The Misguided Beliefs of Financial Advisors∗

Juhani T. Linnainmaa

Brian T. MelzerAlessandro Previtero

October 2016

Abstract

A common view of retail finance is that conflicts of interest contribute to the high cost of financial

advice. Using detailed data on financial advisors and their clients, however, we show that most

advisors invest their personal portfolios just like they advise their clients. They trade frequently,

chase returns, and prefer expensive, actively managed funds over cheap index funds. Differences

in advisors’ beliefs affect not only their own investment choices, but also cause substantial

variation in the quality and cost of their advice. Advisors do not hold expensive portfolios only

to convince clients to do the same—their own performance would actually improve if they held

exact copies of their clients’ portfolios, and they exhibit similar trading behavior even after they

leave the industry. These results suggest that many advisors offer well-meaning, but misguided,

recommendations rather than self-serving ones. Policies aimed at resolving conflicts of interest

between advisors and clients do not address this problem.

∗Juhani Linnainmaa is with the University of Southern California and NBER, Brian Melzer is with the Northwest-ern University, and Alessandro Previtero is with the Indiana University and NBER. We thank Jason Allen, DiegoGarcia, Chuck Grace, John Griffin, Jonathan Reuter, and Sheridan Titman for valuable comments. We are grate-ful for feedback given by conference and seminar participants at Boston College, Dartmouth College, University ofMaryland, Georgetown University, HEC Montreal, Indiana University, Northwestern University, University of Texasat Austin, University of Colorado Boulder, University of Chicago, NBER Behavioral Economics 2016 spring meetings,University of Rochester, SFS Cavalcade 2016, and Western Finance Association 2016 meetings. We are especiallygrateful to Univeris, Fundata, and three anonymous financial firms for donating data and giving generously of theirtime. Alessandro Previtero received financial support from Canadian financial firms for conducting this research.Juhani Linnainmaa received financial support from the PCL Faculty Research Fund at the University of ChicagoBooth School of Business. Address correspondence to Alessandro Previtero, Indiana University, 1275 E. 10th St.,Bloomington, IN 47405, USA (email: aleprevi@indiana.edu).

1 Introduction

A common criticism of the financial advisory industry is that conflicts of interest compromise the

quality, and raise the cost, of advice. Many advisors require no direct payment from clients but

instead draw compensation from commission payments on the mutual funds they sell. Within this

structure, advisors may be tempted to recommend products that maximize commissions instead

of serving the interests of their clients. A growing academic literature has assessed conflicts of

interest and has shown suggestive evidence that sales commissions raise costs and distort portfolios.1

Policymakers in Australia, the United Kingdom, and the United States, in turn, have either banned

commissions or mandated that advisors act as fiduciaries, placing clients’ interests ahead of their

own.2

In this paper we find strong support for an alternative, if not mutually exclusive, explanation

of costly and low-quality advice that carries starkly different policy implications. Advisors are

willing to hold the investments they recommend—indeed they invest very similarly to clients—but

they have misguided beliefs. They recommend frequent trading and expensive, actively man-

aged products because they believe active management, even after commissions, dominates passive

management. Eliminating conflicts of interest may therefore reduce the cost of advice by less than

policymakers hope.

Our analysis uses data provided by two large Canadian financial institutions. Advisors within

these firms provide advice on asset allocation and serve as mutual fund dealers, recommending the

1See, for example, Bergstresser, Chalmers, and Tufano (2009), Mullainathan, Noth, and Schoar (2012), Christof-fersen, Evans, and Musto (2013), Anagol, Cole, and Sarkar (2016), and Egan (2016).

2In 2012, the Australian government implemented the Future of Financial Advice Reform, which banned conflictedcompensation arrangements, including commissions. In 2013, the Financial Conduct Authority in the United Kingdombanned commissions. In 2016, the United States Department of Labor finalized a rule to address conflicts of interestin retirement advice by imposing fiduciary duty on advisors providing such a service.

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purchase or sale of unaffiliated mutual funds. These advisors are not subject to fiduciary duty

under Canadian law (Canadian Securities Administrators 2012). The data include comprehensive

trading and portfolio information on more than 3,000 advisors and almost 500,000 clients between

1999 and 2013. Our data also include, for the vast majority of advisors, the personal trading and

account information of the advisor himself. This unique feature proves fruitful for our analysis.

The advisor’s own trades reveal his beliefs and preferences regarding investment strategies, which

allows us to test whether client trades that are criticized as self-serving emanate from misguided

beliefs rather than misaligned incentives.

We begin by characterizing the trading patterns of advisors and clients in our sample. We

measure trading behaviors in dimensions that previous research suggests hurts risk-adjusted per-

formance: high turnover, preference for funds with active management or high expense ratios,

return chasing, and home bias.3 Both clients and advisors strongly lean toward trading patterns

documented previously for self-directed investors. For example, they purchase funds with better-

than-average historical returns and overwhelmingly favor expensive, actively managed funds. This

similarity in trading patterns suggests that advisors do not dramatically alter their recommenda-

tions when acting as agents rather than principals.

An analysis of fees and investment returns likewise shows little evidence that advisors recom-

mend worse performing funds for clients than they hold themselves. The average expense ratios of

3These behavioral patterns have been studied extensively. See, for example, Nofsinger and Sias (1999), Grinblattand Keloharju (2001b), Barber and Odean (2008), and Kaniel, Saar, and Titman (2008) for analyses of how investorstrade in response to past price movements; Odean (1999), Barber and Odean (2000), and Grinblatt and Keloharju(2009) for analyses of trading activity; French and Poterba (1991), Coval and Moskowitz (1999), and Grinblatt andKeloharju (2001a) for studies of the home bias; and French (2008) for a discussion of the underperformance of activemanagement. These behavioral patterns may lead to both underperformance and underdiversification. French andPoterba (1991) show that expected domestic returns would need to exceed foreign expected returns by hundreds ofbasis points per year to offset home bias’s negative effect on portfolio diversification. Barber and Odean (2000) findthat active trading—which can result from, e.g., return chasing—significantly lowers individual investors’ portfolioreturns. Carhart (1997) shows that returns decrease with fund turnover and expenses reduce performance at leastone-for-one.

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mutual funds in advisors’ and clients’ portfolios are nearly the same, at 2.43% and 2.36%. Advisors

benefit from managing their own assets because they earn sales and trailing commissions on their

purchases. Even after adjusting for these rebates, however, the average advisor’s performance short

of his clients’ performance by 27 basis points. Clients and advisors earn net alphas between −4%

and −3% per year.

A battery of tests suggests that the similarity in trading emerges due to advisors’ influence

over client trades. The identity of the advisor is the single most important piece of information

for predicting nearly all of the client trading behaviors. The common variation among co-clients,

measured through advisor fixed effects, dominates variation explained by observable client traits

such as age, income, risk tolerance, and financial knowledge. We also estimate a model with client

fixed effects to address the possibility that the advisor effects capture shared, but unobservable,

preferences among co-clients. This two-way fixed effects analysis is feasible because we observe

clients who are forced to switch advisors due to their old advisor’s death, retirement, or resignation.

These switches exhibit little client-level selection as they are typically transfers of an advisor’s entire

“book of business” to a new advisor. The client fixed effects also prove important in explaining

portfolio choices, but they do not meaningfully crowd out the advisor effects.

We trace differences in advisors’ recommendations to their own beliefs and preferences, as

reflected in their own trading. An advisor who encourages his clients to chase returns, for example,

typically also chases returns himself. These correlations are significant for all of the trading patterns

that we evaluate. These correlations are present, and of similar magnitude, irrespective of whether

we measure advisor influence with or without client fixed effects.

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We use detailed transaction data—the timing of trades and the specific funds purchased—to

further illustrate advisors’ impact on client trading. While common strategies, such as return chas-

ing, may coincidentally emerge among clients, it is unlikely that clients would buy specifically the

same funds at the same time without common input from the advisor. Clients of the same advisor

often purchase the same funds in the same month. By contrast, we rarely observe coordinated

trading between clients of different advisors when we randomize the client-advisor pairings. The

purchases of advisors show the same pattern. Advisors’ own clients frequently purchase the same

funds as the advisor in the same month, whereas other clients do not.

More than 80% of an advisor’s purchases are funds currently held or purchased by his clients

in the same month. When an advisor deviates from his clients, he favors funds with even stronger

prior performance and higher expense ratios. These trades do not outperform the funds that are

purchased together by clients and advisors.

Collectively, our results suggest that advisors’ own beliefs and preferences drive their recom-

mendations. We examine and rule out two alternative explanations. First, advisors do not appear

to engage in “window dressing,” that is, they do not invest in expensive funds only to convince

their clients to do the same. We show that advisors’ trading behavior remains largely unchanged

after they leave the industry. They continue to chase returns, invest in actively managed funds, and

choose funds that are as expensive as those they chose before. Moreover, if advisors were window-

dressing, their personal portfolios should perform no worse than those of their clients. However,

the average advisor would earn higher returns if he copied his clients’ portfolios.

Second, advisors do not seem to push certain mutual funds on behalf of their dealer firms. If

advisors serve as salespeople rather than analysts, they might be convinced to purchase, both for

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their clients and for themselves, the funds promoted by their dealer. Differences between dealers,

however, explain little of the differences in client behavior. For example, client attributes alone

explain 1.0% of the cross-sectional variation in clients’ return-chasing behavior. The R2 increases

only to 1.1% when we account for differences between dealers, but to 16.5% when include advisor

fixed effects. The same pattern holds for the other trading behaviors. The advisor, rather than the

dealer, provides the common input.

We conclude by showing that differences in advisors’ beliefs predict substantial differences in

clients’ investment performance. We sort advisors into deciles based on the gross performance of

their personal portfolio and compare their clients’ performance. Clients advised by bottom-decile

advisors earn 1.2% lower returns than clients advised by top-decile advisors. The fees display the

same pattern. Advisors who hold portfolios in the top fee decile recommend portfolios that are

0.4% more expensive than those recommended by the advisors at the other end of the distribution.

Portfolio risk, as measured by the volatility of idiosyncratic returns, likewise increases for clients

served by advisors who personally hold portfolios with high idiosyncratic risk. The risk in clients’

portfolios falls by almost half when their advisor is in the bottom decile of idiosyncratic risk rather

than the top decile. Together, these cross-sectional patterns in gross returns, fees, and risk indicate

that differences in advisors’ beliefs cause substantial variation in risk-adjusted portfolio returns.

We contribute to the literature on financial advice by highlighting the importance of advisors’

beliefs. Our work complements Mullainathan, Noth, and Schoar (2012), who show that advisors fail

to override client biases toward return chasing and active management. We confirm their findings

and document a specific reason—mistaken beliefs—as to why advisors fail to de-bias their clients.

