory power over and above investor attributes, they do not meaningfully “crowd out” the advisor fixed effects, which remain strong predictors of risky share. Second, the F statistics reported in Table 5 show that both sets of fixed effects are statistically highly significant. These statistics for the advisor and investor fixed effects are F (8907, 2253)- and F (1015, 2253)distributed in Panel A.14 We can do a back-of-the-envelope “translation” of these statistics into t-values to illustrate their magnitudes. If we compute the p-values (which are both in the p < 10−40 range in Panel A) associated with these statistics and then recover these percentiles from the normal distribution, the advisor and investor fixed effects are significant with “t-values” of 18.2 and 13.8. The home-bias regressions in Panel B yield a similar picture. The adjusted R2 of the model increases from 26.6% to 39.0% when we include advisor fixed effects in addition to investor fixed effects. The two sets of fixed effects are of similar statistical significance. The F -values associated 13 Ameriks and Zeldes (2004) discuss the importance of the problem of (unrestricted) identification of age, time, and cohort effects. 14 The first argument in the second distribution, 1,015, is the number of advisor fixed effects that we can identify by exploiting investors making suitable moves from one advisor to another.

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with the advisor and investor fixed effects in the last column’s full model translate to similar (pseudo) t-values of 9.19 and 8.99. The one notable difference between the risky-share and homebias regressions is that while observable investor characteristics explain little of the cross-sectional variation in home bias, a model with investor fixed effects explains approximately a quarter of this variation. This result suggests that investors indeed have subjective views on the optimal allocation of domestic-versus-international equity, but that these views are unrelated to the investor attributes such as age, gender, risk tolerance, and so forth. By contrast, the same attributes explain a meaningful amount of cross-sectional variation in risky share.

4.5

Advisor attributes and advisor fixed effects

The regression estimates in sections 4.2 and 4.4 suggest that advisors contribute significantly to cross-sectional variation in investors’ portfolio choices. Why do advisors differ so much in the type of investments they recommend to their clients? Figure 2, which plots the advisor fixed effects from Table 4’s regression illustrates the remarkable dispersion in advisor fixed effects even when we control for investor attributes. Our dealer data contain a unique dimension for studying the determinants of advisor’s recommendations. First, the basic data include advisor demographics such as gender and age. Second, and more importantly, most investors and advisors in the data are also associated with encrypted personal insurance numbers, similar to social security numbers in the United States. These identifiers are useful because many advisors also maintain an account at their own firm and therefore appear in the data also as clients—which is why we excluded these advisor-investors from the previous sections’ tests. We can, first, use this link for the mundane purpose of filling in gaps in advisor gender and birth-date information. More importantly, this link allows us to observe many advisors’ personal portfolios and to test whether the personal portfolio explains the “abnormal tilt” (that is, the advisor fixed effect) seen in the portfolios of his or her clients. To set the stage for this analysis, Figure 4 shows that unlike in the sample of non-advisor clients—see Figure 3 Panel A—advisors’ personal risky shares do not vary systematically as a function of advisor age. In unreported regressions of advisor risky share (and home bias) on age and gender—that is, in regressions similar to those presented in Table 4 for non-advisor clients— only gender is reliably statistically significant. In these regressions female advisors have on average 22

3.2 percentage points lower risky share (t-value = −3.32) and 5.9 percentage points higher home bias (t-value = 4.17) than male advisors.15 Table 6 examines the extent to which advisor age and gender explain cross-sectional variation in Table 4’s estimates of advisor fixed effects. Because we extract these fixed effects from regressions that control for investor age and gender (among other investor attributes), no patterns can arise here because investors and advisors match by age and gender; the advisor fixed effects are orthogonal to observable investor attributes. The unit of observation in the data underneath Table 6’s regressions is an advisor. In the first regression, for example, we have the requisite data—the fixed effect from the risky-share regression and advisor age and gender—for 3,645 advisors. We define advisor’s age in these regressions as the advisor’s average age during his or her presence in the data. The estimates in the first column suggest that older advisors direct their clients into substantially riskier portfolios than younger advisors. The omitted age category contains the very youngest advisors, and the point estimates in Table 6 indicate that advisors age 60 or older, near or beyond retirement age, allocate on average at least ten percentage points more of clients’ portfolios to risky assets. These differences are statistically highly significant. This age result is in contrast with the finding that clients’ risky shares are hump-shaped as a function of their own age and it is also in contrast to the finding that advisors’ own average risky share is flat with respect to advisor age. Gender, by contrast, is unrelated to the advisor-driven heterogeneity in risky shares. The second regression in Table 6 adds as a regressor the advisor’s own average risky share. The positive and highly significant slope estimate of 0.25 (t-value = 16.01) indicates that advisors’ own preferences and beliefs partially bleed over to their clients’ portfolios. The model now explains 15.6% of the cross-sectional variation in advisors’ risky-share fixed effects. An advisor with a higher personal risky share gives his or her clients higher risky shares, controlling for investor attributes (and advisor age and gender). This shared-preferences effect, however, is unrelated to the advisor-age effect. Even in the second regression, which controls for the advisor’s risky share, risk-taking increases with advisor age, and the age coefficients retain their levels of statistical significance. This result suggests that there must be a reason other than heterogeneity in advisors’ 15 We also obtain for the linked advisors the Know-Your-Client forms that they filed in the system for themselves. Their responses to risk tolerance and financial knowledge questions, however, are not significant in regressions of risky share or home bias on these variables. It is thus possible that advisors file these forms only because they have to do so, and their responses do not reflect their true preferences and beliefs.

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own beliefs or preferences about the risk-return tradeoff that induce them to give their clients’ riskier portfolios. One mechanism that could generate this pattern is related to conflicts of interest and career concerns. Older advisors may be more willing to give their clients riskier portfolios because, first, equity products are associated with higher commissions and, second, because advisors close to or over the retirement age do not need to be as concerned about any detrimental effect that this behavior might have on their reputation. The home-bias regressions of Table 6 yield a similar picture. In the first regression, older advisors give their clients more international portfolios. This effect arises fully from the difference between the very youngest advisors (the omitted category) and all other advisors. That is, the very youngest advisors display markedly more home bias relative to everyone else. Gender also plays a role. The “abnormal” share of domestic equity is 2.2 percentage points (t-value = 2.28) higher among female advisors—and, importantly, this analysis again controls for investor attributes including age and gender. The last column shows that, similar to the risky-share regressions, advisors’ own home bias is a very significant determinant of their home-bias fixed effect. The slope on this variable is 0.31 (t-value = 22.07) and the full regression explains almost one-fifth of the variation in advisor fixed effects. In contrast to risky-share regressions, the slopes on the age and gender variables attenuate as we include advisor home bias as a regressor. The female-indicator variable, for example, turns insignificant. This result is consistent with the earlier result that female advisors display more home bias also in their personal portfolios. The attenuation here shows that once we control directly for heterogeneity in the home bias that advisors display in their own portfolios, advisor age has no incremental influence on the home-bias fixed effect.

5

Do Advisors’ Investment Recommendations Add Value?

