Download The Misguided Beliefs of Financial Advisors

The Misguided Beliefs of Financial Advisors∗
Juhani T. Linnainmaa
Brian T. Melzer
Alessandro 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 Northwestern University, and Alessandro Previtero is with the Indiana University and NBER. We thank Jason Allen, Diego
Garcia, Chuck Grace, John Griffin, Jonathan Reuter, and Sheridan Titman for valuable comments. We are grateful for feedback given by conference and seminar participants at Boston College, Dartmouth College, University of
Maryland, Georgetown University, HEC Montreal, Indiana University, Northwestern University, University of Texas
at 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 especially
grateful to Univeris, Fundata, and three anonymous financial firms for donating data and giving generously of their
time. 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 Chicago
Booth School of Business. Address correspondence to Alessandro Previtero, Indiana University, 1275 E. 10th St.,
Bloomington, IN 47405, USA (email: [email protected]).
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 managed 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
1
See, for example, Bergstresser, Chalmers, and Tufano (2009), Mullainathan, Nöth, and Schoar (2012), Christoffersen, Evans, and Musto (2013), Anagol, Cole, and Sarkar (2016), and Egan (2016).
2
In 2012, the Australian government implemented the Future of Financial Advice Reform, which banned conflicted
compensation arrangements, including commissions. In 2013, the Financial Conduct Authority in the United Kingdom
banned commissions. In 2016, the United States Department of Labor finalized a rule to address conflicts of interest
in retirement advice by imposing fiduciary duty on advisors providing such a service.
1
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 performance: 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 betterthan-average historical returns and overwhelmingly favor expensive, actively managed funds. This
similarity in trading patterns suggests that advisors do not dramatically alter their recommendations when acting as agents rather than principals.
An analysis of fees and investment returns likewise shows little evidence that advisors recommend worse performing funds for clients than they hold themselves. The average expense ratios of
3
These behavioral patterns have been studied extensively. See, for example, Nofsinger and Sias (1999), Grinblatt
and Keloharju (2001b), Barber and Odean (2008), and Kaniel, Saar, and Titman (2008) for analyses of how investors
trade 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 and
Keloharju (2001a) for studies of the home bias; and French (2008) for a discussion of the underperformance of active
management. These behavioral patterns may lead to both underperformance and underdiversification. French and
Poterba (1991) show that expected domestic returns would need to exceed foreign expected returns by hundreds of
basis points per year to offset home bias’s negative effect on portfolio diversification. Barber and Odean (2000) find
that active trading—which can result from, e.g., return chasing—significantly lowers individual investors’ portfolio
returns. Carhart (1997) shows that returns decrease with fund turnover and expenses reduce performance at least
one-for-one.
2
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.
3
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 chasing, 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 recommendations. 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 windowdressing, 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
4
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, Nöth, 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.
6
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 Canadian 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 individual 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.
4
These firms are among those studied by Foerster et al. (2015). Two of the firms in that study did not provide
the identifiers necessary for matching advisors to their personal portfolios and comparing client and advisor behavior.
We exclude these two dealers throughout this study.
5
Under Canadian securities legislation, advisors do not have fiduciary duty, but they have a duty to make suitable
investment recommendations, based on their clients’ investment goals and risk tolerance. They are not required to
put the client’s interests before their own, but they are legally mandated to “deal fairly, honestly and in good faith
with their clients” (Canadian Securities Administrators 2012).
7
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.
6
Table A1 in the Appendix presents an analysis of advisor survival as a function of the number of clients. The
estimates show that advisors with more than 100 clients have a survival rate of 98.9%. This survival rate decreases
almost monotonically as the number of clients falls, and reaches 81.2% among advisors with at most five clients.
8
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.”
7
Foerster et al. (2015) give examples of the descriptions that accompany some of the risk-tolerance and financialknowledge 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
10
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 backend 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.
8
Advisors do 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 Consulting 2011). We
therefore assume that advisors keep 78% of commissions in calculating their net cost of investment.
11
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 taxdeferred 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
12
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.
9
The category “alternatives” includes funds classified as commodity, real estate, and retail venture capital.
13
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 estimate 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
10
Table 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.
15
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 exhibit 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.
16
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 effects, 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 management 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 coclients. 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 establishing 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 future 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 purchases, 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 exhibited 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
11
Figure A1 estimates the same probabilities using data on advisors who have no more than ten clients at the time
of the purchase. The estimated probabilities for this sample are approximately the same as what they are in Figure 3
Panel 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 advisorpurchase 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 advisoronly 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 advisoronly 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.