Our analyses of return chasing, active trading, and fund fees differ from Foerster, Linnainmaa,

5

Melzer, and Previtero (2015), which uses the same data to document advisors’ influence on equity

allocations. While advisors do not adjust their personal portfolios to manipulate clients, their choice

to hold similar portfolios may engender trust and facilitate client risk-taking (Gennaioli, Shleifer,

and Vishny 2015). Our analysis also relates to studies of advisor misconduct (Egan, Matvos, and

Seru 2015), conflicts of interest (Bergstresser, Chalmers, and Tufano 2009; Christoffersen, Evans,

and Musto 2013; Anagol, Cole, and Sarkar 2016; and Egan 2016), and investment performance of

advised accounts (Chalmers and Reuter 2015; Hackethal, Inderst, and Meyer 2012; and Hoechle,

Ruenzi, Schaub, and Schmid 2015)

Other studies have also used product purchases by sales agents or “experts” to examine the

roles of incentives, beliefs, and information in principal-agent problems. Cheng, Raina, and Xiong

(2014) find that mid-level managers in securitized finance personally invested in real estate during

the mid-2000s housing boom, at the same time they sold mortgage securities. Dvorak (2015), on

the other hand, shows that benefits consultants design similar 401(k) plans for clients as they offer

themselves, but when they differ the funds specific to the clients’ plans are more expensive. Levitt

and Syverson (2008) also find that real estate agents act differently on their own behalf, leaving

their own homes on the market for longer and selling them at higher prices than their clients’

homes. Finally, Bronnenberg et al. (2015) show that pharmacists and chefs are substantially less

likely to buy nationally branded items than lower-priced, private-label alternatives in their domains

of expertise. By contrast, the experts in our setting do not tilt their purchases toward lower-cost

alternatives, suggesting that advisors themselves prefer brand-name mutual funds just as their

clients do.

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The rest of the paper is organized as follows. Section 2 describes the data and the sample.

Section 3 describes our measures of trading behavior and shows that advisors and clients pursue

similar strategies and earn comparable alphas. Sections 4 and 5 measure advisors’ influence on

client trading and explore the similarity between their own investments and their recommendations

to clients. Section 6 examines the correlation between client and advisor performance. Section 7

examines changes in advisors’ behavior after they exit the industry. Section 8 concludes and

discusses the policy implications.

2 Data

We use administrative data on client investments and advisory relationships provided by two Cana-

dian Mutual Fund Dealers (MFDs). Non-bank financial advisors of this type are the main source of

financial advice in Canada—they account for $390 billion (55%) of household assets under advice

as of December 2011 (Canadian Securities Administrators 2012). The two firms for which we have

data advise just under $20 billion of assets, so represent roughly 5% of the MFD sector.4

Advisors within these firms are licensed to sell mutual funds and precluded from selling individ-

ual securities and derivatives. They make recommendations and execute trades on clients’ behalf

but cannot engage in discretionary trading.5 They do not provide captive distribution for particular

mutual fund families. Rather, they are free to recommend all mutual funds. As discussed below,

the breadth in their clients’ holdings reflects this freedom.

4These firms are among those studied by Foerster et al. (2015). Two of the firms in that study did not providethe identifiers necessary for matching advisors to their personal portfolios and comparing client and advisor behavior.We exclude these two dealers throughout this study.

5Under Canadian securities legislation, advisors do not have fiduciary duty, but they have a duty to make suitableinvestment recommendations, based on their clients’ investment goals and risk tolerance. They are not required toput the client’s interests before their own, but they are legally mandated to “deal fairly, honestly and in good faithwith their clients” (Canadian Securities Administrators 2012).

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Both dealers provided a detailed history of client transactions and demographic information

on clients and advisors. Clients and advisors have unique identifiers that are derived from social

insurance numbers. We use these identifiers to link advisors to their personal investment portfolios,

if held at their own firm. While these portfolios are visible to us, they would only be visible to

clients if voluntarily disclosed by the advisor.

Most advisors maintain a personal portfolio at their firm. Out of 4,407 advisors, 3,276 appear

in the data also as clients. The advisors who do not have their personal portfolio at the firm

are usually just starting out. For example, among the 680 advisors who never attract more than

four clients—and typically disappear quickly—only 42% have personal portfolios at the firm. But

among the 2,123 advisors who go on to advise at least 50 clients, 91% appear in the data also as

clients.6

We supplement these administrative data with returns, fees, and net asset values from Fundata,

Morningstar, and Univeris.

2.1 Advisors and their clients

Table 1 provides the key summary statistics for the clients and financial advisors. The sample

includes all individual accounts held at one of the two dealers between January 1999 and December

2013. The sample includes the 3,276 advisors whose personal portfolios appear in the data and the

488,263 clients who are active at some point during the 14-year sample period. The total amount

of assets under advice as of June 2012 is $18.9 billion.

6Table A1 in the Appendix presents an analysis of advisor survival as a function of the number of clients. Theestimates show that advisors with more than 100 clients have a survival rate of 98.9%. This survival rate decreasesalmost monotonically as the number of clients falls, and reaches 81.2% among advisors with at most five clients.

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Among clients, men and women are equally represented, and client ages range from 32 years

old at the bottom decile to 67 years old at the top decile. The average client has 1.7 plans, or

sub-accounts, invested in 3.5 mutual funds. The distribution of client assets is right-skewed: while

the median client has CND 23,500 in assets, the average account size is CND 55,300. Advisors are

slightly different from their clients. Nearly three-quarters of the advisors are men, and the advisors’

own accounts are typically larger than the clients’ accounts. The average advisor’s account value is

CND 112,100, which is twice as large as the average client. The second panel shows the distribution

of account types. The majority of investors—85% of clients and 86% of advisors—have retirement

plans, which receive favorable tax treatment comparable to IRA and 401K plans in the U.S. The

next most common account type is the unrestricted general-purpose plan (“open account”), which

is held by 28% of clients and 44% of advisors. In some of our analyses, we separate retirement and

general accounts because of differences in tax treatment.

The second panel also shows the distributions of risk tolerance, financial knowledge, salary, and

net worth for clients and advisors. Financial advisors collect this information through “Know Your

Client” forms at the start of the advisor-client relationship, and they are required to file these

forms also for themselves.7 Most advisors report either moderate-to-high or high risk tolerance,

whereas the average client is only moderately risk tolerant. Advisors also have higher salaries and

net worth than their clients. Most advisors report “high” financial knowledge although, perhaps

surprisingly, a handful of advisors report “low” financial knowledge, which corresponds to a person

who has “some investing experience but does not follow financial markets and does not understand

the basic characteristics of various types of investments.”

7Foerster et al. (2015) give examples of the descriptions that accompany some of the risk-tolerance and financial-knowledge categories on the Know Your Client forms.

9

The third panel summarizes the overlap in fund purchases between clients and advisors. We

exclude purchases made under automatic savings plans and focus on the remaining, “discretionary”

purchases. We divide the client purchases into three mutually exclusive groups: funds purchased in

the same month by both the client and the advisor; funds purchased by the client that are already

held in the advisor’s portfolio; and funds purchased only by the client. Of the 8.1 million purchases

made by clients, more that one-quarter of those funds are also purchased or held by the advisor in

the same month. For the advisors, the overlap is even more striking. Nearly 20% of purchases are

unique to the advisor; the remaining 80% of funds are either purchased contemporaneously or held

in a client portfolio.

2.2 Investment options, fund types, and fees

The clients in the data invest in 3,023 different mutual funds. In the Morningstar data, a total

of 3,764 mutual funds were available to Canadian investors at some point during the 1999–2013

sample period. Most mutual funds are offered with different load structures. The most common

structures are front-end load, back-end load (or deferred-sales charge), low load, and no load. All

options are available to clients, but it is the advisor who decides the fund type in consultation

with the client. These vehicles differ in how costly they are to the investor, how (and when) they

compensate the advisor, and how they restrict the investor’s behavior. We provide an overview of

fund fees and commissions below, along with more detailed discussion in Appendix A.

In measuring investment performance we calculate returns net of all fees and rebates. The fees

include recurring management expense charges—assessed in proportion to the investment value—

deducted daily by the mutual fund company as well as transaction charges such as front-end and

back-end load payments. The rebates include transaction charges reimbursed by the mutual fund or

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financial advisor. In their own trading, advisors face the same conditions and fees as non-advisors

do. For example, if the advisor sells a back-end load fund too early, he incurs the same back-

end load (or deferred-sales) charge as clients. Advisors do, however, benefit from serving as the

agent for their own purchases—they receive commissions from the mutual fund that reduce their

cost of investing. Specifically, advisors receive up-front sales commissions that act as discounts on

new purchases and recurring “trailing” commissions that lower the effective management expense

ratio for as long as they maintain their investments. When measuring advisors’ net investment

performance, we account for all fees net of such commissions.8

3 Trading behaviors and investment performance of clients and

advisors

3.1 Trading behaviors

We compare investors and advisors using four summary measures of trading behavior—return

chasing, preference for actively managed funds, turnover, and home bias—and two measures of

portfolio cost. Below, we define each measure and discuss the summary statistics reported in

Table 2. We calculate these summary statistics using all trades and holdings in open accounts and

retirement accounts, and, where relevant, report the estimates separately by account type. For

all behaviors except turnover, we measure the behavior using data on the flow of purchases rather

than the stock of portfolio holdings.

8Advisors do share their commissions with their dealer firms. A 2010 industry study of the top ten Canadiandealers reports that advisors received, on average, 78% of the commission payments (Fusion Consulting 2011). Wetherefore assume that advisors keep 78% of commissions in calculating their net cost of investment.

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Both clients and advisors purchase funds with better recent performance. We define return

chasing as in Grinblatt, Keloharju, and Linnainmaa (2012), by ranking all mutual funds based

on their net returns over the prior one year period and computing the average percentile rank of

the funds purchased by investors. A high measure implies that investors purchase funds with high

prior one-year returns. Clients purchase funds that are, on average, in the 60th percentile of prior

year performance. Advisors display slightly more return chasing, with average purchases in the

63rd percentile of prior year performance.

Clients and advisors are also similar in their overwhelming preference for actively managed

mutual funds. We define active management as the fraction of (non-money market) assets

invested in actively managed mutual funds. We classify as passive those funds that are identified as

index funds in Morningstar or that call themselves “index fund” or “target-date fund”. The average

client invests almost exclusively in actively managed mutual funds, with only 1.5% allocated to

passive funds. Likewise, advisors allocate only 1.2% to passive funds. These allocations are close

to the 1.5% market share of index mutual funds in the Canadian market (Canadian Securities

Administrators 2012). For comparison, the market share of index mutual funds in the United

States is 9% (Investment Company Institute 2012).