Advisors cause their clients to take more equity risk than what they would take if left on their own. At the same time we find little evidence of advisors customizing their clients portfolios. The two-way fixed effects analysis, for example, suggests that who you have as an advisor has at least as great an effect on your portfolio as all your traits combined. In this section we assess the cost of this service by comparing clients’ net investment performance

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to passive investment benchmarks. Because we are interested in evaluating advisors, we aggregate holdings to the advisor level in computing returns. The analysis proceeds in two stages. First, we assess advisors’ skill in fund selection, asset allocation and market timing by comparing gross investment returns to a variety of passive investment benchmarks. Next, we incorporate value lost due to fees by repeating the same analysis with net returns.

5.1

Client performance gross of fees

To construct gross returns, we add to each client’s monthly account balance all fees paid on mutual fund investments, including management expenses.16 We examine risk-adjusted returns with a series of models that adjust for common equity and bond market risk factors. We begin with the CAPM, Fama and French (1993) three-factor and Carhart (1997) four-factor models. Next, we add two bond-related factors to account for the substantial non-equity allocation in most client portfolios. These fixed-income factors are the return differences between the ten-year and 90-day Treasuries and that between BAA- and AAArated corporate bonds. For all of these models, we estimate the asset pricing regressions over the full sample period using monthly return series. Table 7 Panel A reports the distributions of advisor-level gross alphas and t-values associated with these alphas. The distributions of t-values are useful for assessing skill because, unlike the alpha estimates, they control for differences in sample lengths and estimation uncertainty (Fama and French 2010). In these computations we first compute an advisor-level return by value-weighting the returns earned by the advisor’s all clients. We then use these advisor-level returns as dependent variables in the asset pricing regressions. The mean and median gross alphas are close to zero across the four asset pricing model. In the four-factor model with the fixed income factors, the average alpha is −0.29% per year, and the average t-value is −0.11. There is little evidence of superior stock-picking or market-timing abilities even in the right tail of the distribution. The t-values at the 99th percentile—corresponding to just over 100 advisors with the best performance in the sample—range from 2.43 (the augmented fourfactor model) to 2.86 (the CAPM). Because the t-values themselves have sampling distributions, we 16 In future versions of this paper we will also incorporate back end loads or redemption fees and all fees paid directly to the advisor, including account maintenance fees and front end loads. Those data, while they will become available, are not incorporated in this set of results. So we understate fees and overstate net returns currently.

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would observe statistically significant alphas by luck alone—in particular, even if the true alphas were identically zero, we would expect to observe t-values of 1.65 at the 95th percentile and those of 2.33 and the 99th percentile. The right-tails of the t-value distribution in Panel A exceed these reference distributions by only a small margin. We note that the t-value distributions are also symmetric—the t-values at the 1st and 5th percentiles are of the same magnitude as those at the 95th and 99th percentiles. There is little evidence of abnormal mass in the right tail of the distribution to indicate the presence of skill. The last column in Table 7 Panel A reports the estimates alphas and t-values for the average advised dollar. We compute the returns on the average advised dollar by value-weighting the returns of all investors in our sample. The last column’s alpha estimates lie just below the means and medians of the advisor-level alpha and t-value distributions. In the four-factor model augmented with fixed income factors the alpha on the average advised dollar is −0.54% per year, and this estimate has a t-value of −0.40.

5.2

Client performance net of fees

The gross return computations suggest that financial advisors are not able (or do not attempt) to profit by timing the market or selecting stocks. As a consequence, the fees that advisors charge result in negative net alphas. These fees are substantial. Across the 9,569 advisors, the average valueweighted management expense ratio that their clients face is 2.39%; the 1st and 99th percentiles of the fee distribution are 0.96% and 3.52%—there are no cheap advisors but an abundance of exceptionally expensive ones. Table 7 Panel B tabulates the distributions of advisor-level net alphas from the same four asset pricing models as above. These computations subtract off management expense ratios (and any direct fees charged by advisors) but do not take into account any front- and back-load fees that investors may pay. As a consequence, the net alphas reported here overstate investors’ realized alphas. The estimates in Panel B show that much of the distributions of the realized net alphas are below zero. Under a quarter of alphas are positive in every asset pricing models. The medians range from a high of −1.86% (the CAPM) to a low of −2.52% (the augmented four-factor model). There is again no evidence of positive alphas even in the rightmost tail of the distribution. There 26

are only marginally significant alphas at the 99th percentile—the t-values from the CAPM are the highest at this percentile, 1.90—whereas those on the other side of the distribution begin to approach statistical significance already at the 25th percentile. The average advised dollar experiences performance comparable to the means of the distributions. Across the same four models, the alphas on the value-weighted portfolios of all investors in the sample are −2.31% (t = −1.75), −2.59% (t = −1.91), −2.74% (t = −2.04), and −2.91% (t = −2.13). Thus, whether we study the distributions of alphas across advisors or focus the performance experienced by the average dollar, the conclusion is the same. There is little evidence of any advisor adding value through superior performance. The performance lag is largely due to the fees the investors pay, not due to the poor performance of the underlying assets. The economic significance of these negative net alphas is non-trivial. Consider, for example, the average advised dollar’s net-of-MERs alpha in the four-factor model augmented with the fixedincome factors, −2.91% per year. Because investors could earn a net alpha of 0% by investing in the passive benchmarks, this estimate implies that the investors hand over a steady stream of potential savings year after year. To illustrate how much the investors give away in present value terms to financial advisors (and mutual fund companies), suppose that an investor sets aside a fixed amount every year, and will retire in 30 years. If the expected return on the investor’s total portfolio— consisting of both equity and fixed income instruments—is 8%, an annual net alpha of 3% decreases the present value of the investor’s savings by 26%.17 This estimate means that the typical investor who begins saving for retirement with a financial advisor hands over a quarter of the present value of his or her retirement savings on day one. Even assuming a more “conservative” average net alpha of −2% per year, the wealth transfer to financial advisors and mutual fund companies amounts to 18% of the present value of the typical investor’s retirement savings. 17

French (2008) makes a similar computation to evaluate how much active investors spend, as a fraction of the total market capitalization of U.S. equities, to beat the market. The computation here is the following. The present value 
 1 of the investment described is an annuity with a present value of PV = Cr 1 − (1+r) , where C is the annual T dollar savings, r is the rate of return on the investment, and  T is the investment .  horizon. The ratio of present values PV1 r2 1 under the rates of return of r1 and r2 is then PV = 1 − 1 − (1+r1 )T . Plugging in the rates of T r1 (1+r ) 2 1

r1 = 8% and r2 = 5% gives

PV1 PV2

= 0.732.