12
Figure A2 reports estimates from two alternative specifications. First, we find that the pattern in idiosyncratic
risk is the same when we use the six-factor model for idiosyncratic risk. Second, for each advisor we measure the
idiosyncratic risk for his clients’ aggregate portfolio, rather than the average idiosyncratic risk among his clients. The
amount 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,
client
advisor
ra,t
− rf,t = µt + β(ra,t
− rf,t ) + εa,t ,
(2)
advisor is the return on the advisor a’s personal portfolio, r client is the return on the valuewhere ra,t
a,t
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
7.1
Do advisors trade contrary to their beliefs?
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 overlapping 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 international 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 advising 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 commissions—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
13
Some mutual fund families let clients “switch” from one fund to another in the same family without triggering
the deferred sales charge. The client is typically charged a 2% switching fee for this service. When we measure
performance, we combine these switching fees with deferred sales charges. Similarly, some mutual funds, regardless of
their type, impose restrictions on short-term trading. In the typical arrangement, a client has to pay a 2% short-term
trading fee if he sells the fund within a month of the purchase. We combine also these penalties with deferred sales
charges 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 Consulting 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 − r̄t = αd + βd,mkt MKTRFt + βterm TERMt + ed,t ,
where rd,t is decile d’s month t return and r̄t = (1/10)
P10
d=1 rd,t .
(3)
We also estimate this regression
with r̄t −rf,t as the dependent variable, where rf,t is the riskless rate, and we denote this regression’s
36
alpha with ᾱ. 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
ˆ to restore the level of alphas, but we take the standard errors
Panel B of Figure 5, we plot α̂d + ᾱ
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
14
Because we take out the time-series variation in returns that is shared by all clients, the standard errors that
we report are appropriate for comparing alpha estimates across deciles. They are not the correct standard errors for
tests 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
REFERENCES
Anagol, S., S. Cole, and S. Sarkar (2016). Understanding the advice of commissions-motivated
agents: Evidence from the indian life insurance market. Review of Economics and Statistics.
Forthcoming.
Barber, B. M. and T. Odean (2000). Trading is hazardous to your wealth: The common stock
investment performance of individual investors. Journal of Finance 55 (2), 773–806.
Barber, B. M. and T. Odean (2008). All that glitters: The effect of attention and news on the
buying behavior of individual and institutional investors. Review of Financial Studies 21 (2),
785–818.
Bergstresser, D., J. M. R. Chalmers, and P. Tufano (2009). Assessing the costs and benefits of
brokers in the mutual fund industry. Review of Financial Studies 22 (10), 4129–4156.
Bronnenberg, B. J., J.-P. Dubé, M. Gentzkow, and J. M. Shapiro (2015). Do pharmacists buy
bayer? informed shoppers and the brand premium. Quarterly Journal of Economics 130 (4),
1669–1726.
Canadian Securities Administrators (2012). Mutual fund fees. Discussion paper and request for
comment 81-407.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance 52 (1),
57–82.
Chalmers, J. and J. Reuter (2015). Is conflicted investment advice better than no advice? Working paper.
Cheng, I., S. Raina, and W. Xiong (2014). Wall street and the housing bubble. American Economic Review 104 (9), 2797–2829.
Christoffersen, S. E. K., R. Evans, and D. K. Musto (2013). What do consumers’ fund flows
maximize? Evidence from their brokers’ incentives. Journal of Finance 68 (1), 201–235.
Coval, J. D. and T. J. Moskowitz (1999). Home bias at home: Local equity preference in domestic
portfolios. Journal of Finance 54 (6), 2045–2073.
Dvorak, T. (2015). Do 401k plan advisors take their own advice? Journal of Pension Economics
and Finance 14 (1), 55–75.
Egan, M. (2016). Brokers vs. retail investors: Conflicting interests and dominated products.
Working paper.
Egan, M., G. Matvos, and A. Seru (2015). The market for financial adviser misconduct. University
of Chicago working paper.
38
Foerster, S., J. T. Linnainmaa, B. T. Melzer, and A. Previtero (2015). Retail financial advice:
Does one size fit all? Journal of Finance. Forthcoming.
French, K. R. (2008). Presidential address: The cost of active investing. Journal of Finance 63 (4),
1537–1573.
French, K. R. and J. Poterba (1991). Investor diversification and international equity markets.
American Economic Review 81 (2), 222–226.
Fusion Consulting (2011). State of the industry.
Gennaioli, N., A. Shleifer, and R. Vishny (2015). Money doctors. Journal of Finance 70 (1),
91–114.
Grinblatt, M. and M. Keloharju (2001a). How distance, language, and culture influence stockholdings and trades. Journal of Finance 56 (3), 1053–1073.
Grinblatt, M. and M. Keloharju (2001b). What makes investors trade? Journal of Finance 56 (2),
589–616.
Grinblatt, M. and M. Keloharju (2009). Sensation seeking, overconfidence, and trading activity.