Advisors trade more actively than clients, particularly in non-retirement accounts. We define

turnover as the market value of funds bought and sold divided by the beginning-of-the-month

market value of the portfolio, as in Barber and Odean (2000). We split the sample between tax-

deferred retirement accounts and general-purpose (or “open”) accounts within which income and

capital gains are taxed annually. Clients and advisors display slightly lower turnover in retirement

accounts—annual purchases and sales comprise just under 31% of retirement account value for

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clients and 39% of account value for advisors. Advisors also trade substantially more in open

accounts, with average turnover of 52% compared to 34% for clients.

We next consider international diversification and find pronounced home bias among both clients

and advisors. To measure home bias we compute the fraction of the equity holdings invested in

Canadian equities. The average client allocates 54% to Canadian equities, and the average advisor

allocates 46%. These proportions significantly exceed Canada’s share of the global market portfolio,

which is less than 4% (Pakula et al. 2014).

Finally, we consider the cost of funds purchased and find similar, but slightly higher, costs in

advisor accounts compared to client accounts. We measure the average cost of funds purchased in

two ways. The first measure is the average annualized management expense ratio (MER) of the

funds purchased. The second measure is the average percentile rank of management expense ratio,

computed separately within five asset classes: equities, balanced, fixed income, money market, and

alternatives.9 This measure is neutral with respect to asset allocation. A high percentile rank

implies that clients hold mutual funds that are expensive compared to other funds in the same

class. Both advisors and clients invest in expensive mutual funds. The average annualized MERs

are 2.36% and 2.43% for clients and advisors, and the difference between the two is statistically

significant. This difference also holds within asset classes: the average fund bought by a client lies

in the 43th percentile while that bought by the advisor is in the 46th percentile. The difference

between these ranks is statistically significant with a t-value of −10.1.

To summarize, the estimates in Table 2 show broad similarity in the trading of advisors and

clients. Not only do advisors and clients display similar patterns such as return chasing and

turnover, they also invest in funds of similar cost.

9The category “alternatives” includes funds classified as commodity, real estate, and retail venture capital.

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3.2 Investment performance

Table 3 summarizes the investment performance of advisors and clients. For both advisors and

clients, we compute value-weighted returns, and use three alternative asset pricing models to esti-

mate alphas. We measure both gross and net returns. In calculating net returns we consider both

mutual fund expense ratios and further fees paid on transactions. These fees consist of front-end

loads and deferred sales charges. When measuring advisors’ net returns, we include the sales and

trailing commissions that mutual funds rebate on their personal fund purchases and holdings. Due

to these rebates, advisors’ net returns inclusive of fees and commissions are almost always higher

than their returns net of mutual fund expense ratios.

Table 3 presents estimates from three asset pricing models. The first model is the Sharpe (1964)-

Lintner (1965) capital asset pricing model with the excess return on the Canadian equity market as

the market factor. The second model adds a factor measuring the term spread in bonds, the return

difference between the long-term and short-term Canadian government bonds. The third model

adds the North American size, value, and momentum factors, and the return difference between

high-yield Canadian corporate debt and investment grade debt. We include bond market factors

to account for clients’ and advisors’ bond holdings, and the size, value, and momentum factors to

adjust for any style tilts. We report the estimates for all three models to assess whether the alpha

estimates are sensitive to the choice of factors.

Table 3 reports annualized alphas.10 Both clients and advisors earn gross alphas that are

statistically indistinguishable from zero. In the first model, gross alpha is 14 basis points (t-value

= 0.15) per year for clients and −68 basis points (t-value = −0.66) per year for advisors. The

10Table A2 in the Appendix reports the factor loadings and model fits.

14

addition of the other factors in the second and third models lowers the alpha estimates, which

remain statistically indistinguishable from zero. The full model with six factors explains 87% to

88% of the time-series variation in the value-weighted returns on the client and advisor portfolios.

The alpha on the return difference between clients and advisors, which is equivalent to taking

a $1 short position on the advisor portfolio and investing that in the client portfolio, is statistically

highly significant in all three models. The alpha on this return difference is measured more precisely

because the difference removes much of the time-series variation in returns. In the full model, the

alpha for the difference in gross returns is 55 basis points per year (in the clients’ favor), and this

difference is significant with a t-value of 2.55.

Because both clients and advisors invest in funds with high management expense ratios (see

Table 2), their net alphas—computed after management expense ratios but before other fees—are

substantially negative. The annualized six-factor model alphas are −3.07% (t-value = −3.42) for

clients and −3.66% (t-value = −3.79) for advisors. Foerster et al. (2015) discuss clients’ negative

net alphas and their implications for long-term wealth accumulation in detail.

The additional fees—front-end loads and deferred sales charges—net of any rebates reduce

clients’ alphas by an additional 15 basis points per year. The sales commissions rebated to advisors,

net of deferred sales charges, raise advisors’ net alpha by 17 basis points per year. Net of all mutual

fund expenses, fees and commission rebates, then, the total performance of advisors and clients is

similar. In the six-factor model, advisors lag clients by a statistically insignificant 27 basis points

per year.

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4 Measuring advisors’ influence on client trading

In this section we measure advisors’ influence on client portfolios. We use the return chasing

behavior to introduce the methodology in detail and then present the key results for the remaining

trading patterns and the fee measures.

4.1 Return chasing behavior

The distribution of return chasing, plotted in Figure 1, shows considerable variation across clients

and advisors. Although the mean of the distribution is positive, some clients and advisors ex-

hibit contrarian tendencies. In the following analysis, we study the cross-section of clients and

test whether the advisor’s common input explains where his clients fall within this distribution.

Subsequently, in Section 5, we test whether a client’s position in this distribution correlates with

his advisor’s position in the distribution.

Table 4 Panel A displays estimates from the following regression model:

yia = µa + θXi + εia, (1)

in which the dependent variable, yia, is the average percentile rank of the funds bought by client

i when advised by advisor a. The vector Xi includes investor attributes summarized in Table 1—

such as risk tolerance, investment horizon, and age—as well as geographic location (province fixed

effects). The advisor fixed effects µa capture common variation in the return chasing behavior

among clients of the same advisor. We estimate the model using OLS, with standards errors

clustered by advisor to account for correlation in behavior between clients who share an advisor.

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The first model reported in Table 4 excludes the advisor fixed effects to gauge the explanatory

power of the dealer effect and investor attributes alone. This model’s explanatory power is modest.

The adjusted R2 is 1.1% with the dealer effect and 1.0% without this effect. Some covariates stand

out. Return chasing is more common among more risk tolerant and financially knowledgeable

clients who are wealthier and report short investment horizons. The second regression includes

advisor fixed effects. These fixed effects increase the model’s explanatory power substantially, to

16.5%. The estimates indicate that clients who share the same advisor chase returns to a similar

extent; some advisors’ clients follow momentum strategies while others’ clients follow contrarian

strategies.

The significance of the advisor fixed effects in Table 4 could emanate from endogenous matching

between advisors and clients. An investor who is predisposed to chase returns may seek an advisor

who recommends such trades to all his clients. In that case, the advisor fixed effects may overstate

the common input of the advisor—some of the common trading among clients may reflect trades

that clients initiate themselves. Although the regressions control for many demographics that

plausibly relate to the advisor-client matching, such as gender, age, and income, it is possible that

advisors and clients also match with each other in some other dimension that also correlates with

return chasing.

We use a two-way fixed effects approach to address this issue. In this analysis, we limit the

sample to clients who switch advisors (within the same dealer firm) after their initial advisor dies,

retires, or leaves the industry. We exclude client-initiated switches since they may coincide with a

change in client preferences. We identify a client as having been displaced if the advisor goes from

having at least ten clients to quitting within six months. By observing clients who switch advisors,

17

we can simultaneously identify advisor and client fixed effects, the latter controlling for unobserved

characteristics that may be similar among clients of the same advisor. To the extent that clients

and advisors match on characteristics that remain fixed over time, the client fixed effects absorb

those differences and purge the advisor fixed effects of potential bias due to matching.

While it is true that clients can still select their post-switch advisor, the data reveal that selection

at this stage is somewhat rare. The vast majority of switches in our sample represent transfers

of entire client groups, or “books of business,” from one advisor to another at the same dealer.

Upon being displaced, 85% of clients maintain an account at the same dealer, and conditional on

staying, 87% of the clients end up with the same new advisor. The variation that we examine in

the two-way fixed effects model, therefore, is mostly unaffected by client-level selection.

The estimates in Panel B of Table 4 show that advisors significantly influence client behavior.

The regression with just client fixed effects has an adjusted R2 of 5.1%; that with just advisor fixed

effects has an adjusted R2 of 19.7%; and the model with both client and advisor fixed effects has

an adjusted R2 of 29.1%. The F -tests at the bottom of the table indicate that both client and

advisor fixed effects in the last of these regressions are statistically highly significant.

4.2 Other trading patterns

We repeat the analysis of Section 4.1 for each measure of trading behavior and the two fee measures.

After first estimating regressions of trading behavior on client attributes and advisor fixed effects,

we then estimate a model with investor fixed effects in the subsample of displaced clients. Because

the differences between open accounts and retirement accounts in Table 2 are relatively modest for

turnover and home bias, we henceforth pool these accounts when measuring behavior.

18

The estimates in Panel A of Table 5 show that, in most cases, the inclusion of advisor fixed

effects yields a significant boost to the model’s explanatory power. In the active-management

regressions, for example, the client attributes explain just 0.9% of the variation in the use of

passive investment vehicles. With the advisor fixed effects added into this regression, the model’s

explanatory power increases 18.0%. The explanatory power of these advisor fixed effects does

not arise from differences between dealers. Models with and without the dealer effect have the

same explanatory power of 0.9%. These results do not support the view that advisors passively

implement dealer recommendations.

The other columns in Panel A use displaced clients to estimate models with client fixed ef-

fects, advisor fixed effects, and both. Similar to the return-chasing regressions presented in Table 4

Panel B, advisor fixed effects often increase the model’s explanatory power significantly. In all four

behavior regressions and two fee regressions, F -tests (not reported) reject the null that the advisor

fixed effects in the full model with both sets of fixed effects are jointly zero. In the active manage-

ment regression, for example, investor fixed effects alone yield a negative adjusted R2 because the

reduction in the degrees of freedom more than offsets the amount of variation they resolve.

These estimates suggest that advisors have substantial influence over their clients’ decisions and

direct many clients to trade in similar ways.

