Retail Financial Advice: Does One Size Fit All?

• Date: 01 August 2017
• Author: ALESSANDRO PREVITERO,STEPHEN FOERSTER,JUHANI T. LINNAINMAA,BRIAN T. MELZER
• DOI: http://doi.org/10.1111/jofi.12514
• Published date: 01 August 2017

THE JOURNAL OF FINANCE •VOL. LXXII, NO. 4 •AUGUST 2017

Retail Financial Advice: Does One Size Fit All?

STEPHEN FOERSTER, JUHANI T. LINNAINMAA, BRIAN T. MELZER,

and ALESSANDRO PREVITERO∗

ABSTRACT

Using unique data on Canadian households, we show that financial advisors exert sub-

stantial influence over their clients’ asset allocation, but provide limited customiza-

tion. Advisor fixed effects explain considerably more variation in portfolio risk and

home bias than a broad set of investor attributes that includes risk tolerance, age,

investment horizon, and financial sophistication. Advisor effects remain important

even when controlling flexibly for unobserved heterogeneity through investor fixed

effects. An advisor’s own asset allocation strongly predicts the allocations chosen on

clients’ behalf. This one-size-fits-all advice does not come cheap: advised portfolios

cost 2.5% per year, or 1.5% more than life cycle funds.

THE LIFE CYCLE ASSET ALLOCATION PROBLEM is complex. Choosing how to allocate

savings across risky assets requires, among other things, an understanding

of risk preferences, investment horizon, and the joint dynamics of asset re-

turns and labor income. To help solve this problem, many households turn

to investment advisors. In the United States, more than half of households

owning mutual funds make purchases through an investment professional

∗Stephen Foerster is with Western University, Juhani Linnainmaa is with the University of

Southern California and NBER, Brian Melzer is with Northwestern University, and Alessandro

Previtero is with Indiana University and NBER. A significant part of this project was completed

when Linnainmaa was with the University of Chicago and Previtero was with Western Uni-

versity. We thank Shlomo Benartzi, Antonio Bernardo, John Cochrane, Chuck Grace, Tal Gross

(discussant), Luigi Guiso, Michael Haliassos (discussant), Markku Kaustia (discussant), Roger

Loh (discussant), Jonathan Parker, Jonathan Reuter (discussant), Antoinette Schoar (discussant),

Barry Scholnick, Ken Singleton (Editor), Dick Thaler, the Associate Editor, and two anonymous

referees for valuable comments. We are also grateful for feedback given by seminar and conference

participants at Nanyang Technological University, Singapore Management University, National

University of Singapore, University of Windsor, McMaster University, Rice University, Yale Uni-

versity,University of Washington, SUNY–Buffalo, Federal Reserve Bank of Cleveland, University

of Chicago, University of Alberta, University of Southern California, University of California–

San Diego, University of Colorado–Boulder, University of California–Berkeley, McGill University,

Columbia University, Mannheim University, Goethe University, American Economic Association

2014 meetings, NBER Behavioral Economics Spring 2014 meetings, Rothschild Caesarea Center

11th Annual Conference, 2014 Helsinki Finance Summit, 2014 Napa Conference on Financial

Markets Research, Oxford-Harvard-Sloan conference “Household Behavior in Risky Asset Mar-

kets: An International Perspective,” and 2014 European Household Finance Conference. We are

especially grateful to Univeris, Fundata, Ipsos-Reid, and four anonymous financial firms for donat-

ing data and giving generously of their time. Zhou Chen, Brian Held, and Paolina Medina-Palma

provided helpful research assistance. Stephen Foerster and Alessandro Previtero acknowledge

support from five anonymous financial institutions. These institutions provided data and financial

support, subject to non-disclosure agreements to protect the confidentiality of the data.

DOI: 10.1111/jofi.12514

1441

1442 The Journal of Finance R

(Investment Company Institute (2013)). Likewise, nearly half of Canadian

households report using financial advisors (The Investment Funds Institute

of Canada (2012)), and roughly 80% of the $876 billion in retail investment

assets in Canada reside in advisor-directed accounts (Canadian Securities Ad-

ministrators (2012)).

Despite widespread use of financial advisors, relatively little is known about

how advisors shape their clients’ investment portfolios. Recent studies high-

light underperformance and return chasing by advisor-directed investments

and provide suggestive evidence that agency conflicts contribute to underper-

formance.1An opposing view is that financial advisors nevertheless add value

by building portfolios suited to each investor’s unique characteristics, an ap-

proach described as “interior decoration” by Bernstein (1992) and Campbell

and Viceira (2002).