Journal of Finance 64 (2), 549–578.
Grinblatt, M., M. Keloharju, and J. Linnainmaa (2012). IQ, trading behavior, and performance.
Journal of Financial Economics 104 (2), 339–362.
Hackethal, A., R. Inderst, and S. Meyer (2012). Trading on advice. CEPR Discussion Paper
8091.
Hoechle, D., S. Ruenzi, N. Schaub, and M. Schmid (2015). Financial advice and bank profits.
Swiss Institute of Banking and Finance Working paper.
Investment Company Institute (2012). 2012 investment company fact book, 52nd edition.
Kaniel, R., G. Saar, and S. Titman (2008). Individual investor trading and stock returns. Journal
of Finance 63 (1), 273–310.
Lakonishok, J., A. Shleifer, R. Thaler, and R. Vishny (1991). Window dressing by pension fund
managers. American Economic Review 81 (2), 227–231.
Levitt, S. D. and C. Syverson (2008). Market distortions when agents are better informed: The
value of information in real estate transactions. Review of Economics and Statistics 90 (4),
599–611.
Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock
portfolios and capital budgets. Review of Economics and Statistics 47 (1), 13–37.
39
Mullainathan, S., M. Nöth, and A. Schoar (2012). The market for financial advice: An audit
study. NBER Working Paper No. 17929.
Nofsinger, J. R. and R. W. Sias (1999). Herding and feedback trading by institutional and
individual investors. Journal of Finance 54 (6), 2263–2295.
Odean, T. (1999). Do investors trade too much? American Economic Review 89 (5), 1279–1298.
Pakula, D. C., D. J. Walker, D. T. Kwon, P. M. Bosse, V. Maciulis, and C. B. Philips (2014).
Global equities: Balancing home bias and diversification—a Canadian investor’s perspective.
Vanguard reseach.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of
risk. Journal of Finance 19 (3), 425–442.
Sias, R. W. and L. T. Starks (1997). Institutions and individuals at the turn-of-the-year. Journal
of Finance 52 (4), 1543–1562.
40
5
5
4
4
3
3
2
2
1
1
0
0
10
20
30
40
50
60
70
80
90
Density of advisors
Density of clients
Clients
Advisors
0
100
Return chasing estimate
Figure 1: Return chasing by clients and advisor. We measure return chasing by computing
the average percentile return rank of mutual funds bought. This figure plots these measures for
each advisor and client with at least 10 mutual fund purchases during the sample period. In this
figure, the advisor measures on the secondary y-axis are scaled down to make it easier to distinguish
the two distributions from each other.
41
Pr(Co-client purchases the same fund)
0:5
0:4
Own co-clients
Random clients (same dealer)
Random clients (di,erent dealer)
0:3
0:2
0:1
0
!12
!9
!6
!3
0
3
Month relative to the client purchase
6
9
12
Figure 2: Similarity in fund purchases and timing between clients and co-clients. For
all purchases of a new fund by a client, we compute the probability that at least one client of the
same advisor (a “co-client”) makes a new purchase of the same fund in the two-year window around
the purchase. The solid black line indicates this estimated probability and the dashed black lines
indicate the 95% confidence interval associated with these estimates. Repeating this exercise, we
compute the probabilities of common purchase between a client and counterfactual co-clients drawn
from a different advisor at the same dealer (blue line) or the other dealer (red line). To form these
estimates we resample the data 100 times with replacement and match each client with a randomly
drawn advisor’s clients.
42
Pr(Client purchases the same fund)
0:5
0:4
Own advisor
Random advisor (same dealer)
Random advisor (di,erent dealer)
0:3
0:2
0:1
0
!12
!9
!6
!3
0
3
Month relative to the advisor purchase
6
9
12
Figure 3: Similarity in fund purchases between advisors and clients. For all purchases of
a new fund by an advisor, we compute the probability that at least one client of the advisor makes
a new purchase of the same fund in the two-year window around the purchase. The solid black
line indicates this estimated probability and the dashed black lines indicate the 95% confidence
interval associated with these estimates. Repeating this exercise, we compute the probabilities
of 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 times
with replacement and randomly match each advisor with the client group of another advisor that
purchased a new fund in the same month.
43
8
Before displacement
After displacement
Co-purchase probability (%)
7
6
5
4
3
2
1
0
Old
New
New
Advisor
Figure 4: Estimated co-purchase probabilities for displaced clients. This figure studies the
co-purchase probabilities of clients and their advisors using the sample of displaced clients. The
before-displacement bars denote the probabilities that a client’s current advisor and future (after
displacement) advisor purchase the same fund as the client. We measure co-purchase probabilities
using a three-month window around the client purchase. The after-displacement bar denotes the
probability 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 average
probabilities across advisors. The error bars indicate 95% confidence intervals.