4.3 Event-study analysis of purchases by clients of the same advisor

As further illustration that advisors provide common recommendations to their clients, we show

that clients of the same advisor (“co-clients”) often purchase the same funds at the same time. We

use an event-study approach. First, we identify all events in which a client purchases a new mutual

19

fund. Second, for a two-year window around this month, we estimate the probability that at least

one co-client buys the same fund for the first time.

Figure 2 displays these estimates. The black line indicates the estimates from month t− 12 to

month t+12, where month t is the client’s purchase month. The dashed lines denote 95% confidence

intervals, computed with clustering at the advisor level. The estimated probability that at least one

co-client purchases the same fund in the same month is 0.45. In addition to this contemporaneous

spike in purchases, there is also an elevated probability of a co-client purchase in the two months

before or after the original client’s purchase. By contrast, when we randomly match each client with

another advisor’s clients, we find little overlap in their purchases. For this analysis we resample

the data 100 times with replacement, each time matching the client to another advisor at the same

dealer (blue line) or the other dealer (red line). We then measure the fraction of fund purchases

that are also made by at least one counterfactual co-client during a two-year window. Regardless

of whether the counterfactual co-clients are drawn from the same dealer or the other dealer, we

find few common purchases.

The coordination in trading that we observe among co-clients is strong evidence that advisors

direct clients to trade in similar ways. Even if clients selected advisors who prefer a given trading

strategy such as return chasing, it would be unlikely that co-clients would purchase precisely the

same funds at the same time without common input from the advisor. Clients could also buy the

same funds at the same time because of other coordination mechanisms, such as news stories or

ratings changes. However, the fact that the clients’ trades only overlap with those of their co-clients,

and not with other clients, rules out this possibility.

20

5 Do advisors encourage clients to trade like themselves?

We now explore whether advisors adopt for themselves the same trading strategies or individual

trades that we have identified as common among their clients. In these tests, we compare each

advisor’s estimated fixed effects to his own trading behaviors, and we also examine the overlap in

individual trades between advisors and their clients.

5.1 Explaining advisor fixed effects with advisors’ own investment behavior

Table 6 reports estimates from regressions of advisor fixed effects on advisor attributes and advisor

behavior. The dependent variables are the estimated fixed effects from the regressions reported

in Panels A and B of Table 5. We first examine the advisor fixed effects estimated from models

that control for client attributes. We then examine advisor fixed effects estimated for the subset of

displaced clients and their advisors. While the latter analysis covers a smaller set of advisors—those

that work with displaced clients—its measure of advisor influence cleanly identifies the causal input

of those advisors. The key explanatory variable in each regression is the advisor’s own behavior.

The regressions in the first column, for example, explain variation in the advisor fixed effects

estimated from the return chasing regression with the return chasing behavior measured in the

advisor’s personal portfolio. Alongside advisor behavior, each model includes controls for advisor

age, gender, native language, number of clients, and risk tolerance.

The estimates in Table 6 indicate that, in most cases, an advisor’s personal investment behavior

correlates closely with that of his clients. In the return chasing regression, for example, the slope

estimate for the advisor-behavior variable is 0.24 (t-value = 13.67). If an advisor chases returns,

his clients are more likely to chase returns. For the other trading behaviors, the advisor-behavior

21

coefficients range from a low of 0.13 (for total MER) to a high of 0.34 (for home bias), indicating

some variation in which dimensions advisor behavior most closely tracks that of his clients. Advisor

attributes, by contrast, do not correlate meaningfully with advisor fixed effects: the adjusted R2

decreases only modestly when we exclude them from the regressions. The bottom half of Table 6

shows that the estimates are generally similar when we use the fixed effects from the displacement

regressions.

5.2 Similarity in fund purchases and timing between advisors and clients

The estimates in Table 6 suggest that an advisor’s personal beliefs may be an important reason

why his clients trade differently from those of other advisors. That is, clients may chase returns

because the advisor advises them to do so, and he advises them to do so because he personally

follows the same investment strategy. The connection between the advisor and client behavior is

even closer than what the estimates in Table 6 suggest. Advisors often personally purchase the

very same funds that they recommend to their clients, meaning that advisors do not just pursue

similar strategies they even invest in the same funds at the same time as their clients.

We compare advisor and client purchases in an event study, just as we did for clients and co-

clients. We identify all events in which an advisor purchases a new mutual fund and estimate the

probability that at least one of the advisor’s clients buys the same fund around the same time. We

also compare each advisor’s purchases to the purchases of clients that use another advisor. For this

comparison, we resample other advisors’ client groups 100 times with replacement, and compute

the fraction of times that at least one counterfactual client purchases the same fund as the advisor

in the months surrounding the advisor’s own purchase.

22

The black line in Figure 3 shows that an advisor’s clients often buy the same new fund as

the advisor within a month or two of the advisor’s own purchase. The estimated probability of

contemporaneous purchase by at least one client is 0.45.11 There is little overlap in purchases with

respect to the clients of other advisors. The probability of common purchase with at least one client

of the randomly matched advisor never exceeds 0.04. This estimate is similar for counterfactual

advisors drawn from the same dealer (blue line) or the other dealer (red line).

As in the estimation of advisor fixed effects, the sample of displaced clients is useful for es-

tablishing a causal link between advisors’ own trades and their clients’ trades. Before a client is

displaced, we can measure the overlap between a client’s purchases and those of his current and fu-

ture advisors. Figure 4 shows that clients’ purchases overlap with their current advisors’ purchases.

Before the displacement, more than 5% of client purchases overlap with the current advisor’s pur-

chases, and just 1% overlap with their future advisor. Following the switch, however, the overlap

in purchases with the new advisor increases more than four-fold, to nearly the same level as exhib-

ited with the old advisor. This pattern is consistent with a causal connection—advisors’ preferred

investments appear in their clients’ portfolios as well not at all times, but specifically when they

work together.

5.3 A comparison of advisors’ and clients’ overlapping and non-overlapping

trades

Advisors often, but not always, purchase the same mutual funds for themselves as for their clients.

Table 1 Panel C shows that one-fifth of advisor purchases are “advisor-only purchases,” mutual

11Figure A1 estimates the same probabilities using data on advisors who have no more than ten clients at the timeof the purchase. The estimated probabilities for this sample are approximately the same as what they are in Figure 3Panel A. The estimates suggest that advisors with a large number of clients do not drive the results.

23

funds not bought or held by clients at the same time. Among client transactions, three-quarters of

fund purchases are “client-only purchases,” neither bought nor held by advisors at the same time.

In Table 7, we measure the differences in characteristics—return chasing, home bias, and fees—

and future performance of the funds bought just by the advisor, just for the clients, or jointly.

After classifying all fund purchases into one of these three “purchase types,” we collapse the data

by advisor. The resulting sample provides the average characteristics for just over 10,000 advisor-

purchase type pairs. The regressions reported in Panel A summarize the differences across purchase

types. The omitted category consists of the client-only purchases.

The advisor-only fund purchases are significantly different from client-only purchases. The

differences between client-only and joint purchases, by contrast, are small in economic magnitude

and statistical significance. The funds purchased solely by the advisor have earned higher returns

over the prior year; their percentile rank is, on average 5 points higher than funds purchased or

owned on behalf of clients too. Advisor-only purchases also exhibit less home bias, an estimated

11-percentage point decline in exposure to non-Canadian equities. Finally, purchases made solely

by the advisor are more expensive. The average advisor-only purchase lies 3 percentage points

higher in the fee distribution (within asset class) compared to client-only and joint purchases.

The funds that advisors purchase only for themselves are not better investments than those

purchased just for the client. In Panel B, we create three value-weighted portfolios that select

funds based on the value of the purchases in each of the three categories over the prior one-year

period. The advisor-only portfolio, for example, holds funds in month t that we identify as advisor-

only purchases in months t− 12 to t− 1. The weights are proportional to the total dollar amounts

of these purchases. The two sets of columns in Panel B compare the performance of the advisor-

24

only purchases to the client-only purchases and joint purchases to client-only purchases. The first

difference, for example, can be interpreted as a long-short strategy that purchases funds recently

bought just by advisors, financing these purchases by selling funds recently bought just for the

clients.

The differences in average returns are close to zero and statistically insignificant. In the advisor-

only versus client-only comparison, the difference in the average gross returns is 0.55 percentage

points per year, and this difference is associated with a t-value of 0.90. The regression that adjusts

for the market factor lowers this difference to just −7 basis points. None of the differences in

average returns or alphas in Panel B are more than one standard error away from the zero.

6 Summarizing advisors’ impact: Comparing the risk and return

of client and advisor portfolios

The fee estimates in Table 5 indicate that advisors who choose expensive mutual funds themselves

tend to have clients who do the same. If advisors recommend for their clients the same investments

as they hold personally, then client and advisor performance will correlate in the cross section

because of differences in fees and performance of the mutual funds. Advisors who pay high fees

will underperform those who pay low fees and so will their clients; and advisors whose mutual

fund investments perform relatively poorly will have clients whose returns fall short of those earned

by others. Likewise, advisors who fail to diversify, for example by holding an outsized share of

Canadian equities, will experience more volatile returns themselves and deliver a riskier portfolio

to their clients as well.

25

Panel A of Figure 5 plots the association in fees between advisors and clients by sorting advisors

into deciles based on the average fees that they personally pay on their investments during the

sample period. These fees consist of management expense ratios and deferred sales charges. We

compute the average fee paid by the clients of each advisor and then average across advisors in

each decile. This methodology equal weights the advisors within each decile.

Client fees increase significantly with advisors’ personal fees. Moving from the bottom decile

to the top decile corresponds to an increase of 36 basis points in annualized fees. The standard

deviation of fees in the cross section of clients is 76 basis points. This comparison indicates that

an indirect sort on advisor fees generates considerable amount of dispersion in client fees.

Panels B and C of Figure 5 examine the association between client alphas and advisor fees or

alphas. We construct this graph as follows. First, we estimate each advisor’s gross and net alphas

using a two-factor model that includes the market and term factors. We then sort advisors into

deciles based on these estimated alphas, and form a time-series of client returns within each decile

by taking the cross-sectional average of that month’s advisor-level client return. Similar to the fee

computation, we give each advisor the same weight. Appendix B describes our methodology in

detail.

Panel B of Figure 5 shows that client net alphas decrease significantly in advisor fees. Likewise,

Panel C shows clients’ gross and net alphas increase significantly in advisor alphas. Moving from

the bottom decile to the top decile, the point estimate for the gross alpha increases by 1.17% per

year and the net alpha estimate increases by 1.21%. The differences between the top and bottom

deciles are significant with t-values in excess of 5.0. The positive association between client and

advisor performance in Figure 5 is thereby consistent with advisors and clients following similar

26

trading rules, investing in similar—and sometimes the same—funds, and paying similar amounts

in fees for their holdings.