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2

5.3

Performance over retirement-date matched lifecycle funds

The gross and net alpha estimates in Table 7 Panels A and B compare investors’ realized performance to the performance they would have obtained by holding passive, well-diversified portfolios without incurring any fees. This comparison is challenging because, first, it assumes that if these investors were made “unadvised,” they would have the knowledge to hold well-diversified benchmark portfolios and, second, because investors incur costs when making any investments. Table 7 Panel C addresses these limitations by comparing investors’ performance to the performance of retirement-date matched lifecycle funds. We assume that investors will retire at the age of 65 and then assign every investor one of the Fidelity Clearpath lifecycle funds that were available to these investors as one of the investment options—that is, it is a fund they could have held instead. The target dates of these funds range from 2005 to 2045 in five-year increments. These funds invest in other Fidelity equity and bond funds, and change the mixture of funds towards bonds as the retirement date approaches. The estimates on row “Difference in gross returns” show that, similar to Panel A’s gross-alpha analysis, there is no evidence of skill in gross returns across advisors. The distribution is symmetric, and the average dollar lags the performance of the value-weighted portfolio of the lifecycle funds we assign to investors by 0.87% per year. The mean and median differences in gross returns are −0.41% and −0.47% per year. If advisors do not outperform lifecycle funds in gross returns, the (generally) higher fees of non-lifecycle funds will generate performance drag. Row “Difference in MERs” in Panel C shows that the fees investors actually pay create a significant drag on their performance relative to the performance they would obtain by investing in lifecycle funds. The average dollar pays 1.31% per year more in management expense ratios, and the mean (median) across advisors is 1.33% (1.35%). The resulting drag on performance is statistically significant: even investors at the 1st percentile of the advisor distribution pay 3 basis points more in fees than what they would pay for the lifecycle funds. Investors are the other end of the distribution, at the 99th percentile, pay 2.48% more in fees.

5.4

Estimating the fraction of skilled advisors

Table 8 uses the Fama and French (2010) bootstrapping methodology to estimate the fraction of advisors who can consistently outperform passive benchmarks after fees. Fama and French (2010)

28

introduce this technique in a study of actively managed mutual funds. Because returns are very noisy, funds (and advisors) can have high or low alphas—and t(α)s—just by luck. The empirical difficulty then is disentangling luck from skill. Fama and French assess skill using the following procedure: 1. Estimate each fund’s alpha using all available data; 2. Set funds’ full-sample alphas to zero by subtracting estimated alphas from monthly funds returns; 3. Resample months from the panel with replacement to preserve the covariance structure of fund returns and factors. 4. Re-estimate alphas of all funds using the resampled data; and 5. Go back to step 3 and repeat the simulation procedure 10,000 times. By setting funds’ full-sample alphas to zero, the variation in the re-estimated alphas (and t(α)s) is due to noise. Fama and French (2010) then examine how the true distribution of t(α)s differs from the simulated distributions. The benefit of the by-month sampling scheme is that it retains the covariance structure of fund returns and factors, so the bootstrapping procedure properly accounts for correlated observations. Fama and French’s (2010) main analysis is based on the analysis of likelihoods. They compute the percentiles of the actual t(α)-distribution and then report the fraction of simulations in which the corresponding percentile is lower. If, for example, the simulated 90th percentile of the t(α) distribution is often lower than the corresponding percentile in the actual t(α) distribution, then fund managers at this percentile appear to have skill-that is, their t(α)s are higher than what we would expect them to be by luck alone. Fama and French (2010) conclude that only a handful of managers have skill. In the three-factor model only at the top-2% percentiles of the actual t(α) distribution the t-values dominate the simulated t-values more than 50% of the time. Table 8 shows the simulated and actual distributions of t-values using advisors’ net (Panel A) and gross returns (Panel B), and reports the fraction of simulations in which the actual t-value is higher than the simulated t-value. To illustrate, consider the 10th percentile of the t-values

29

associated with the CAPM alphas in Panel A. The actual t(α) at this percentile is −2.20, and this statistic is considerably lower than what it is in the average simulation, −1.04. The number 3.42 in the % < Act-column indicates that in just 3.42% of the simulations the 10th percentile of the simulated distribution lower than −2.20. That is, advisors at this percentile are considerably worse (in terms of their net alphas) than what they would be if the net alpha distribution were centered at zero and all variation in alphas was due to luck. A percentage less than 50% signifies the absence of skill. Whereas Fama and French (2010) find that some mutual fund managers have enough skill to cover the costs they impose on their investors, the results on financial advisors in Panel A are more pessimistic. Even in the CAPM the percentage of simulations in which the actual t-value exceeds the t-value from the simulations never breaches the 50% threshold. This finding regarding the lack of skill strengthens as we move to the three- and four-factor models. In these models the fraction of simulations in which the actual t-value exceeds the t-value from the simulations hovers around 1/3 even at the 99th percentile. The reason for this downward shift in perceived skill—which is also apparent in Table 7—is that advisors overweight mutual funds that invest in small value stocks.18 The estimates in Table 8 Panel B show that the lack of skill in advisors’ net returns is due to the fees they charge. The analysis of gross returns in Panel B asks whether advisors have enough skill to cover the costs missing from mutual funds’ expense ratios (Fama and French 2010). The actualexceeds-simulated percentage climbs above 50% already at the 20th percentile of the distribution in the CAPM, and around the 40th percentile in the three- and four-factor models. These estimates suggest that if no one in the system—advisors, mutual funds, or dealers—charged any fees for the services they provide then investors could benefit from advisors’ mutual fund choices. But because everyone in the chain provides their services at cost, investors lose relative to what they would earn if their money were instead invested in passive benchmarks. 18

We do not implement the Fama and French (2010) methodology for the augmented four-factor model because, by doing so, we would need to increase the number of months an advisor is required to be in the sample to be included in the analysis. We require an advisor to have at least 8 months of returns to be included in the sample, which is the same threshold used by Fama and French (2010). In the four-factor model this leaves us with three degrees of freedom.

30

6

Conclusions

Exploring unique data on Canadian financial advisors and their clients, we evaluate the influence advisors have over their clients’ portfolios. We present two key findings. First, advisors encourage increased risk-taking among their clients, whether by reducing uncertainty about future returns or by relieving anxiety when taking financial risk. Exploiting a regulatory change that reduced the supply of advisors, we estimate that taking on a financial advisor leads to an increase of roughly 40 percentage points in the share of risky assets held by the client. Second, advisors do relatively little to customize their advice on risk-taking. In total, characteristics such as risk tolerance and the point in the lifecycle explain only one-tenth of the variation in risky share across clients. Advisor fixed effects, by contrast, explain twice as much variation in risky shares. Advisor fixed effects also predict economically meaningful differences in risk: a one-standard deviation movement across the distribution of advisors corresponds to a 17-percentage point change in the risky asset share. Differences in advisors’ beliefs and preferences contribute to these advisor-specific effects. Specifically, we document a strong positive correlation between the amount of risk an advisor takes in his or her own portfolio and the amount of risk taken by his or her clients. Given the lack of customization and the fact that advisor fixed effects—which are, in essence, one-time draws of luck—have an economically significant impact on clients’ portfolios, the puzzle then is that this one-size-fits-all advice does not come cheap. We find that investors pay on average 2.5% of assets per year for advice. If the equity premium is 6 percent, the 40-percentage point increase in risky share caused by advisors translates into 0.40 × 6% = 2.4% higher expected return on investors’ total portfolios. But for the average investor it is the advisor who captures all of these additional returns. We conclude that, for the average investor, investment advice alone does not justify the fees paid to advisors. To be clear, our results do not imply that households do not benefit from using financial advisors. Given households’ strong revealed preference for using financial advisors, it is likely that they receive other benefits beyond investment advice. Such benefits may come in the form of financial planning, including advice on saving for college and retirement, tax planning and estate planning. It is also possible that financial advisors add value by mitigating psychological costs rather than providing financial benefit, that is, reducing anxiety rather than improving investment

31

performance (Gennaioli, Shleifer, and Vishny 2012). Exploring the importance of these benefits is an important topic for future work.