44
Panel A: Client fees conditional on advisor fees
Client fee, annualized %
2:7
2:6
2:5
2:4
2:3
1
2
3
4
5
6
7
8
9
10
9
10
Advisor fee decile
Panel B: Client net alpha conditional on advisor fees
Client net alpha, annualized %
!2:0
!2:5
!3:0
!3:5
1
2
3
4
5
6
Advisor fee decile
45
7
8
Panel C: Client alphas conditional on advisor alphas
1:0
Gross alpha
Net alpha
0:5
Client alpha, annualized %
0:0
!0:5
!1:0
!1:5
!2:0
!2:5
!3:0
!3:5
!4:0
Advisor alpha decile
!4:5
1
2
3
4
5
6
7
8
9
10
Panel D: Client idiosyncratic risk conditional on advisor idiosyncratic risk
Client idiosyncratic risk, annualized %
7
6
5
4
3
1
2
3
4
5
6
7
8
9
10
Advisor idiosyncratic risk decile
Figure 5: Client fees, performance, and idiosyncratic risk conditional on advisor fees,
performance, and idiosyncratic risk. This figure sorts advisors into deciles based on their
personal fees (Panels A and B), alphas (Panel C), and idiosyncratic portfolio risk (Panel D) and
reports the average fees, alphas, portfolio idiosyncratic risks of the clients of these advisors. The
fees consist of management expense ratios, front-end loads, and deferred sales charges. The alphas
in Panels B and C are estimated using a two-factor model with the market (equity) and term (fixed
income) factors. In Panels A through C, we compute the 95% confidence intervals after removing
the variation in the time series of fees and returns that is shared by all clients; see the text for
details. The idiosyncratic risks in Panel D are the annualized volatilities of the residuals from
the two-factor model. We estimate the two-factor model for every client and advisor, and then
compute, 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, and
client information for financial advisors. “Account age (years)” is the number of years an investor’s
account 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” is
the fraction of assets invested in equities. In computing the risky share, we assume that balanced
funds invest 50% in equities. In Panel A, we first compute the distributions for each calendar month
and 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 reports
the distributions across these types. Discretionary purchases are purchases that are not part of
automatic savings plans. The client only-category contains purchases in which the advisor does
not purchase the same fund at the same time and does not hold the fund; the client and advisor
purchase-category contains purchases in which both the client and advisor buy the same fund in
the same month; and the client purchases and advisor holds-category contains purchases in which
the client purchases a fund that is held by the advisor but not bought in the same month. The
advisor-side categories are defined analogously.
47
Panel A: Demographics, portfolio characteristics, and client accounts
Percentiles
th
th
Variable
Mean
10
25
50th
75th
90th
Clients (N = 488,263)
Demographics
Female (%)
Age
Investment portfolio
Account age (years)
Number of plans
Number of funds
Account value, $K
Risky share (%)
52.2
49.2
32.1
39.7
48.4
58.1
67.4
4.6
2.0
4.7
55.3
73.3
0.9
1.0
1.0
2.2
46.6
2.3
1.0
1.8
7.3
56.9
4.5
1.7
3.5
23.5
76.5
6.9
2.4
6.3
63.4
96.7
8.0
3.9
10.0
136.0
100.0
Financial advisors (N = 3,276)
Demographics
Female (%)
Age
Investment portfolio
Account age (years)
Number of plans
Number of funds
Account value, $K
Risky share (%)
Client accounts
Experience
Number of clients
Client assets, $ thousands
27.1
48.4
34.8
40.9
48.3
56.1
62.0
5.4
3.4
8.7
112.1
80.7
1.2
1.0
1.3
3.8
51.2
2.8
1.1
2.9
14.7
70.2
5.2
2.7
6.4
50.9
88.1
7.4
4.6
12.0
130.7
99.7
8.8
6.7
18.7
269.2
100.0
5.8
109.8
6,242.6
2.3
4.2
94.9
4.3
17.7
569.4
6.9
63.6
2,546.8
7.0
153.6
7,799.2
7.0
275.2
17,499.5
48
Panel B: Account and client characteristics
Account types
Clients
Advisors
General
27.6%
44.3%
Retirement
84.9%
85.6%
Education savings
9.4%
23.0%
Tax-free
4.3%
7.4%
Other
0.6%
0.1%
Time horizon
1–3 years
4–5 years
6–9 years
10+ years
Clients
2.5%
8.1%
69.7%
19.7%
Advisors
2.2%
5.3%
67.2%
25.3%
Risk tolerance
Very low
Low
Low to Moderate
Moderate
Moderate to High
High
Clients
4.2%
4.3%
8.5%
51.5%
19.7%
11.9%
Advisors
1.0%
2.7%
3.1%
30.1%
20.7%
42.3%
Salary
$30–50k
$50–70k
$70–100k