In this paper,we use unique dataon Canadian households to explore whether

advisors tailor investment risk to clients’ characteristics or instead deliver

one-size-fits-all portfolios. The data, which were furnished by four large fi-

nancial institutions, include transaction-level records on over 10,000 financial

advisors and these advisors’ 800,000 clients, along with demographic infor-

mation on both investors and advisors. Many of the investor attributes—such

as risk tolerance, age, investment horizon, income, occupation, and financial

knowledge—should be of first-order importance in determining the appropriate

allocation to risky assets.

What determines cross-sectional variation in investors’ exposure to risk?

In neoclassical portfolio theory, differences in risk aversion account for the

variation in risky shares (see Mossin (1968), Merton (1969), and Samuelson

(1969)). In richer classes of models, many other factors also shape investors’

optimal risk exposures. For example, according to most models, old investors

and investors facing greater labor income risk should invest less in risky assets

(see, for example, Bodie, Merton, and Samuelson (1992)). The recommendations

implicit in life cycle funds also embody such advice. These funds allocate nearly

the entire portfolio to equities for young investors and then reduce this exposure

as investors near retirement.

We test whether advisors adjust portfolios in response to such factors by

studying variation in the proportion of equities in investors’ portfolios (“risky

share”). We find that advisors modify portfolios based on client characteristics,

with a particular emphasis on clients’ risk tolerance and point in the life cycle.

As one would expect, more risk-tolerant clients hold riskier portfolios: the least

risk tolerant allocate on average 40% of their portfolio to risky assets, while

1A number of studies document underperformance of advisor-directed investments: brokered

mutual funds underperform nonbrokered funds (Bergstresser, Chalmers, and Tufano (2009),

Christoffersen, Evans, and Musto (2013)) and investors who pay for advice underperform life

cycle funds (Chalmers and Reuter (2013)) and self-managed accounts (Hackethal, Haliassos, and

Jappelli (2012)). Brokers are also more likely to sell funds that earn them higher commissions

(Christoffersen, Evans, and Musto (2013)). Mullainathan, Noeth, and Schoar (2012) find in a field

experiment that advisors encourage their clients to chase past returns and to purchase actively

managed mutual funds.

Retail Financial Advice: Does One Size Fit All 1443

the most risk tolerant allocate 80%. The risky share also declines with age,

peaking at 75% before age 40 and declining by 5 to 10 percentage points as

retirement approaches. While risk-taking peaks at the same age as in a life

cycle fund, the risky share of advised clients otherwise differs substantially

from the pattern in a life cycle fund—younger clients take less risk and older

clients take substantially more risk than they would in a life cycle fund. We

find only modest differences in portfolios across occupations and mixed evi-

dence regarding the typical recommendations of portfolio theory. Controlling

for risk tolerance and other characteristics, government workers invest more

in equities. This choice fits with the typical prescription of portfolio theory for

an occupation with low-risk labor income. On the other hand, self-employed

clients and clients working in the finance industry hold modestly higher risky

shares despite labor income that is likely to be more volatile and more strongly

correlated with market returns (Heaton and Lucas (2000)).

The most striking finding from our analysis of portfolio allocations, how-

ever, is that clients’ observable characteristics jointly explain only 12% of the

cross-sectional variation in risky share. That is, although differences in risk

tolerance and age translate into significant differences in average risky shares,

a remarkable amount of variation in portfolio risk remains unexplained.

Advisor fixed effects, by contrast, have substantially more explanatory power.

On their own, advisor effects explain 22% of the variation in risky share. When

added to the model with investor characteristics, however, they more than dou-

ble the adjusted-R2from 12% to 30%, meaning that advisor fixed effects explain

one and a half times as much of the variation in risky share as that explained by

the full set of client characteristics. Similarly, advisor fixed effects are pivotal

in explaining home bias: client characteristics explain only 4% of the variation

in the share of risky assets invested in Canadian equity funds, whereas advisor

fixed effects explain an additional 24% of the variation. The advisor effects are

also economically large. Moving from the 25th to the 75th percentile in the advi-

sor distribution corresponds to a 20-percentage-point change in risky share and

a 32-percentage-point change in home bias. One interpretation of this finding

is that, instead of customizing, advisors build very similar portfolios for many

of their clients. Another interpretation is that matching between investors and

advisors leads to common variation in portfolio allocations among investors of

the same advisor, that is, advisor fixed effects capture omitted client charac-

teristics that are common across investors of the same advisor.

We use clients that switch advisors to investigate the latter hypothesis. Our

data include investor identifiers that allow us to track clients who switch

advisors. We use this feature to implement a two-way fixed effects analysis,

similar to research on managerial style (Bertrand and Schoar (2003)). We ex-

clude client-initiated switches that may coincide with a change in preferences

and focus instead on clients who are forced to switch due to their advisor’s

death, retirement, or resignation. We show that client portfolios shift away

from the allocation common to the old advisor’s clients and toward the allo-

cation held by the new advisor’s clients. For this subset of investors, we also

estimate models with both advisor and investor fixed effects. The latter control