32

REFERENCES Abowd, J. M., F. Kramarz, and D. N. Margolis (1999). High wage workers and high wage firms. Econometrica 67 (2), 251–333. Ameriks, J. and S. P. Zeldes (2004). How do household portfolio shares vary with age? Columbia University working paper. Bergstresser, D., J. M. R. Chalmers, and P. Tufano (2009). Assessing the costs and benefits of brokers in the mutual fund industry. Review of Financial Studies 22 (10), 4129–4156. Berk, J. B. and R. C. Green (2004). Mutual fund flows and performance in rational markets. Journal of Political Economy 112 (6), 1269–1295. Canadian Securities Administrators (2012). Mutual fund fees. Discussion paper and request for comment 81-407. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance 52 (1), 57–82. Chalmers, J. and J. Reuter (2013). What is the impact of financial advice on retirement portfolio choice and outcomes? NBER Working Paper No. 18158. Fama, E. F. and K. R. French (1993). Common risk factors in the returns of stocks and bonds. Journal of Financial Economics 33 (1), 3–56. Fama, E. F. and K. R. French (2010). Luck versus skill in the cross section of mutual fund returns. Journal of Finance 65 (5), 1915–1947. Gennaioli, N., A. Shleifer, and R. Vishny (2012). Money doctors. NBER Working Paper No. 18174. Graham, J. R., S. Li, and J. Qiu (2012). Managerial attributes and executive compensation. Review of Financial Studies 25 (1), 144–186. Investment Company Institute (2013). ICI research perspective (February 2013). Mullainathan, S., M. Noeth, and A. Schoar (2012). The market for financial advice: An audit study. NBER Working Paper No. 17929.

33

100% 90% 80%

Risky share

70% 60% 50% 40% 30%

Risk tolerance = Very low Risk tolerance = Low Risk tolerance = Moderate Risk tolerance = High Vanguard target-date funds

20% 10% 0% 20

25

30

35

40

45

50 Age

55

60

65

70

75

80

Figure 1: Advised investors’ average risky share as a function of age and risk tolerance. This figure plots advised investors’ average risky shares separately for the three risk-tolerance categories as a function of age. Solid line plots the policy risky share of Vanguard target-date funds.

34

Panel A: Risky share 160 140

Frequency

120 100 80 60 40 20 0 -80

-70

-60

-50

-40

-30 -20 -10 0 Advisor fixed effect

10

20

30

40

Panel B: Home bias 120

100

Frequency

80

60

40

20

0 -70

-60

-50

-40

-30

-20 -10 0 10 Advisor fixed effect

20

30

40

50

Figure 2: Distributions of advisor fixed effects in risky-share and home-bias regressions. This figure plots the distributions of advisor fixed effects from Table 4’s regressions (2) and (4) in which the dependent variable is the risky share (Panel A) or home bias (Panel B) and the regressors consist of investor characteristics, advisor fixed effects, and year fixed effects.

35

Panel A: Age 80% 75% 70%

Risky share

65% 60% 55% 50% Without advisor FEs 45% With advisor FEs 40% 20

25

30

35

40

45

50

55

60

65

70

75

Age

Panel B: Risk tolerance 80%

75%

Risky share

70%

65%

60%

55% Without advisor FEs 50% With advisor FEs 45% 1

2

3

4

Risk Tolerance

Figure 3: Investors’ average risky share as a function of age and risk tolerance: Marginal effects with and without advisor fixed effects. The solid line plots advised marginal effects associated with age groups 21–25, 26–30,. . . , 75–80 (Panel A) and for the four risk-tolerance categories (Panel B) from Table 4’s regressions of risky share against investor attributes without advisor fixed effects. The dashed line plots marginal effects from the regression with advisor fixed effects. The level of marginal effects is normalized by setting the values of the other regressor to sample averages. 36

100% 90%

Risky share

80% 70% 60% 50% 40% 30% 20% 25

30

35

40

45

50

55

60

65

70

75

Age Figure 4: Advisor risky share as a function of age. This figure plots average risky share for advisors’ own portfolios as a function of advisor age. The dotted lines denote the 95% confidence interval around the sample average. Averages and confidence intervals are computed by estimating regressions of risky share against age-indicator variables and year fixed effects and clustering errors by advisor.

37

Table 1: Descriptive Statistics from Survey Data This table reports summary statistics from the Canadian Financial Monitor survey of Canadian households conducted by Ipsos-Reid. Age is that of the head of household. Education is the maximum level of education of the head of household and spouse. The indicator Retired takes the value of one if the head of household is retired. The data are from 16,530 advised and 42,720 unadvised households except for variable ∆Financial Assets/Income, which is available for 16,530 advised and 24,390 unadvised households.

Age Income ($C Thousands) Net worth ($C Thousands) Financial assets ($C Thousands) % equity % mutual funds % fixed income ∆Financial Assets/Income (%) Education, HS or less (%) Education, some college (%) Education, college diploma (%) Education, graduate degree (%) Homeowner (%) Retired (%)

Mean 56.3 74.9 472.3 221.1 15.0 39.7 45.2 13.5 15.6 15.3 51.8 17.2 87.4 34.7

Advised SD Median 14.2 58.0 41.6 65.0 413.0 372.5 272.2 131.1 27.8 0.0 38.4 33.2 38.9 32.9 146.4 7.7 36.3 0.0 36.0 0.0 50.0 100.0 37.7 0.0 33.2 100.0 47.6 0.0

38

Mean 52.4 55.4 212.5 71.6 6.9 12.7 80.4 4.4 25.9 19.1 44.7 10.2 67.8 27.2

Unadvised SD Median 16.2 53.0 38.4 50.0 290.8 105.9 162.9 11.3 20.9 0.0 27.7 0.0 34.2 100.0 102.8 0.6 43.8 0.0 39.3 0.0 49.7 0.0 30.4 0.0 46.7 100.0 44.5 0.0

Table 2: The Effect of Financial Advisors on Households’ Risk-taking Mutual fund dealers and their agents, financial advisors, were required to register with the Mutual Fund Dealers Association of Canada (MFDA) as of February 2001 to continue operating. This registration requirement, which forced dealers to follow the rules and regulations of the MFDA, did not apply to the province of Quebec. This table uses Ipsos-Reid household survey data on investors’ use of financial advisors and asset allocation along with a differences-in-differences model to examine financial advisors’ impact on these outcomes. Panel A uses monthly data from January 1999 through January 2004 and estimates the effect of the registration requirement on the households’ likelihood of using financial advisor. Household-level controls consist of control variables for income, education, age, and retirement status. Panel B measures the effect of financial advisors on household log-income, and investment in risky assets. The log-income regression in Panel B excludes income controls from the set of household-level controls. Robust standard errors, clustered at the province level, are reported in parentheses. Panel A: The effect of the Registration requirement on the use of a financial advisor Dependent variable (mean): Use Advisor (0.38) Regressor (1) (2) (3) ∗∗∗ ∗∗∗ Register * Post −0.040 −0.043 −0.043∗∗∗ (0.008) (0.008) (0.008) Register 0.020∗∗ −0.002 (0.008) (0.005) Post −0.024∗∗∗ −0.023∗∗∗ (0.000) (0.002) Observations R2 Household-level controls? Province and month FEs?