$100–200k
$200–300k
Over $300k
Clients
34.6%
35.4%
17.3%
12.1%
0.2%
0.2%
Advisors
15.1%
26.1%
21.6%
34.1%
2.2%
0.8%
Financial knowledge
Low
Moderate
High
Clients
40.3%
54.5%
5.2%
Advisors
1.5%
15.7%
82.8%
Net worth
Under $35k
$35–60k
$60–100k
$100–200k
Over $200k
Clients
3.6%
6.2%
9.3%
18.3%
62.7%
Advisors
1.3%
2.5%
5.9%
13.1%
77.2%
Panel C: Clients’ and advisors’ discretionary mutual
Client only
72.5%
Client and advisor purchase
4.3%
Client purchases, advisor holds
23.3%
No. of discretionary purchases
8,119,446
fund purchases
Advisor only
Advisor and client purchase
Advisor purchases, client holds
No. of discretionary purchases
49
19.7%
43.7%
36.6%
127,251
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) Turnover
is the market value of purchases and sales divided by the beginning of month market value of
holdings, annualized by multiplying by 12; and (iv) Home bias is the fraction of Canadian equity
mutual fund purchases out of all equity fund purchases. We compute these measures for each client
and then aggregate the data to the advisor level. The bottom two rows report two measures of
fees. “Total MER” is the average management expense ratio of the funds bought by clients and
advisors. “Percentile within asset class” is the average percentile fee rank of funds bought. We
compute 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 case
of turnover and home bias, also report the measures separately for the two account types.
Behavior
Return chasing
Active management
Turnover
Retirement accounts
Open accounts
All
Home bias
Retirement accounts
Open accounts
All
Fees
Percentile within asset class
Total MER
Clients
Mean
SE
60.3
0.2
98.5
0.1
Advisors
Mean
SE
63.1
0.3
98.8
0.2
Difference,
t-value
−9.67
−1.52
N
2,313
2,380
30.9
33.7
31.5
0.7
0.9
0.7
38.9
52.2
40.0
1.3
1.9
1.2
−6.15
−9.83
−7.20
2,352
1,498
2,589
54.1
50.6
53.9
0.4
0.7
0.4
45.7
42.6
45.9
0.6
0.9
0.6
15.01
9.45
15.57
2,131
1,269
2,330
43.2
2.36
0.2
0.01
45.9
2.43
0.3
0.01
−10.10
−6.70
2,361
2,364
50
Table 3: Investment performance of clients and advisors
This table reports annualized percentage alphas for clients’ and advisors’ portfolios. We measure
gross returns, net returns, and net returns adjusted for fees and (advisor) rebates for value-weighted
client and advisor portfolios. Gross return adds back the management expense ratio; net return is
the return on the investment portfolio without additional fees or rebates; net return with fees and
rebates subtracts off front-end loads and deferred sales charges and, for advisors, adds back rebates
earned through sales commissions on personal purchases and trailing commissions earned on funds
held. 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 as
the market factor; the second model adds the return difference between the long-term and shortterm Canadian government bonds (the term factor); and the third model adds the return difference
between high-yield Canadian corporate debt and investment grade debt (the default factor) and
the North American size, value, and momentum factors.
Factors in the asset pricing model
Return
series
Clients
Return
type
Gross return
Net return
w/fees and rebates
MKTRF, TERM
MKTRF, SMB,
HML, UMD,
TERM, DEF
α̂
0.14
−2.23
−2.38
t(α̂)
0.15
2.40
2.56
α̂
−0.11
−2.49
−2.64
t(α̂)
−0.12
−2.64
−2.80
α̂
−0.69
−3.07
−3.22
t(α̂)
−0.78
−3.42
−3.59
Advisors
Gross return
Net return
w/fees and rebates
−0.68
−3.10
−2.92
−0.66
−2.99
−2.79
−0.88
−3.30
−3.11
−0.84
−3.13
−2.93
−1.25
−3.66
−3.49
−1.29
−3.79
−3.57
Clients
− Advisors
Gross return
Net return
w/fees and rebates
0.82
0.86
0.53
2.50
2.62
1.61
0.77
0.81
0.47
2.30
2.42
1.40
0.55
0.60
0.27
2.55
2.74
1.21
MKTRF
51
Table 4: Explaining cross-sectional variation in return chasing behavior with client attributes and
advisor 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 unit
of observation is a client-advisor pair. The first regression in Panel A includes the client attributes
and a dealer effect. The second regression adds advisor fixed effects. The age fixed effects are based
on 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 old
advisor dies, retires, or leaves the industry. The model in Panel B includes both advisor and client
fixed effects.