The fourth panel of Figure 5 examines portfolio risk. For each client and advisor portfolio,

we estimate a two-factor model and calculate the residual, or unexplained, portfolio return for the

full sample of monthly returns. To measure idiosyncratic risk, we then compute the annualized

volatility of those monthly residuals. Less diversified portfolios will display greater idiosyncratic

risk. In Panel D, we sort advisors into deciles based on the idiosyncratic risk of their own portfolio,

which ranges from 3% per year in the bottom decile to 14% per year at the top decile. For each

decile of advisors, we calculate the average idiosyncratic risk for clients of those advisors. We find

that client idiosyncratic risk increases strongly with the advisor’s own idiosyncratic risk. Moving

from the bottom decile to the top decile of the advisor distribution, idiosyncratic risk increases by

more than half, from 3.9% per year to 6.6% per year.12

Table 8 reports cross-sectional Spearman rank correlations between advisors’ alphas and fees

and those of their clients. We estimate gross and net alphas for each advisor and his clients and

then compute the cross-sectional correlation in these estimates. We compute the standard errors by

block bootstrapping the data by calendar month 100 times. In each iteration, we resample calendar

months with replacement, recompute alphas and average fees, and obtain a new set of estimates for

the correlations. The correlations range between 0.37 and 0.40, and they are not sensitive to the

choice of returns (gross or net) or the asset pricing model. The estimates in Table 8 are consistent

with the positive association apparent in Figure 5.

12Figure A2 reports estimates from two alternative specifications. First, we find that the pattern in idiosyncraticrisk is the same when we use the six-factor model for idiosyncratic risk. Second, for each advisor we measure theidiosyncratic risk for his clients’ aggregate portfolio, rather than the average idiosyncratic risk among his clients. Theamount of idiosyncratic risk falls because the aggregate portfolio is better diversified but, similar to Figure 5 Panel D,the idiosyncratic risk increases significantly when we move from a low-risk advisor to the high-risk advisor.

27

An alternative method for assessing the return correlation between advisors and clients is to

estimate a panel regression of client net returns on advisor net returns with month fixed effects,

rclienta,t − rf,t = µt + β(radvisora,t − rf,t) + εa,t, (2)

where radvisora,t is the return on the advisor a’s personal portfolio, rclienta,t is the return on the value-

weighed portfolio of advisor a’s clients, and µt are the month fixed effects. The month fixed effects

subsume all common shocks in the time series. That is, β measures the marginal correlation in

client and advisor returns when holding constant market-wide movements. The estimate of β from

equation (2) is 0.176, which is statistically significant with a t-value of 43.2 when we cluster standard

errors by month. This estimate indicates that if an advisor’s portfolio earns an “idiosyncratic”

return of 10% (that is, a return unrelated to market-wide shocks subsumed by the month fixed

effects), the average return on the client portfolio is 1.8%.

7 Do advisors trade contrary to their beliefs?

7.1 Post-career advisors

Advisors may trade contrary to their personal beliefs for two reasons. First, even though clients

cannot observe advisors’ personal portfolios, advisors could voluntarily disclose this information to

gain their clients’ trust. For example, if an advisor personally invests in expensive actively managed

funds, the client can perhaps be convinced to do the same. Although advisors could lie about their

own investments, doing so might generate legal liabilities. Second, an advisor might suffer from

cognitive dissonance if he advises his clients to invest differently than he invests himself. In both

28

explanations, the advisor-client relationship changes the advisor’s behavior. We test this conjecture

by studying advisors’ behavior after they stop advising clients. We observe more than 400 advisors

that stop advising clients. Nearly 90% of those advisors continue to hold a personal portfolio at

their old firm.

Table 9 presents estimates of advisor behavior before and after advisors exit the industry. The

change in behavior is a pairwise t-test, comparing the behavior of the same advisor before and after

he leaves the industry. The estimates suggest that active advisors’ personal investment behavior is

probably not significantly affected by the presence of their clients. During the post-career period,

advisors trade substantially more often, with turnover of 53% compared to 35% during their career.

This change is inconsistent with the view that they trade actively to convince clients to do the same.

Advisors slightly moderate their return chasing behavior in the post-career period, though they still

purchase funds that are, on average, in the 58th percentile of past-year returns. Post-career advisors

continue to favor actively managed funds and Canadian equities, with allocations similar to when

they were advising clients. Advisors’ annualized management expense ratios decrease by 14 basis

points (t-value = −2.84) after they leave the industry, but this change relates to changes in asset

allocation—the within-asset class fee measure remains nearly unchanged (t-value = 0.26) around

the 46th percentile. Advisors’ preference for expensive mutual funds is thus not specific to the

time they advise clients—they maintain this preference even after there is no need to keep up the

appearances.

29

7.2 Hypothetical investment performance of advisors: What if advisors held

perfect copies of their clients’ portfolios?

In addition to examining changes in advisor behavior after departures from the industry, we can

compute hypothetical advisor returns to assess the plausibility of advisors holding portfolios that

are contrary to their beliefs. If advisors make poor investments only to convince their clients to do

the same, their optimal portfolio should perform no worse than their clients’ portfolio. That is, if

an advisor wants to use his own portfolio to convince his clients to invest in funds A and B, the

best way to do so would be to invest personally in funds A and B in the desired proportions.

In Table 10, we measure the extent to which advisors enhance or hurt their investment returns

by deviating from the portfolio held by their clients. We first report alphas based on advisors’

actual portfolios and trades. We compute net returns after all fees and rebates on this portfolio.

The six-factor model alpha for this return series is −3.49% per year (t-value of −3.57), as reported

in Table 3.

We next report alphas for a hypothetical portfolio that the advisors could have held instead.

This computation replaces the advisor’s personal portfolio with the value-weighted portfolio of his

clients. To make this return series comparable with the actual return series, we assume that the

advisor would have paid the same deferred sales charges as those paid by his clients, and we credit

the advisor with the sales and trailing commissions generated by this client portfolio. The six-factor

model alpha for this hypothetical “perfect-copy” portfolio is −2.67% per year, and this estimate

has a t-value of −2.96. This estimate is higher than the net alpha with fees estimate of −3.22%

for the clients reported in Table 3 because of the sales commissions and trailing commissions that

advisors would earn by serving as agent for their own purchases and holdings.

30

The bottom part of Table 10 measures how much advisors’ alphas would change if they shifted

to holding perfect copies of their clients’ portfolios. In the six-factor model, the annual 0.82%

increase has a t-value of 3.79. This estimate is not particularly sensitive to the choice of the asset

pricing model; the alpha estimates for the return difference range from 0.82% to 1.10% across the

three models.

These estimates suggest that advisors probably do not use their personal investments to convince

their clients to hold particular funds. Under this explanation, we would expect that component of

the advisor’s portfolio that does not overlap with the client portfolio to outperform the overlap-

ping part. In the data, however, this non-overlapping portfolio performs significantly worse than

the client portfolio. Advisors could significantly improve their performance by holding the same

portfolios as held by their clients.

8 Conclusions

Many households turn to financial advisors for guidance, and receive advice that has been criticized

as costly or of low-quality. A central concern, highlighted in academic studies and policy debates

alike, is that advisors lack a fiduciary obligation to clients and are paid through commissions that

may create an agency conflict with their clients. For example, advisors receive more commissions

when their clients trade frequently and larger commissions when their clients purchase expensive,

actively managed funds even if clients do not benefit from those strategies.

We show, however, that many advisors invest personally just as they direct their clients to

invest. They trade actively and tilt their purchases toward expensive, actively managed mutual

funds with high past returns, despite evidence that these strategies often underperform. Advisors

31

also favor domestic assets, both in their own and their clients’ portfolios, despite evidence that in-

ternational diversification can substantially reduce portfolio risk. Notably, these similarities emerge

even though the advisors in our sample lack fiduciary duty and receive compensation through sales

commissions. Advisors pursue similar strategies in their own portfolios even after they stop advis-

ing clients, which rules out strategic “window dressing” in which advisors hold expensive portfolios

merely to convince clients to do the same. Rather than passively implementing their firm’s strategy

as an uninformed salesperson might, advisors of the same dealer display considerable autonomy.

Our results suggest that advisors have the wrong beliefs rather than the wrong incentives.

Differences in advisors’ beliefs, as reflected in their personal investments, predict substantial

differences in clients’ bottom-line investment performance. Advisors in the top decile of investment

returns deliver higher gross returns to clients, a difference of 1.4% per year above compared to clients

of bottom-decile advisors. Advisors in the top decile of portfolio fees likewise deliver portfolios that

cost 0.4% per year more than advisors in the bottom decile. Finally, advisors that invest personally

in high-volatility strategies (controlling for differences in asset allocation) also deliver portfolios

with nearly twice the idiosyncratic volatility as advisors in the bottom decile.

Policies aimed at resolving conflicts of interest—by imposing fiduciary duty or banning commiss-

ions—do not address misguided beliefs. When advisors recommend strategies that underperform,

they often act as an agent exactly as they would as a principal, so aligning their interests would not

change their behavior. Solving the problem of misguided beliefs would instead require improved

education or screening of advisors, perhaps enforced with professional licensing requirements. The

market, of course, may not reward such actions if clients cannot differentiate good and bad advice.

Changing advisors’ views about active investment strategies may also be difficult. Advisors are not

32

random draws from the population, and they may pursue their vocation in part because of their

misguided beliefs. Those who believe that active management adds value, for example, may be

more likely to pursue a career in financial advice.

33

A Appendix: Mutual Fund Fee Structures

The differences in mutual fund fee structures are relevant for advisors’ and clients’ incentives, and

we therefore briefly describe them. Every fund purchase by a client involves four parties: the client,

the advisor, the mutual fund company, and the advisor’s dealer firm. Mutual fund transactions

can generate five types of payments:

1. Front-end load is a direct payment from the client to the advisor at the time of a purchase

of a front-end load fund. The minimum and maximum front-end loads are set by the mutual

fund company, but the mutual fund company does not receive any of this payment.

2. Sales commission is a payment from the mutual fund company to the advisor at the time

of a purchase of a back-end load fund. The typical sales commission is 5% of the value of the

purchase.

3. Deferred sales charge is a payment from the client to the mutual fund company at the

time the client redeems his shares in a back-end load fund. The deferred sales charge typically

starts at the same level as the sales commission, but the penalty is amortized: it is typically

5% of the value of the investment if the fund is sold in the first year, 6/7th of 5% if sold in

the second year, continuing to decrease to 1/7th of 5% if sold in year seven. The seven-year

mark for the expiration of deferred sales charge schedule is the most common, followed by

eight-year schedules. Additionally, some mutual funds free 10% of the shares each year, which

means that the client can sell a fraction of the shares each year without incurring a penalty.