62,683 0.00

62,683 0.06

62,683 0.06

N N

Y N

Y Y

Panel B: The effect of the Registration requirement on income, participation, and risky share The Effect of Dependent Financial Advisors HH-level Province and 2 variable Sample OLS IV N R controls? month FEs? Log(Income) All 0.246∗∗∗ −0.172 62,683 0.24 Y Y (0.009) (0.258) Participation

All

Risky share

All

∗

significant at 10%;

0.142∗∗∗ (0.010)

∗∗

0.667∗∗∗ (0.157)

59,033

0.22

Y

Y

0.095∗∗∗ 0.387∗∗∗ 59,033 (0.010) (0.099) significant at 5%; ∗∗∗ significant at 1%

0.20

Y

Y

39

Table 3: Descriptive Statistics from Dealer Data This table reports summary statistics for investors (Panel A) and financial advisors (Panel B). “Investor known since” is the number of years an investor has been the client of the current advisor. “Investor set-up since” is the number of years an investor has been with any advisor. Both variables are computed as of year-end 2010. Advisors collect information on their clients’ risk tolerance, financial knowledge, net worth, and salary using “Know Your Client” surveys. The percentages under “account types,” “time horizon,” and “risk tolerance” sum up to more than 100% because investors can have multiple accounts and time horizon and risk tolerance information is collected at the account level. The different license types are rights to sell mutual funds, mutual funds and life insurance, segregated funds, labor-sponsored funds, and principal-protected notes. Panel A: Investors Variable Female Age Investor known since Investor set-up since Number of accounts Number of funds Account value, $K Expense ratio, % Expense ratio, $

Mean 0.50 49.95 4.73 3.18 2.04 7.99 57.84 2.43 1574.89

Account types General Retirement savings Retirement income Education savings Tax-free

10th 32 0 0 1 1 1.7 1.8 55.0

25th

75th

49 3 3 1 4

59 6 5 2 10

68 12 7 4 19

6.1 2.3 197.2

21.0 2.4 664.0

62.8 2.6 1843.5

142.1 2.8 3917.9

Time horizon 1–3 years 4–5 years 6–9 years 10+ years

19.7% 65.3% 6.4% 4.2% 4.0%

SD 14.01 5.92 3.14 1.85 10.00 399.8 0.57 3372.9

3.8% 8.9% 72.1% 21.7%

Financial knowledge Low 41.9% Moderate 51.7% High 6.3%

2.7% 10.7% 60.6% 25.9% Net worth

Under $35k $35-60k $60-100k $100-200k Over $200k

90th

40 1 1 1 2

Risk tolerance Very low Low Moderate High

Percentiles 50th

Salary 15.2% 31.8% 8.0% 13.8% 31.2%

Under $50k $50-100k $100-200k $200-300k Over $300k

40

22.0% 8.1% 18.1% 35.3% 16.5%

Panel B: Financial Advisors Variable Age Tenure Number of clients Number of accounts Number of funds/client Number of licenses License types Mutual funds MFs and life insurance Labor-sponsored funds Segregated funds Principal-protected notes Account value, $K Expense ratio, %

Mean 50.09 3.19 73.92 151.15 4.55 1.81

10th 36 0 1 2 1.8 1

25th 43 1 3 5 2.8 1

39.7 2.1

204.0 2.3

Percentile 50th 75th 50 57 2 5 18 82 29 139 4.0 5.7 2 2

90th 63 8 206 414 7.7 3

SD 10.38 2.85 164.50 371.18 2.9 0.70

10300.0 2.7

12000.0 0.38

85.2% 14.8% 18.4% 47.3% 10.1% 3853.03 2.39

41

876.2 2.4

3474.4 2.6

Table 4: Regressions of risky share and home bias on household demographics and advisor fixed effects This table reports estimates from panel regressions of risky share (Panel A) and home bias (Panel B) on different investor attributes, year fixed effects, and advisor fixed effects. Risky share is the fraction of wealth invested in equity and home bias is the fraction of equity invested in Canadian companies. We record measurements of risky share and home bias for each investor at year-ends 1999 through 2012. We always omit the indicator variable for the lowest category and so the intercept corresponds to the average risky share or home bias of an investor at the year-end 1999 who is between 21 and 24 years old, male, has very low risk tolerance, very-short investment horizon, low financial knowledge, annual salary less than $50k, and net worth below $35k. The first two regressions are estimated using data on all advisors. We exclude from the sample clients who we identify as advisors through their social insurance numbers. The regressions in the low-dispersion and high-dispersion columns divide advisors each year into two groups of equal size based on clientbase heterogeneity. The measure of heterogeneity is the within-advisor standard deviation of the fitted values from column 1’s regression. The last row, “Adjusted R2 w/o advisor FEs,” reports the adjusted R2 from an alternative model that does not include the advisor fixed effects. Standard errors are clustered by advisor.

42

Panel A: Dependent variable = Risky share Independent variable Constant Age, 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–80 Female Risk tolerance Low Moderate High Time horizon Short Moderate Long Fin. knowledge Moderate High Salary $50-100k $100-200k $200-300k Over $300k Net worth $35-60k $60-100k $100-200k Over $200k Year FEs Advisor FEs # of observations # of investors # of advisors Adjusted R2 w/o advisor FEs

All advisors ˆb t 43.22 25.71 0.62 0.96 2.97 4.31 4.40 5.96 3.95 5.21 2.89 3.73 1.94 2.48 0.45 0.57 −1.18 −1.46 −2.98 −3.54 −4.51 −5.04 −4.19 −4.46 −1.27 −9.11

All advisors ˆb t 45.50 31.00 1.57 2.93 3.76 6.86 4.70 8.39 3.84 6.72 2.41 4.11 1.21 2.04 −0.53 −0.91 −2.53 −4.19 −4.47 −7.27 −6.31 −9.60 −6.79 −9.88 −1.31 −12.78

Low-dispersion advisors ˆb t 36.93 8.71 1.12 1.64 3.53 4.82 4.14 5.29 3.65 4.49 2.41 2.88 1.12 1.32 −0.58 −0.71 −2.60 −3.05 −4.50 −5.26 −6.37 −6.88 −7.58 −7.66 −1.10 −7.29

High-dispersion advisors ˆb t 43.42 24.45 1.81 2.42 3.88 5.25 5.08 6.99 3.92 5.34 2.33 3.08 1.19 1.57 −0.59 −0.77 −2.55 −3.26 −4.54 −5.67 −6.29 −7.39 −6.27 −7.08 −1.49 −11.56