52
Independent
variable
Constant
Risk tolerance
Low
Low to Moderate
Moderate
Moderate to High
High
Financial knowledge
Moderate
High
Time horizon
Short
Moderate
Long
Female
French speaking
Salary
$30-50k
$50-70k
$70-100k
$100-200k
Over $200k
Net worth
$35-60k
$60-100k
$100-200k
Over $200k
Advisor FEs
Dealer FE
Age FEs
Province FEs
N
Adjusted R2
w/o Dealer FE
EST
55.12
Regression 1
t-value
48.47
EST
56.39
Regression 2
t-value
73.56
−0.26
−0.03
1.48
2.10
1.47
−0.35
−0.04
2.42
3.34
2.06
−0.21
−0.14
0.97
1.29
0.14
−0.35
−0.28
2.01
2.65
0.26
0.75
1.48
4.07
4.41
0.28
0.99
3.14
4.30
1.95
1.62
0.94
−0.06
1.19
3.87
3.35
1.84
−0.62
2.19
1.82
1.75
1.52
0.06
0.26
4.27
4.33
3.69
0.94
0.83
−0.05
0.17
0.16
−2.61
0.19
−0.46
1.02
0.87
−1.95
0.28
−0.03
0.22
0.26
−1.43
0.20
−0.37
2.21
2.18
−1.17
0.37
0.64
0.66
0.85
1.37
2.60
2.56
3.63
5.36
0.36
0.35
0.52
0.79
1.65
1.66
2.58
3.95
No
Yes
Yes
Yes
Yes
–
Yes
Yes
311,032
311,032
1.1%
1.0%
16.5%
53
Panel B: Regressions with advisor and client fixed effects
Advisor FEs
Client FEs
Yes
No
No
Yes
Yes
Yes
Test: Client FEs jointly zero
Test: Advisor FEs jointly zero
Adjusted R2
19.7%
5.1%
29.1%
F (9537, 2495) = 1.30
F (154, 1402) = 4.19
Number of observations
12,476
54
Table 5: Explaining cross-sectional variation in client behavior with client attributes, advisor fixed
effects, client fixed effects, and advisor behavior
Panel A evaluates the importance of client attributes, advisor fixed effects, and province fixed effects
in explaining cross-sectional variation in client behavior. The measures of behavior are described
in text and summarized in Table 2. The unit of observation is a client-advisor pair. The estimates
reported in Panel A are the models’ adjusted R2 s. The measures pool trades and holdings across
open and retirement accounts. Panel B estimates models with advisor and client fixed effects using
a sample of displaced clients. These are clients who are forced to switch advisors when their old
advisor dies, retires, or leaves the industry.
Panel A: Regressions with client attributes and advisor fixed effects
Client attributes
Behavior
Client attributes
+ dealer effect
Return chasing
1.0%
1.1%
Active management
0.9%
0.9%
Turnover
0.7%
0.7%
Home bias
2.7%
2.9%
Fees
Total MER
8.1%
8.3%
Percentile within asset class
3.0%
3.1%
Panel B: Two-way fixed effects models for client behavior
Client
Advisor
Behavior
FEs
FEs
Return chasing
5.1%
19.7%
Active management
8.8%
34.1%
Turnover
7.2%
13.9%
Home bias
33.3%
31.5%
Fees
Total MER
57.0%
34.3%
Percentile within asset class
30.7%
29.3%
55
Client attributes
+ advisor FEs
16.5%
18.0%
7.1%
25.9%
N
311,032
325,472
387,640
312,085
26.1%
22.4%
322,968
321,064
Both
FEs
29.1%
49.0%
21.9%
47.1%
N
12,476
13,259
22,764
12,544
67.3%
47.9%
13,161
13,076
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 and
investment behavior. These advisor fixed effects are from Table 5’s all-clients regressions of client
behavior on client attributes and advisor fixed effects.
Regressor
Return
chasing
Active
management
Fees
Turnover
Home
bias
Total
MER
Cond.