34

The deferred sales charge is based either on the value of the initial purchase or the value of

the shares at the time of the redemption.13

4. Trailing commission is a recurring payment from the mutual fund company to the advisor.

The fund pays the trailing commission for as long as the client remains invested in the fund.

Trailing commissions of 0.25% to 1% per year are standard on all funds sold by advisors.

5. Management expense ratio is a recurring payment from the client to the mutual fund

company. These expenses are subtracted daily from the fund’s net asset value.

6. Administrative fee is a recurring payment from the client to the mutual fund company.

Some mutual funds charge this fee for shares held in retirement accounts.

These payments vary across load structures, and the load structures differ in how they restrict

client behavior by imposing costs:

1. Front-end load fund. The advisor and client negotiate the front-end load, and the investor

is free to sell the mutual fund at any time without any additional cost. The trailing commission

associated with this option is typically the highest because the mutual fund company does

not pay an upfront sales commission to the advisor.

2. Back-end load fund. The client makes no payment to the advisor at the time of the

purchase, but the mutual fund company pays the advisor a sales commissions. If the client

sells the mutual fund “too soon,” he incurs a deferred sales charge. Back-end load funds

13Some mutual fund families let clients “switch” from one fund to another in the same family without triggeringthe deferred sales charge. The client is typically charged a 2% switching fee for this service. When we measureperformance, we combine these switching fees with deferred sales charges. Similarly, some mutual funds, regardless oftheir type, impose restrictions on short-term trading. In the typical arrangement, a client has to pay a 2% short-termtrading fee if he sells the fund within a month of the purchase. We combine also these penalties with deferred salescharges when measuring performance.

35

also often release 10% of the shares each year so that the client can sell these shares without

incurring a sales charge. The trailing commission associated with this option is typically low

because of the upfront sales commission.

3. Low-load fund. These investments are similar to back-end load funds, except that the sales

commission is smaller and the deferred sales charge schedule shorter.

4. No-load fund. The client makes no payment to the advisor at the time of the purchase,

and the mutual fund company does not pay the advisor a sales commission. The trailing

commission is often also 0%.

In every option, the client pays the mutual fund company the management expense ratio. Advisors

share their commissions with their dealer firms. A 2010 industry study of the top ten Canadian

dealers reports that advisors received, on average, 78% of the commission payments (Fusion Con-

sulting 2011).

B Appendix: Method for analyzing client fees and performance

across deciles of advisors

We normalize monthly decile returns by subtracting the average return across the deciles, and run

the two-factor asset pricing regression using these normalized returns. That is, the regression we

estimate is

rd,t − rt = αd + βd,mktMKTRFt + βtermTERMt + ed,t, (3)

where rd,t is decile d’s month t return and rt = (1/10)∑10

d=1 rd,t. We also estimate this regression

with rt−rf,t as the dependent variable, where rf,t is the riskless rate, and we denote this regression’s

36

alpha with α. This normalization strategy gives more precise estimates for comparing performance

across deciles; it removes the time-series variation in returns that is common to all clients. In

Panel B of Figure 5, we plot αd + ˆα to restore the level of alphas, but we take the standard errors

from the normalized regressions, thereby showing only the across-advisors estimation uncertainty.

This procedure removes uncertainty about the level of alpha, which we set equal to its point estimate

for each decile of advisors.14

14Because we take out the time-series variation in returns that is shared by all clients, the standard errors thatwe report are appropriate for comparing alpha estimates across deciles. They are not the correct standard errors fortests concerning the level of alphas, such as for a test of whether decile d’s alpha is significantly different from zero.The estimates reported in Table 3 are appropriate for the latter test.

37

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40

Return chasing estimate

Densityofclients

Densityofadvisors

0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

0

1

2

3

4

5

ClientsAdvisors

Figure 1: Return chasing by clients and advisor. We measure return chasing by computingthe average percentile return rank of mutual funds bought. This figure plots these measures foreach advisor and client with at least 10 mutual fund purchases during the sample period. In thisfigure, the advisor measures on the secondary y-axis are scaled down to make it easier to distinguishthe two distributions from each other.

41

Month relative to the client purchase

Pr(Co-clientpurchasesthesamefund)

!12 !9 !6 !3 0 3 6 9 120

0:1

0:2

0:3

0:4

0:5

Own co-clientsRandom clients (same dealer)Random clients (di,erent dealer)

Figure 2: Similarity in fund purchases and timing between clients and co-clients. Forall purchases of a new fund by a client, we compute the probability that at least one client of thesame advisor (a “co-client”) makes a new purchase of the same fund in the two-year window aroundthe purchase. The solid black line indicates this estimated probability and the dashed black linesindicate the 95% confidence interval associated with these estimates. Repeating this exercise, wecompute the probabilities of common purchase between a client and counterfactual co-clients drawnfrom a different advisor at the same dealer (blue line) or the other dealer (red line). To form theseestimates we resample the data 100 times with replacement and match each client with a randomlydrawn advisor’s clients.

42

Month relative to the advisor purchase

Pr(Clientpurchasesthesamefund)

!12 !9 !6 !3 0 3 6 9 120

0:1

0:2

0:3

0:4

0:5

Own advisorRandom advisor (same dealer)Random advisor (di,erent dealer)

Figure 3: Similarity in fund purchases between advisors and clients. For all purchases ofa new fund by an advisor, we compute the probability that at least one client of the advisor makesa new purchase of the same fund in the two-year window around the purchase. The solid blackline indicates this estimated probability and the dashed black lines indicate the 95% confidenceinterval associated with these estimates. Repeating this exercise, we compute the probabilitiesof common purchase between a client and a counterfactual advisor drawn from the same dealer(blue line) or the other dealer (red line). To form these estimates we resample the data 100 timeswith replacement and randomly match each advisor with the client group of another advisor thatpurchased a new fund in the same month.

43

Advisor

Co-purchase

probability(%

)

Old New New0

1

2

3

4

5

6

7

8Before displacement After displacement

Figure 4: Estimated co-purchase probabilities for displaced clients. This figure studies theco-purchase probabilities of clients and their advisors using the sample of displaced clients. Thebefore-displacement bars denote the probabilities that a client’s current advisor and future (afterdisplacement) advisor purchase the same fund as the client. We measure co-purchase probabilitiesusing a three-month window around the client purchase. The after-displacement bar denotes theprobability that the client’s new advisor (after displacement) purchases the same fund as the client.The before-displacement sample is restricted to purchases when also the future advisor has clients.We compute the average purchase propensity at the advisor level, and then report the averageprobabilities across advisors. The error bars indicate 95% confidence intervals.

44

Panel A: Client fees conditional on advisor fees

Advisor fee decile

Clien

tfe

e,annualize

d%

1 2 3 4 5 6 7 8 9 102:3

2:4

2:5

2:6

2:7

Panel B: Client net alpha conditional on advisor fees

Advisor fee decile

Clien

tnet

alp

ha,annualize

d%

1 2 3 4 5 6 7 8 9 10!3:5

!3:0

!2:5

!2:0

45

Panel C: Client alphas conditional on advisor alphas

Advisor alpha decile

Clientalpha,annualized%

1 2 3 4 5 6 7 8 9 10!4:5

!4:0

!3:5

!3:0

!2:5

!2:0

!1:5

!1:0

!0:5

0:0

0:5

1:0

Gross alphaNet alpha

Panel D: Client idiosyncratic risk conditional on advisor idiosyncratic risk

Advisor idiosyncratic risk decile

Clien

tid

iosy

ncr

atic

risk

,annualize

d%

1 2 3 4 5 6 7 8 9 103

4

5

6

7

Figure 5: Client fees, performance, and idiosyncratic risk conditional on advisor fees,performance, and idiosyncratic risk. This figure sorts advisors into deciles based on theirpersonal fees (Panels A and B), alphas (Panel C), and idiosyncratic portfolio risk (Panel D) andreports the average fees, alphas, portfolio idiosyncratic risks of the clients of these advisors. Thefees consist of management expense ratios, front-end loads, and deferred sales charges. The alphasin Panels B and C are estimated using a two-factor model with the market (equity) and term (fixedincome) factors. In Panels A through C, we compute the 95% confidence intervals after removingthe variation in the time series of fees and returns that is shared by all clients; see the text fordetails. The idiosyncratic risks in Panel D are the annualized volatilities of the residuals fromthe two-factor model. We estimate the two-factor model for every client and advisor, and thencompute, for each advisor, the average volatility over each advisor’s clients.

46

Table 1: Descriptive statistics from dealer data

This table reports demographics and portfolio information for clients and financial advisors, andclient information for financial advisors. “Account age (years)” is the number of years an investor’saccount has been open. “Experience” is the number of years since the advisor obtained a license or,if not available, the number of years after becoming an advisor in the dealer data. “Risky share” isthe fraction of assets invested in equities. In computing the risky share, we assume that balancedfunds invest 50% in equities. In Panel A, we first compute the distributions for each calendar monthand then average these distributions over time. Time horizon, risk tolerance, financial knowledge,income, and net worth information, reported in Panel B, is collected on the “Know-Your-Client”forms. Panel C categorizes clients’ and advisors’ discretionary mutual fund purchases and reportsthe distributions across these types. Discretionary purchases are purchases that are not part ofautomatic savings plans. The client only-category contains purchases in which the advisor doesnot purchase the same fund at the same time and does not hold the fund; the client and advisorpurchase-category contains purchases in which both the client and advisor buy the same fund inthe same month; and the client purchases and advisor holds-category contains purchases in whichthe client purchases a fund that is held by the advisor but not bought in the same month. Theadvisor-side categories are defined analogously.