10.02 23.98 26.59

10.14 24.48 24.96

10.20 21.66 24.34

11.86 24.65 26.44

20.07 32.79 35.87

5.53 9.21 10.03

9.84 21.20 23.53

11.34 23.97 24.54

2.53 6.00 6.29

3.08 8.00 7.49

3.27 4.91 5.38

4.93 8.10 8.60

2.59 4.07 4.37

2.35 3.95 4.14

3.42 5.20 5.81

4.35 7.30 7.90

2.83 4.17

7.06 6.61

1.76 3.77

12.27 12.63

1.78 2.73

7.57 6.58

1.73 4.47

10.58 11.83

0.88 1.36 1.41 1.24

2.23 3.02 5.44 5.04

0.67 −0.56 0.35 0.71

3.06 −2.74 2.44 4.72

1.13 −0.28 0.64 0.83

3.69 −1.00 3.05 3.77

0.29 −0.75 0.18 0.65

1.02 −2.61 1.01 3.50

2.32 1.34 2.10 0.62

4.82 3.18 5.44 1.81

−0.09 −0.08 0.38 0.23

−0.31 −0.30 1.68 1.14

0.43 0.44 0.99 0.27

1.05 1.02 2.64 0.81

−0.35 −0.36 0.03 0.21

−0.95 −1.10 0.11 0.88

Yes No

Yes Yes

Yes Yes

Yes Yes

765,483 173,449 4,984

765,483 173,449 4,984

336,771 89,910 2,744

425,285 112,574 2,499

11.0% .

30.8% 11.0%

28.8% 6.9%

29.6% 11.6%

43

Panel B: Dependent variable = Home bias Independent variable Constant Age, 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–80 Female Risk tolerance Low Moderate High Time horizon Short Moderate Long Fin. knowledge Moderate High Salary $50-100k $100-200k $200-300k Over $300k Net worth $35-60k $60-100k $100-200k Over $200k Year FEs Advisor FEs # of observations # of investors # of advisors Adjusted R2 w/o advisor FEs

All advisors ˆb t 60.37 25.03 0.95 0.98 −0.17 −0.15 −1.12 −0.94 −1.31 −1.10 −1.12 −0.94 −1.03 −0.86 −0.35 −0.29 0.38 0.31 1.14 0.88 2.19 1.61 3.36 2.42 0.89 3.71

All advisors ˆb t 55.89 26.03 1.13 1.37 0.92 0.99 0.71 0.76 0.71 0.76 0.80 0.85 0.67 0.71 1.18 1.23 1.71 1.74 2.57 2.60 3.83 3.71 4.49 4.30 0.48 3.08

Low-dispersion advisors ˆb t 58.41 18.00 1.44 1.03 0.08 0.06 −0.59 −0.41 −1.04 −0.72 −0.86 −0.59 −1.17 −0.80 −0.86 −0.59 −0.14 −0.09 0.58 0.38 2.13 1.36 2.13 1.33 0.27 1.28

High-dispersion advisors ˆb t 54.48 17.83 1.01 1.10 1.42 1.29 1.53 1.39 1.85 1.67 1.94 1.73 1.93 1.70 2.59 2.25 2.93 2.48 3.83 3.23 4.95 3.94 5.89 4.66 0.61 3.14

−1.62 −1.10 −4.49

−1.28 −1.06 −3.87

−0.24 −0.02 −2.93

−0.26 −0.03 −3.60

−0.83 0.43 −2.72

−0.66 0.40 −2.46

0.26 −0.59 −3.35

0.22 −0.56 −3.10

0.58 0.30 −0.42

0.48 0.26 −0.34

0.53 1.06 1.30

0.60 1.25 1.51

−0.75 0.01 0.15

−0.62 0.01 0.13

1.59 1.93 2.24

1.35 1.71 1.97

1.22 −1.03

2.11 −1.28

−0.81 −2.70

−3.87 −6.14

−1.08 −3.13

−3.87 −4.90

−0.63 −2.53

−2.34 −4.68

−3.99 −1.61 −1.02 −1.83

−6.25 −2.69 −2.69 −4.71

−1.32 1.23 0.44 −0.43

−3.75 4.06 1.93 −1.80

−1.66 1.91 0.44 −0.33

−2.79 4.30 1.44 −1.05

−1.29 0.83 0.39 −0.56

−3.13 2.26 1.34 −1.80

−5.27 −1.55 −2.58 −0.32

−6.95 −2.49 −4.25 −0.63

−0.53 0.26 −0.47 −1.11

−1.25 0.65 −1.34 −3.73

−1.36 −0.17 −0.71 −1.07

−2.06 −0.31 −1.54 −3.02

−0.28 0.51 −0.31 −1.10

−0.54 0.97 −0.65 −2.55

Yes No

Yes Yes

Yes Yes

Yes Yes

748,125 170,270 4,949

748,125 170,270 4,949

307,932 94,563 2,916

436,901 111,484 2,508

1.7% .

26.4% 1.7%

26.8% 1.8%

26.4% 1.6%

44

Table 5: Fund-selection regressions of risky share and home bias on household demographics, advisor fixed effects, and investor fixed effects This table reports estimates from regressions of risky share (Panel A) and home bias (Panel B) on different investor attributes, year fixed effects, and advisor and investor fixed effects. We create a list of investors (“movers”) who move across advisors during the sample period due to the disappearance of their former advisor, and then examine all the clients of all advisors who are at some point associated with at least one mover. We compute the average risky share and home bias of new funds for each advisor-investor pair. The regressors in the first column consist of investor characteristics and year fixed effects. The regression in the second column adds advisor fixed effects. The third regression replaces investor characteristics with investor fixed effects. In the first two regressions we omit always omit the indicator variable for the lowest category and so the intercept corresponds to the average risky share or home bias of an investor at the year-end 1999 who is between 21 and 24 years old, male, has very low risk tolerance, very-short investment horizon, low financial knowledge, annual salary less than $50k, and net worth below $35k. We exclude from the sample clients who we identify as advisors through their social insurance numbers. The numbers in parentheses on the advisor and investor fixed effects rows report F -values from the tests that these fixed effects are jointly zero. In the third column of Panel A (Panel B) advisor fixed effects take up 1,015 (959) degrees of freedom because the remaining advisor fixed effects are not identified due to the lack of suitable movers. The last row, “Adjusted R2 w/o advisor FEs,” reports the adjusted R2 from an alternative model that does not include the advisor fixed effects. Standard errors are clustered by advisor.