percentile
Advisor fixed effects from simple regressions
Advisor behavior
0.24
(13.67)
0.29
(4.21)
0.16
(2.71)
0.34
(18.89)
0.13
(3.73)
0.27
(15.54)
Adjusted R2
w/o Advisor attributes
22.3%
18.7%
19.2%
18.4%
7.3%
6.9%
25.6%
24.1%
9.9%
9.3%
20.1%
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
(5.48)
0.26
(1.87)
0.22
(2.78)
0.28
(6.01)
0.14
(2.22)
0.25
(4.57)
Adjusted R2
w/o Advisor attributes
6.4%
5.9%
6.7%
5.5%
2.2%
1.5%
7.9%
5.4%
2.1%
2.3%
7.3%
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 purchases the fund and whether the other party already owns the same fund or purchases it at the
same time. A purchase is a client-only purchase if the advisor does not purchase the same fund
at the same time and does not hold the fund; it is an advisor-only purchase if none the clients
purchase the same fund at the same time and do not hold the fund. A purchase is a joint purchase
if both the client and purchase the same fund at the same time, or if the other party already
owns the fund at the time of the purchase. Panel A compares the characteristics of the mutual
funds bought by regressing the percentile rank of past returns, home bias, MER, and percentile
fee on advisor-only and joint-purchase indicator variables. The omitted category is the client-only
category. The unit of observation is an advisor-purchase type pair, and the standard errors cluster
by advisor. Panel B computes value-weighted returns on portfolios that choose weights based on
which funds are bought in each category. The returns are net returns without an adjustment for
additional fees and rebates. A fund’s weight in month t in purchase category c is proportional to
the total value of purchases of this fund in category c in months t − 12 to t − 1. Panel B compares
returns advisor-only and client-only purchases and joint purchases and client-only purchases.
Panel A: Differences in the characteristics of mutual fund purchases
Dependent variable
Return
Home
Regressor
chasing
bias
MER
Intercept
59.36
52.71
2.39
[0.19]
[0.38]
[0.01]
Fees
Percentile fee
44.14
[0.19]
5.06
[0.40]
−11.23
[0.79]
0.05
[0.01]
2.77
[0.38]
Joint purchase
−0.30
[0.36]
0.96
[0.70]
0.03
[0.01]
−0.70
[0.34]
R2
N
1.6%
10,558
2.2%
10,368
0.2%
10,697
0.6%
10,675
Advisor-only purchase
Panel B: Differences in future fund returns (%) conditional on purchase type
Return difference
Advisor-only purchase
Joint purchase
− Client-only purchase
− Client-only purchase
Model
α̂
t(α̂)
α̂
t(α̂)
Gross return
Average return
0.55
0.90
0.37
0.80
CAPM
−0.07
−0.14
−0.09
−0.21
Six-factor model
−0.01
−0.02
−0.10
−0.25
Net return
Average return
CAPM
Six-factor model
0.36
−0.26
−0.19
0.59
−0.51
−0.44
57
0.41
−0.05
−0.06
0.88
−0.12
−0.16
Table 8: Correlations between advisors’ and their clients’ alphas and fees
This table reports Spearman rank correlations between advisors’ and their clients’ gross and net
alphas and fees. The gross and net alphas are estimated using three asset pricing models that are
described in Table 3. The fees consist of management expense ratios, front-end loads, and deferred
sales charges. We block bootstrap the data by calendar month 100 times to compute the standard
errors. We include advisors and clients with at least one year of returns.
MKTRF
MKTRF, TERM
MKTRF, SMB, HML, UMD, TERM, DEF
EST
0.40
0.40
0.37
Correlation
Bootstrapped SE
0.04
0.04
0.04
Net alphas
MKTRF
MKTRF, TERM
MKTRF, SMB, HML, UMD, TERM, DEF
0.40
0.40
0.37
0.04
0.04
0.03
Fees
None
0.37
0.01
Measure
Gross alphas
Factors in the
asset pricing model
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 computing
the four measures of behavior and the two fee measures both before and after they stop advising
clients.
Behavior
Return chasing
Active management
Turnover
Home bias
Fees
Percentile within asset class
Total MER
Active
advisors
EST
SE
63.4
1.2
99.5
0.2
35.0
4.0
48.6
2.4
Post-career
advisors
EST
SE
58.3
1.5
98.6
0.5
53.4
6.4
52.9
2.8
45.8
2.47
46.2
2.33
1.1
0.03
59
1.4
0.04
Difference
EST
t-value
−5.1
−2.64
−0.9
−1.99
18.4
2.74
4.2
1.47
0.4
−0.15
0.26
−2.84
N
168
195
420
171
183
184
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-weighted
aggregate portfolio. The hypothetical net alphas are computed by assuming that the advisors hold
perfect copies of their clients’ portfolios. The return on this portfolio equals the net return earned
by the clients, adjusted for the sales commissions that advisors earn on their personal purchases and
for the trailing commissions that they earn on their personal holdings. In this computation, advisors
pay the same deferred sales charges as those paid by the clients. The net alphas are computed using
the three asset pricing models described in Table 3. t-values are reported in parentheses.