47

Panel A: Demographics, portfolio characteristics, and client accountsPercentiles

Variable Mean 10th 25th 50th 75th 90th

Clients (N = 488,263)

DemographicsFemale (%) 52.2Age 49.2 32.1 39.7 48.4 58.1 67.4

Investment portfolioAccount age (years) 4.6 0.9 2.3 4.5 6.9 8.0Number of plans 2.0 1.0 1.0 1.7 2.4 3.9Number of funds 4.7 1.0 1.8 3.5 6.3 10.0Account value, $K 55.3 2.2 7.3 23.5 63.4 136.0Risky share (%) 73.3 46.6 56.9 76.5 96.7 100.0

Financial advisors (N = 3,276)

DemographicsFemale (%) 27.1Age 48.4 34.8 40.9 48.3 56.1 62.0

Investment portfolioAccount age (years) 5.4 1.2 2.8 5.2 7.4 8.8Number of plans 3.4 1.0 1.1 2.7 4.6 6.7Number of funds 8.7 1.3 2.9 6.4 12.0 18.7Account value, $K 112.1 3.8 14.7 50.9 130.7 269.2Risky share (%) 80.7 51.2 70.2 88.1 99.7 100.0

Client accountsExperience 5.8 2.3 4.3 6.9 7.0 7.0Number of clients 109.8 4.2 17.7 63.6 153.6 275.2Client assets, $ thousands 6,242.6 94.9 569.4 2,546.8 7,799.2 17,499.5

48

Panel B: Account and client characteristicsAccount types Clients Advisors Time horizon Clients Advisors

General 27.6% 44.3% 1–3 years 2.5% 2.2%Retirement 84.9% 85.6% 4–5 years 8.1% 5.3%Education savings 9.4% 23.0% 6–9 years 69.7% 67.2%Tax-free 4.3% 7.4% 10+ years 19.7% 25.3%Other 0.6% 0.1%

Risk tolerance Clients Advisors Salary Clients Advisors

Very low 4.2% 1.0% $30–50k 34.6% 15.1%Low 4.3% 2.7% $50–70k 35.4% 26.1%Low to Moderate 8.5% 3.1% $70–100k 17.3% 21.6%Moderate 51.5% 30.1% $100–200k 12.1% 34.1%Moderate to High 19.7% 20.7% $200–300k 0.2% 2.2%High 11.9% 42.3% Over $300k 0.2% 0.8%

Financial knowledge Clients Advisors Net worth Clients Advisors

Low 40.3% 1.5% Under $35k 3.6% 1.3%Moderate 54.5% 15.7% $35–60k 6.2% 2.5%High 5.2% 82.8% $60–100k 9.3% 5.9%

$100–200k 18.3% 13.1%Over $200k 62.7% 77.2%

Panel C: Clients’ and advisors’ discretionary mutual fund purchases

Client only 72.5% Advisor only 19.7%Client and advisor purchase 4.3% Advisor and client purchase 43.7%Client purchases, advisor holds 23.3% Advisor purchases, client holds 36.6%

No. of discretionary purchases 8,119,446 No. of discretionary purchases 127,251

49

Table 2: Measures of trading behavior: Clients versus advisors

This table reports estimates of how clients and advisors trade. The measures are defined as follows:(i) Return chasing is the average percentile rank of prior one-year returns for funds bought; (ii)Active management is the proportion of index funds and target-date funds bought; (iii) Turnoveris the market value of purchases and sales divided by the beginning of month market value ofholdings, annualized by multiplying by 12; and (iv) Home bias is the fraction of Canadian equitymutual fund purchases out of all equity fund purchases. We compute these measures for each clientand then aggregate the data to the advisor level. The bottom two rows report two measures offees. “Total MER” is the average management expense ratio of the funds bought by clients andadvisors. “Percentile within asset class” is the average percentile fee rank of funds bought. Wecompute percentile ranks within five asset classes: equity, balanced, fixed income, money market,and alternatives. We include clients’ and advisors’ open and retirement accounts, and in the caseof turnover and home bias, also report the measures separately for the two account types.

Clients Advisors Difference,Behavior Mean SE Mean SE t-value N

Return chasing 60.3 0.2 63.1 0.3 −9.67 2,313Active management 98.5 0.1 98.8 0.2 −1.52 2,380Turnover

Retirement accounts 30.9 0.7 38.9 1.3 −6.15 2,352Open accounts 33.7 0.9 52.2 1.9 −9.83 1,498All 31.5 0.7 40.0 1.2 −7.20 2,589

Home biasRetirement accounts 54.1 0.4 45.7 0.6 15.01 2,131Open accounts 50.6 0.7 42.6 0.9 9.45 1,269All 53.9 0.4 45.9 0.6 15.57 2,330

FeesPercentile within asset class 43.2 0.2 45.9 0.3 −10.10 2,361Total MER 2.36 0.01 2.43 0.01 −6.70 2,364

50

Table 3: Investment performance of clients and advisors

This table reports annualized percentage alphas for clients’ and advisors’ portfolios. We measuregross returns, net returns, and net returns adjusted for fees and (advisor) rebates for value-weightedclient and advisor portfolios. Gross return adds back the management expense ratio; net return isthe return on the investment portfolio without additional fees or rebates; net return with fees andrebates subtracts off front-end loads and deferred sales charges and, for advisors, adds back rebatesearned through sales commissions on personal purchases and trailing commissions earned on fundsheld. We measure alphas using three asset pricing models. The first model is the Sharpe (1964)-Lintner (1965) capital asset pricing model with the excess return on the Canadian equity market asthe market factor; the second model adds the return difference between the long-term and short-term Canadian government bonds (the term factor); and the third model adds the return differencebetween high-yield Canadian corporate debt and investment grade debt (the default factor) andthe North American size, value, and momentum factors.

Factors in the asset pricing modelMKTRF, SMB,

HML, UMD,

Return Return MKTRF MKTRF, TERM TERM, DEF

series type α t(α) α t(α) α t(α)

Clients Gross return 0.14 0.15 −0.11 −0.12 −0.69 −0.78Net return −2.23 2.40 −2.49 −2.64 −3.07 −3.42w/fees and rebates −2.38 2.56 −2.64 −2.80 −3.22 −3.59

Advisors Gross return −0.68 −0.66 −0.88 −0.84 −1.25 −1.29Net return −3.10 −2.99 −3.30 −3.13 −3.66 −3.79w/fees and rebates −2.92 −2.79 −3.11 −2.93 −3.49 −3.57

Clients Gross return 0.82 2.50 0.77 2.30 0.55 2.55− Advisors Net return 0.86 2.62 0.81 2.42 0.60 2.74

w/fees and rebates 0.53 1.61 0.47 1.40 0.27 1.21

51

Table 4: Explaining cross-sectional variation in return chasing behavior with client attributes andadvisor fixed effects

This table evaluates the importance of client attributes, advisor, client, and dealer fixed effects,and province fixed effects in explaining cross-sectional variation in clients’ return chasing behavior.Return chasing is the average percentile rank of prior one-year returns of funds bought. The unitof observation is a client-advisor pair. The first regression in Panel A includes the client attributesand a dealer effect. The second regression adds advisor fixed effects. The age fixed effects are basedon the client’s average age during the time the client is active, measured in five-year increments.Panel B uses a sample that consists of clients who are forced to switch advisors when their oldadvisor dies, retires, or leaves the industry. The model in Panel B includes both advisor and clientfixed effects.

52

Independent Regression 1 Regression 2variable EST t-value EST t-value

Constant 55.12 48.47 56.39 73.56Risk tolerance

Low −0.26 −0.35 −0.21 −0.35Low to Moderate −0.03 −0.04 −0.14 −0.28Moderate 1.48 2.42 0.97 2.01Moderate to High 2.10 3.34 1.29 2.65High 1.47 2.06 0.14 0.26

Financial knowledgeModerate 0.75 4.07 0.28 3.14High 1.48 4.41 0.99 4.30

Time horizonShort 1.95 3.87 1.82 4.27Moderate 1.62 3.35 1.75 4.33Long 0.94 1.84 1.52 3.69

Female −0.06 −0.62 0.06 0.94French speaking 1.19 2.19 0.26 0.83Salary

$30-50k −0.05 −0.46 −0.03 −0.37$50-70k 0.17 1.02 0.22 2.21$70-100k 0.16 0.87 0.26 2.18$100-200k −2.61 −1.95 −1.43 −1.17Over $200k 0.19 0.28 0.20 0.37

Net worth$35-60k 0.64 2.60 0.36 1.65$60-100k 0.66 2.56 0.35 1.66$100-200k 0.85 3.63 0.52 2.58Over $200k 1.37 5.36 0.79 3.95

Advisor FEs No YesDealer FE Yes –Age FEs Yes YesProvince FEs Yes Yes

N 311,032 311,032

Adjusted R2 1.1% 16.5%w/o Dealer FE 1.0%

53

Panel B: Regressions with advisor and client fixed effectsAdvisor FEs Client FEs Adjusted R2

Yes No 19.7%No Yes 5.1%Yes Yes 29.1%

Test: Client FEs jointly zero F (9537, 2495) = 1.30Test: Advisor FEs jointly zero F (154, 1402) = 4.19

Number of observations 12,476

54

Table 5: Explaining cross-sectional variation in client behavior with client attributes, advisor fixedeffects, client fixed effects, and advisor behavior

Panel A evaluates the importance of client attributes, advisor fixed effects, and province fixed effectsin explaining cross-sectional variation in client behavior. The measures of behavior are describedin text and summarized in Table 2. The unit of observation is a client-advisor pair. The estimatesreported in Panel A are the models’ adjusted R2s. The measures pool trades and holdings acrossopen and retirement accounts. Panel B estimates models with advisor and client fixed effects usinga sample of displaced clients. These are clients who are forced to switch advisors when their oldadvisor dies, retires, or leaves the industry.

Panel A: Regressions with client attributes and advisor fixed effectsClient attributes Client attributes

Behavior Client attributes + dealer effect + advisor FEs N

Return chasing 1.0% 1.1% 16.5% 311,032Active management 0.9% 0.9% 18.0% 325,472Turnover 0.7% 0.7% 7.1% 387,640Home bias 2.7% 2.9% 25.9% 312,085

FeesTotal MER 8.1% 8.3% 26.1% 322,968Percentile within asset class 3.0% 3.1% 22.4% 321,064

Panel B: Two-way fixed effects models for client behaviorClient Advisor Both

Behavior FEs FEs FEs N

Return chasing 5.1% 19.7% 29.1% 12,476Active management 8.8% 34.1% 49.0% 13,259Turnover 7.2% 13.9% 21.9% 22,764Home bias 33.3% 31.5% 47.1% 12,544

FeesTotal MER 57.0% 34.3% 67.3% 13,161Percentile within asset class 30.7% 29.3% 47.9% 13,076

55

Table 6: Explaining advisor fixed effects with advisor attributes and investment behavior

This table reports estimates from regressions of advisor fixed effects on advisor attributes andinvestment behavior. These advisor fixed effects are from Table 5’s all-clients regressions of clientbehavior on client attributes and advisor fixed effects.

Active FeesReturn manage- Home Total Cond.