45

Panel A: Risky share of new funds Independent (1) ˆb variable Constant 42.93 Age, 25–29 −1.05 30–34 2.08 35–39 3.29 40–44 2.09 45–49 1.07 50–54 −0.10 55–59 −0.76 60–64 −0.60 65–69 −3.45 70–74 −2.67 75–80 −7.41 Female −1.07 Risk tolerance Low 10.09 Moderate 23.05 High 25.28 Time horizon Short 3.61 Moderate 7.38 Long 6.97 Fin. knowledge Moderate 3.61 High 4.32 Salary $50-100k −0.02 $100-200k 0.38 $200-300k 1.28 Over $300k 1.32 Net worth $35-60k 1.80 $60-100k 0.10 $100-200k 0.06 Over $200k 1.62 Year FEs Advisor FEs (F -test) Investor FEs (F -test) # # # #

of of of of

observations investors movers advisors

Adjusted R2 w/o advisor FEs

(2)

(3)

t 8.07 −0.38 0.78 1.25 0.79 0.40 −0.04 −0.28 −0.22 −1.23 −0.93 −2.37 −2.48

ˆb 49.73 1.63 4.21 5.19 3.82 2.74 1.09 0.05 −1.00 −3.42 −2.51 −8.80 −1.60

t 13.59 0.79 2.14 2.67 1.97 1.42 0.56 0.02 −0.51 −1.68 −1.18 −3.96 −4.43

3.78 8.94 9.43

9.49 19.43 21.98

6.50 14.58 16.00

1.44 3.16 2.70

3.33 6.18 5.51

1.89 3.85 3.26

4.15 3.41

2.18 4.43

4.88 5.04

−0.02 0.33 1.93 1.67

−0.19 −0.97 0.32 0.09

−0.21 −1.27 0.66 0.16

1.13 0.09 0.06 1.60

0.34 0.43 0.28 0.83

0.38 0.47 0.37 1.29

ˆb

t

Yes No No

Yes Yes (5.03) No

Yes Yes (2.54) Yes (1.62)

12,190 8,908

12,190 8,908

1,555

1,555

12,190 8,908 3,245 1,555

10.4% .

40.7% 10.4%

49.0% 24.6%

46

Panel B: Home bias of new funds Independent (1) ˆ variable b Constant 71.26 Age, 25–29 −9.66 30–34 −9.99 35–39 −12.32 40–44 −11.45 45–49 −10.75 50–54 −11.98 55–59 −11.00 60–64 −9.19 65–69 −6.99 70–74 −7.66 75–80 −6.92 Female 2.01 Risk tolerance Low 2.83 Moderate −0.39 High −4.44 Time horizon Short −4.48 Moderate −5.77 Long −7.74 Fin. knowledge Moderate 1.64 High 1.90 Salary $50-100k −4.60 $100-200k −0.49 $200-300k 0.77 Over $300k 1.06 Net worth $35-60k −3.53 $60-100k −3.07 $100-200k −2.42 Over $200k −2.68 Year FEs Advisor FEs (F -test) Investor FEs (F -test) # # # #

of of of of

observations investors movers advisors

Adjusted R2 w/o advisor FEs

(2)

(3)

t 10.16 −2.59 −2.80 −3.46 −3.21 −2.98 −3.41 −3.01 −2.47 −1.83 −1.84 −1.65 3.37

ˆb 65.26 −8.74 −8.76 −11.61 −10.65 −9.54 −10.66 −9.75 −7.86 −6.66 −7.81 −6.50 1.72

t 11.83 −2.90 −3.03 −4.07 −3.74 −3.37 −3.74 −3.40 −2.70 −2.23 −2.50 −1.99 3.23

0.90 −0.13 −1.50

2.63 −1.77 −4.47

1.10 −0.80 −1.97

−1.34 −1.78 −2.12

−5.33 −5.84 −6.04

−1.96 −2.34 −2.31

1.37 1.05

−0.22 −0.97

−0.33 −0.74

−2.67 −0.32 0.81 1.09

−0.92 2.34 0.76 0.61

−0.71 2.08 1.04 0.74

−1.63 −1.82 −1.63 −1.87

−2.45 −1.56 −1.33 −1.14

−1.83 −1.16 −1.18 −1.20

ˆb

t

Yes No No

Yes Yes (4.34) No

Yes Yes (1.64) Yes (1.38)

11,700 8,659

11,700 8,659

1,492

1,492

11,700 8,659 3,069 1,492

2.0% .

31.1% 2.0%

39.0% 26.6%

47

Table 6: Regressions of advisor fixed effects on advisor demographics, risky share, and home bias This table reports estimates from cross-sectional regressions of risky-share and home-bias fixed effects on advisor age and gender, and time-series averages of advisors’ personal risky share and home bias. The unit of observation is an advisor. The fixed-effect estimates are from Table 4. The risky-share fixed effects are from the second regression in Panel A and the home-bias fixed effects are from the second regression in Panel B. Dependent variable = Risky-share FE Independent variable Age, 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 Female Advisor’s risky share Advisor’s home bias

(1) ˆb 3.83 1.17 4.53 5.88 6.43 7.04 6.10 11.27 11.62 17.62 0.25

Home-bias FE (2)

t 0.91 0.29 1.14 1.49 1.64 1.79 1.54 2.84 2.86 4.02 0.38

ˆb 8.05 5.31 6.45 7.62 9.40 9.72 8.33 13.79 14.19 20.67 1.15 0.25

t 1.69 1.13 1.40 1.66 2.05 2.13 1.81 3.00 3.02 4.25 1.81 16.01

(3) ˆb −9.90 −16.78 −14.08 −15.20 −14.80 −16.01 −10.25 −10.38 −13.05 −14.92 2.22

t −1.51 −2.64 −2.24 −2.45 −2.38 −2.58 −1.65 −1.66 −2.03 −2.16 2.28

(4) ˆb −4.92 −14.50 −10.32 −9.98 −10.23 −11.25 −5.42 −5.33 −9.05 −8.81 1.42

t −0.77 −2.41 −1.74 −1.71 −1.75 −1.93 −0.93 −0.91 −1.51 −1.34 1.48

0.31

22.07

# of observations

3,645

2,904

3,622

2,860

Adjusted R2

3.3%

15.6%

1.3%

19.2%

48

Table 7: Gross and Net Alphas and Performance Relative to Lifecycle Funds This table reports the distributions of advisor-level gross alphas (Panel A), net alphas (Panel B), and the excess returns over retirement date-matched lifecycle funds, and the t-values associated with the alpha estimates. The asset pricing models in Panels A and B are the CAPM, three-factor model, four-factor model, and four-factor model augmented with two fixed-income factors, the return difference between 10-year and 90-day Treasuries and that between BAA- and AAA-rated corporate bonds. Net returns adjust returns for management expense ratios but do not adjust for front- and back-load fees. Advisor-level alphas are computed by value-weighting returns across each advisor’s all clients, and then estimating the asset pricing regressions for separately for every advisor. Column “Average dollar” reports the estimated alphas and t-values for the average advised dollar. The alphas earned by the average advised dollar are computed from the value-weighted returns of all investors in the sample. The retirement date-matched lifecycle funds in Panel C are Fidelity Clearpath funds with retirement target dates ranging from 2005 to 2045 in five-year increments. We assume that investors retire at age 65 when assigning these lifecycle funds. Row “Difference in gross returns” reports the difference between the average realized performance of an advisor’s clients and the average realized performance of the value-weighted portfolio of the lifecycle funds assigned to the advisor’s clients. “Difference in MERs” reports the difference between the realized management expense ratio of an advisor’s clients and that of the lifecycle funds assigned to the advisor’s clients. Panel A: Gross alphas

α t(α)

Mean 0.58 0.26

1% −10.29 −2.89

5% −4.88 −1.60

Percentiles 25% 50% −1.03 0.53 −0.44 0.25

75% 2.12 0.99

95% 6.28 2.08

99% 10.89 2.86

Average dollar 0.06 0.05

Three-factor

α t(α)