Factors in the asset pricing model
Advisor
portfolio
Actual
MKTRF
α̂
−2.92
(−2.79)
MKTRF, TERM
R2
85.5%
MKTRF, SMB,
HML, UMD,
TERM, DEF
α̂
−3.11
(−2.93)
R2
85.5%
α̂
−3.49
(−3.57)
R2
88.3%
Hypothetical
−1.82
(−1.94)
84.7%
−2.07
(−2.19)
84.8%
−2.67
(−2.96)
86.9%
Hypothetical
− actual
1.10
(3.36)
51.2%
1.04
(3.13)
51.2%
0.82
(3.79)
80.5%
60
Pr(Client purchases the same fund)
0:5
0:4
Own advisor
Random advisor (same dealer)
Random advisor (di,erent dealer)
0:3
0:2
0:1
0
!12
!9
!6
!3
0
3
Month relative to the advisor purchase
6
9
12
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 purchases
made by advisors who have at most 10 clients at the time of the purchase. The black lines indicate
the actual purchase probabilities by the clients and the 95% confidence intervals associated with
these probabilities. The blue and red lines indicate purchase probabilities simulated under the null
hypothesis that advisors’ purchases are independent of those of their clients. We resample the
data 100 times with replacement and randomly match an advisor with another advisor who also
purchases a new fund in the same month. The blue line draws the random advisors from the same
dealer firm; the red line draws these advisors from the other dealer firm.
61
7
Client idiosyncratic risk, annualized %
Average client, six-factor model
Aggregate client, two-factor model
6
5
4
3
1
2
3
4
5
6
7
8
9
10
Advisor idiosyncratic risk decile
Figure A2: Client idiosyncratic risk conditional on advisor idiosyncratic risk. This figure
sorts advisors into deciles based on their idiosyncratic portfolio risk and reports the average portfolio
idiosyncratic risks of the clients of these advisors. The black line is drawn by first measuring the
idiosyncratic risk for each client and advisor as the volatility of the residual return from the sixfactor model. We then compute the average volatility over each advisor’s clients. This line plots
the idiosyncratic volatility of the average client of the average advisor in each decile. The red line
computes the value-weighted return earned by the aggregate client portfolio, and measures the
volatility of the residual return from the two-factor model with the market and term factors. This
line plots the idiosyncratic volatility of the aggregate client of the average advisor in each decile.
The bars denote the 95% confidence intervals.
62
Table A1: Estimated probabilities of advisor survival
This table reports estimates from linear probability models that examine the relationship between
advisor survival and the number of clients. The data are annual. If an advisor serves clients in
year y and continues to do so in year y + 1, the dependent variable takes the value of one. If an
advisor stops advising clients during the following year, the dependent variable takes the value of
zero. The regressors consist of indicator variables that indicate the number of clients the advisor
has in year y. Advisors with more than 100 clients are the omitted category. The regressions are
estimated with year fixed effects and standard errors are clustered by advisor.
Regressor
Intercept
Number of clients
1–5
6–10
11–15
16–20
21–30
31–40
41–50
50–100
Coefficient
0.989
SE
0.002
−0.177
−0.083
−0.051
−0.034
−0.040
−0.027
−0.019
−0.016
0.010
0.010
0.009
0.008
0.007
0.006
0.006
0.004
Adjusted R2
N
8.28%
98,974
63
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 performance
regressions reported in Table 3. The six factors consist of the the excess return on the Canadian
equity 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 investment
grade debt (DEF).
Return series
Factor
MKTRF
SMB
HML
UMD
TERM
DEF
Adjusted R2
Clients
b̂
0.540
0.074
0.018
−0.036
0.118
0.135
SE
0.019
0.026
0.021
0.013
0.038
0.037
Advisors
b̂
SE
0.597
0.021
0.111
0.028
−0.038
0.023
−0.029
0.014
0.098
0.041
0.158
0.040
Clients
− Advisors
b̂
SE
−0.057
0.005
−0.036
0.006
0.057
0.005
−0.007
0.003
0.020
0.009
−0.023
0.009
88.4%
79.3%
87.0%
64
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 return
difference into four components. We compute net returns after management expense ratios but
before 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 same
style. “Within-style gross alpha” is the difference in within-style gross returns, where the withinstyle return is computed as the difference between the actual fund return and the return earned
by the average fund of the same style. “Style fee” is the difference in style fees, where style fee is
defined 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 fund
MER and the MER of the average fund of the same style. These four components add up to the
total difference in net returns between clients and advisors shown on the bottom row The first set
of columns report time-series averages of these components for value-weighted advisor and client
portfolios. The second set of columns reports the six-factor model alphas for these components and
for the total difference. The t-values associated with the style fee and within-style fee components
are large because these differences are very stable in the time-series.
Client-minus-advisor
return component
Style gross alpha
Within-style gross alpha
Style fee
Within-style fee
Total difference
Excess returns
Estimate
t-value
0.33
1.64
0.49
2.36
0.05
42.35
−0.01
−3.71
0.86
2.62
65
Six-factor model
Estimate
t-value
0.27
1.51
0.28
2.12
0.05
41.92
−0.01
−4.03
0.60
2.74