Regressor chasing ment Turnover bias MER percentile

Advisor fixed effects from simple regressions

Advisor behavior 0.24 0.29 0.16 0.34 0.13 0.27(13.67) (4.21) (2.71) (18.89) (3.73) (15.54)

Adjusted R2 22.3% 19.2% 7.3% 25.6% 9.9% 20.1%w/o Advisor attributes 18.7% 18.4% 6.9% 24.1% 9.3% 20.7%

N 1,982 2,105 2,209 1,983 2,073 2,056

Advisor fixed effects from the two-way FE analysis

Advisor behavior 0.29 0.26 0.22 0.28 0.14 0.25(5.48) (1.87) (2.78) (6.01) (2.22) (4.57)

Adjusted R2 6.4% 6.7% 2.2% 7.9% 2.1% 7.3%w/o Advisor attributes 5.9% 5.5% 1.5% 5.4% 2.3% 6.1%

N 592 624 739 598 616 613

56

Table 7: Differences in mutual fund purchases between advisors and clients

We categorize clients’ and advisors’ discretionary mutual fund purchases depending on who pur-chases the fund and whether the other party already owns the same fund or purchases it at thesame time. A purchase is a client-only purchase if the advisor does not purchase the same fundat the same time and does not hold the fund; it is an advisor-only purchase if none the clientspurchase the same fund at the same time and do not hold the fund. A purchase is a joint purchaseif both the client and purchase the same fund at the same time, or if the other party alreadyowns the fund at the time of the purchase. Panel A compares the characteristics of the mutualfunds bought by regressing the percentile rank of past returns, home bias, MER, and percentilefee on advisor-only and joint-purchase indicator variables. The omitted category is the client-onlycategory. The unit of observation is an advisor-purchase type pair, and the standard errors clusterby advisor. Panel B computes value-weighted returns on portfolios that choose weights based onwhich funds are bought in each category. The returns are net returns without an adjustment foradditional fees and rebates. A fund’s weight in month t in purchase category c is proportional tothe total value of purchases of this fund in category c in months t− 12 to t− 1. Panel B comparesreturns advisor-only and client-only purchases and joint purchases and client-only purchases.

Panel A: Differences in the characteristics of mutual fund purchasesDependent variable

Return Home FeesRegressor chasing bias MER Percentile fee

Intercept 59.36 52.71 2.39 44.14[0.19] [0.38] [0.01] [0.19]

Advisor-only purchase 5.06 −11.23 0.05 2.77[0.40] [0.79] [0.01] [0.38]

Joint purchase −0.30 0.96 0.03 −0.70[0.36] [0.70] [0.01] [0.34]

R2 1.6% 2.2% 0.2% 0.6%N 10,558 10,368 10,697 10,675

Panel B: Differences in future fund returns (%) conditional on purchase typeReturn difference

Advisor-only purchase Joint purchase− Client-only purchase − Client-only purchase

Model α t(α) α t(α)

Gross return Average return 0.55 0.90 0.37 0.80CAPM −0.07 −0.14 −0.09 −0.21Six-factor model −0.01 −0.02 −0.10 −0.25

Net return Average return 0.36 0.59 0.41 0.88CAPM −0.26 −0.51 −0.05 −0.12Six-factor model −0.19 −0.44 −0.06 −0.16

57

Table 8: Correlations between advisors’ and their clients’ alphas and fees

This table reports Spearman rank correlations between advisors’ and their clients’ gross and netalphas and fees. The gross and net alphas are estimated using three asset pricing models that aredescribed in Table 3. The fees consist of management expense ratios, front-end loads, and deferredsales charges. We block bootstrap the data by calendar month 100 times to compute the standarderrors. We include advisors and clients with at least one year of returns.

Factors in the CorrelationMeasure asset pricing model EST Bootstrapped SE

Gross alphas MKTRF 0.40 0.04MKTRF, TERM 0.40 0.04MKTRF, SMB, HML, UMD, TERM, DEF 0.37 0.04

Net alphas MKTRF 0.40 0.04MKTRF, TERM 0.40 0.04MKTRF, SMB, HML, UMD, TERM, DEF 0.37 0.03

Fees None 0.37 0.01

58

Table 9: Change in advisor behavior after the end of the career

This table reports estimates how advisor behavior changes after the advisor stops advising clients.The measures are defined in Table 2. We include advisors who execute trades suitable for computingthe four measures of behavior and the two fee measures both before and after they stop advisingclients.

Active Post-careeradvisors advisors Difference

Behavior EST SE EST SE EST t-value N

Return chasing 63.4 1.2 58.3 1.5 −5.1 −2.64 168Active management 99.5 0.2 98.6 0.5 −0.9 −1.99 195Turnover 35.0 4.0 53.4 6.4 18.4 2.74 420Home bias 48.6 2.4 52.9 2.8 4.2 1.47 171

FeesPercentile within asset class 45.8 1.1 46.2 1.4 0.4 0.26 183Total MER 2.47 0.03 2.33 0.04 −0.15 −2.84 184

59

Table 10: Hypothetical advisor returns from holding perfect copies of client portfolios

This table reports actual and hypothetical annualized net alphas for the advisors’ value-weightedaggregate portfolio. The hypothetical net alphas are computed by assuming that the advisors holdperfect copies of their clients’ portfolios. The return on this portfolio equals the net return earnedby the clients, adjusted for the sales commissions that advisors earn on their personal purchases andfor the trailing commissions that they earn on their personal holdings. In this computation, advisorspay the same deferred sales charges as those paid by the clients. The net alphas are computed usingthe three asset pricing models described in Table 3. t-values are reported in parentheses.

Factors in the asset pricing modelMKTRF, SMB,

HML, UMD,

Advisor MKTRF MKTRF, TERM TERM, DEF

portfolio α R2 α R2 α R2

Actual −2.92 85.5% −3.11 85.5% −3.49 88.3%(−2.79) (−2.93) (−3.57)

Hypothetical −1.82 84.7% −2.07 84.8% −2.67 86.9%(−1.94) (−2.19) (−2.96)

Hypothetical 1.10 51.2% 1.04 51.2% 0.82 80.5%− actual (3.36) (3.13) (3.79)

60

Month relative to the advisor purchase

Pr(Clientpurchasesthesamefund)

!12 !9 !6 !3 0 3 6 9 120

0:1

0:2

0:3

0:4

0:5

Own advisorRandom advisor (same dealer)Random advisor (di,erent dealer)

Figure A1: Similarity in fund purchases and timing between advisors and their clients:Robustness. This figure repeats the analysis of Figure 3 Panel A in a sample limited to purchasesmade by advisors who have at most 10 clients at the time of the purchase. The black lines indicatethe actual purchase probabilities by the clients and the 95% confidence intervals associated withthese probabilities. The blue and red lines indicate purchase probabilities simulated under the nullhypothesis that advisors’ purchases are independent of those of their clients. We resample thedata 100 times with replacement and randomly match an advisor with another advisor who alsopurchases a new fund in the same month. The blue line draws the random advisors from the samedealer firm; the red line draws these advisors from the other dealer firm.

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Advisor idiosyncratic risk decile

Clientidiosyncraticrisk,annualized%

1 2 3 4 5 6 7 8 9 103

4

5

6

7

Average client, six-factor modelAggregate client, two-factor model

Figure A2: Client idiosyncratic risk conditional on advisor idiosyncratic risk. This figuresorts advisors into deciles based on their idiosyncratic portfolio risk and reports the average portfolioidiosyncratic risks of the clients of these advisors. The black line is drawn by first measuring theidiosyncratic risk for each client and advisor as the volatility of the residual return from the six-factor model. We then compute the average volatility over each advisor’s clients. This line plotsthe idiosyncratic volatility of the average client of the average advisor in each decile. The red linecomputes the value-weighted return earned by the aggregate client portfolio, and measures thevolatility of the residual return from the two-factor model with the market and term factors. Thisline plots the idiosyncratic volatility of the aggregate client of the average advisor in each decile.The bars denote the 95% confidence intervals.

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Table A1: Estimated probabilities of advisor survival

This table reports estimates from linear probability models that examine the relationship betweenadvisor survival and the number of clients. The data are annual. If an advisor serves clients inyear y and continues to do so in year y + 1, the dependent variable takes the value of one. If anadvisor stops advising clients during the following year, the dependent variable takes the value ofzero. The regressors consist of indicator variables that indicate the number of clients the advisorhas in year y. Advisors with more than 100 clients are the omitted category. The regressions areestimated with year fixed effects and standard errors are clustered by advisor.

Regressor Coefficient SE

Intercept 0.989 0.002Number of clients

1–5 −0.177 0.0106–10 −0.083 0.01011–15 −0.051 0.00916–20 −0.034 0.00821–30 −0.040 0.00731–40 −0.027 0.00641–50 −0.019 0.00650–100 −0.016 0.004

Adjusted R2 8.28%N 98,974

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Table A2: Investment performance of clients and advisors: Factor loadings and model fit

This table reports factor loadings and adjusted R2 statistics for the six-factor model performanceregressions reported in Table 3. The six factors consist of the the excess return on the Canadianequity market (MKTRF); North American size (SMB), value (HML), and momentum (UMD)factors; the return difference between the long-term and short-term Canadian government bonds(TERM); and the return difference between high-yield Canadian corporate debt and investmentgrade debt (DEF).

Return seriesClients

Clients Advisors − Advisors

Factor b SE b SE b SE

MKTRF 0.540 0.019 0.597 0.021 −0.057 0.005SMB 0.074 0.026 0.111 0.028 −0.036 0.006HML 0.018 0.021 −0.038 0.023 0.057 0.005UMD −0.036 0.013 −0.029 0.014 −0.007 0.003TERM 0.118 0.038 0.098 0.041 0.020 0.009DEF 0.135 0.037 0.158 0.040 −0.023 0.009

Adjusted R2 87.0% 88.4% 79.3%

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Table A3: Decomposition of the return gap between the client and advisor net returns

We measure the difference in net returns between clients and advisors, and decompose this returndifference into four components. We compute net returns after management expense ratios butbefore additional expenses and rebates. “Style gross alpha” is the difference in style gross returns,where style gross returns are computed by replacing every fund with the average fund of the samestyle. “Within-style gross alpha” is the difference in within-style gross returns, where the within-style return is computed as the difference between the actual fund return and the return earnedby the average fund of the same style. “Style fee” is the difference in style fees, where style fee isdefined as the management expense ratio of the average fund of the same style. “Within-style fee”is the difference in within-style fees, which are computed as the differences between actual fundMER and the MER of the average fund of the same style. These four components add up to thetotal difference in net returns between clients and advisors shown on the bottom row The first setof columns report time-series averages of these components for value-weighted advisor and clientportfolios. The second set of columns reports the six-factor model alphas for these components andfor the total difference. The t-values associated with the style fee and within-style fee componentsare large because these differences are very stable in the time-series.

Client-minus-advisor Excess returns Six-factor modelreturn component Estimate t-value Estimate t-value

Style gross alpha 0.33 1.64 0.27 1.51Within-style gross alpha 0.49 2.36 0.28 2.12Style fee 0.05 42.35 0.05 41.92Within-style fee −0.01 −3.71 −0.01 −4.03

Total difference 0.86 2.62 0.60 2.74

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