0.21 0.09

−10.82 −3.10

−5.27 −1.67

−1.28 −0.56

0.28 0.13

1.69 0.78

5.79 1.77

10.39 2.60

−0.21 −0.16

Four-factor

α t(α)

0.08 0.05

−10.86 −3.07

−5.41 −1.70

−1.33 −0.58

0.17 0.08

1.51 0.70

5.63 1.72

9.87 2.66

−0.37 −0.28

Four-factor with bond factors

α t(α)

−0.29 −0.11

−11.76 −3.09

−6.26 −1.83

−1.70 −0.71

−0.13 −0.06

1.22 0.54

5.10 1.49

10.56 2.43

−0.54 −0.40

Pricing Model CAPM

49

Panel B: Net alphas (after MERs)

α t(α)

Mean −1.83 −0.87

1% −13.01 −4.31

5% −7.35 −2.71

Percentiles 25% 50% −3.40 −1.86 −1.57 −0.88

75% −0.29 −0.12

95% 3.81 1.15

99% 8.46 1.90

Average dollar −2.31 −1.75

Three-factor

α t(α)

−2.21 −1.02

−13.60 −4.36

−7.73 −2.77

−3.66 −1.69

−2.11 −1.03

−0.74 −0.32

3.38 0.93

7.99 1.79

−2.59 −1.91

Four-factor

α t(α)

−2.34 −1.07

−13.59 −4.34

−7.89 −2.80

−3.74 −1.73

−2.22 −1.09

−0.92 −0.40

3.19 0.90

7.47 1.78

−2.74 −2.04

Four-factor with bond factors

α t(α)

−2.70 −1.20

−14.39 −4.43

−8.59 −2.96

−4.08 −1.87

−2.52 −1.22

−1.22 −0.51

2.70 0.69

8.26 1.58

−2.91 −2.13

Pricing Model CAPM

Panel C: Realized returns over retirement date-matched lifecycle funds Percentiles Difference in: Mean 1% 5% 25% 50% 75% 95% Gross returns −0.41 −20.28 −8.48 −2.36 −0.47 1.33 7.98 MERs

1.33

0.03

0.70

1.18

50

1.35

1.51

1.81

99% 19.53

Average dollar −0.87

2.48

1.31

Table 8: Fama and French (2010) Luck-versus-Skill Analysis of Advisors’ Gross and Net Returns We estimate advisors’ alphas using the CAPM, three-factor model, and four-factor model. We record the actual distributions of t(α)s from these models and then subtract estimated alphas from monthly advisor returns. We then resample months 10,000 times. In each simulation we reestimate the alphas, construct the t(α) distribution, and compare the percentiles of each simulated distribution against the actual t(α) distribution. Each block in this table reports the simulated and actual distributions of t(α)s and the fraction of simulations in which the simulated t-value at given percentile is lower than the actual t-value. Panel A (Panel B) estimates alphas from gross returns (net returns). The sample is restricted to advisors with at least 8 months of returns. Panel A: Gross returns CAPM Pct Sim Act % < Act 1 −2.11 −2.91 11.54 2 −1.79 −2.40 15.68 3 −1.61 −1.96 25.64 4 −1.48 −1.75 29.72 5 −1.37 −1.61 32.14 10 −1.04 −1.12 41.16 20 −0.66 −0.61 50.68 30 −0.39 −0.28 55.10 40 −0.17 0.01 60.18 50 0.04 0.27 64.34 60 0.25 0.55 68.70 70 0.47 0.87 73.60 80 0.74 1.23 78.36 90 1.13 1.72 83.20 95 1.48 2.14 85.58 96 1.58 2.24 85.46 97 1.72 2.38 85.58 98 1.91 2.57 85.64 99 2.23 2.94 86.22

Sim −2.25 −1.88 −1.68 −1.53 −1.42 −1.06 −0.66 −0.39 −0.16 0.05 0.26 0.49 0.76 1.15 1.51 1.62 1.77 1.97 2.33

3-factor model Act % < Act −3.14 9.94 −2.43 18.02 −2.06 24.14 −1.84 27.90 −1.68 30.18 −1.24 35.52 −0.72 44.18 −0.39 48.34 −0.11 52.76 0.16 56.72 0.42 60.14 0.68 62.92 0.98 65.76 1.44 70.58 1.86 74.52 1.99 75.52 2.16 76.60 2.42 79.00 2.83 80.20

51

Sim −2.40 −1.98 −1.75 −1.59 −1.47 −1.09 −0.68 −0.40 −0.17 0.04 0.25 0.48 0.75 1.16 1.53 1.65 1.80 2.02 2.41

4-factor model Act % < Act −3.11 15.30 −2.44 22.02 −2.07 27.42 −1.86 29.64 −1.70 31.98 −1.25 36.16 −0.75 43.18 −0.41 47.58 −0.15 50.42 0.11 54.00 0.36 56.98 0.61 59.14 0.90 61.10 1.39 67.50 1.83 72.06 1.98 73.48 2.16 75.20 2.42 77.24 2.88 79.70

Panel B: Net returns CAPM Pct Sim Act % < Act 1 −2.11 −4.34 0.18 2 −1.79 −3.57 0.40 3 −1.61 −3.11 0.98 4 −1.48 −2.87 1.40 5 −1.38 −2.71 1.64 10 −1.04 −2.20 3.42 20 −0.66 −1.73 4.72 30 −0.39 −1.39 6.22 40 −0.17 −1.13 6.84 50 0.04 −0.84 8.46 60 0.25 −0.57 10.32 70 0.47 −0.24 13.66 80 0.74 0.16 19.38 90 1.13 0.80 32.50 95 1.48 1.26 39.42 96 1.58 1.39 41.06 97 1.72 1.51 40.36 98 1.91 1.69 40.68 99 2.23 2.01 40.74

Sim −2.25 −1.88 −1.68 −1.53 −1.42 −1.06 −0.66 −0.39 −0.16 0.05 0.26 0.48 0.76 1.15 1.51 1.62 1.77 1.97 2.34

3-factor model Act % < Act −4.43 0.50 −3.56 0.80 −3.15 1.16 −2.94 1.14 −2.77 1.46 −2.31 2.18 −1.84 3.14 −1.51 3.88 −1.24 4.54 −0.99 5.10 −0.71 5.84 −0.42 6.88 −0.02 9.90 0.59 18.42 1.11 28.18 1.23 27.98 1.39 29.40 1.62 31.64 1.94 29.90

52

Sim −2.40 −1.98 −1.75 −1.59 −1.47 −1.09 −0.68 −0.40 −0.17 0.04 0.25 0.48 0.75 1.16 1.53 1.65 1.80 2.02 2.42

4-factor model Act % < Act −4.45 1.66 −3.59 1.60 −3.19 1.58 −2.96 1.70 −2.78 1.88 −2.34 2.42 −1.86 3.14 −1.56 3.78 −1.29 4.30 −1.04 4.72 −0.79 5.02 −0.49 5.80 −0.10 7.76 0.55 15.92 1.07 24.40 1.20 25.16 1.37 26.50 1.64 30.72 2.05 33.12