Does Herding Behavior Reveal Skill? An Analysis of Mutual Fund Performance

• DOI: http://doi.org/10.1111/jofi.12699
• Date: 01 October 2018
• Author: HAO JIANG,MICHELA VERARDO
• Published date: 01 October 2018

THE JOURNAL OF FINANCE •VOL. LXXIII, NO. 5 •OCTOBER 2018

Does Herding Behavior Reveal Skill? An Analysis

of Mutual Fund Performance

HAO JIANG and MICHELA VERARDO∗

ABSTRACT

Weuncover a negative relation between herding behavior and skill in the mutual fund

industry. Our new, dynamic measure of fund-level herding captures the tendency of

fund managers to follow the trades of the institutional crowd. We find that herd-

ing funds underperform their antiherding peers by over 2% per year. Differences in

skill drive this performance gap: Antiherding funds make superior investment deci-

sions even on stocks not heavily traded by institutions, and can anticipate the trades

of the crowd; furthermore, the herding-antiherding performance gap is persistent,

wider when skill is more valuable, and larger among managers with stronger career

concerns.

THEORIES OF HERDING BEHAVIOR PREDICT THAT people tend to “follow the crowd”

for a variety of reasons, for instance, to appear as talented as others or to

learn from others.1One important yet underexplored feature of these models

is the idea that less skilled individuals may herd on the decisions of their

predecessors, while those with superior ability may be more likely to deviate

from past actions—to the point of exhibiting antiherding behavior. Despite the

rich implications of this intuition, however, there is little empirical evidence on

the relationship between skill and the tendency to follow the crowd.

In this paper, we investigate the link between herding behavior and skill

in the context of the mutual fund industry, which is an ideal setting to study

the relation between herding and skill for two reasons. First, ample evidence

shows that mutual funds and other institutional investors tend to herd in

∗Hao Jiang is at Michigan State University. Michela Verardo is at the London School of Eco-

nomics. We thank two anonymous referees, an anonymous Associate Editor, and the Editor, Ken-

neth Singleton, for many insightful comments and suggestions. We are grateful for comments from

Amil Dasgupta; Dong Lou; Christopher Polk; Lukasz Pomorski; Ren´

e Stulz; Sheridan Titman;

Dimitri Vayanos; Paul Woolley; and Kathy Yuan as well as from conference participants at the

2011 IDC Summer Conference, 2012 WFA, and 2015 CFAUK Society; and seminar participants at

the Universities of Aarhus, Amsterdam, Arizona, BI Oslo, Cattolica Milan, Cologne, Drexel, Eras-

mus Rotterdam, ESSEC, IESE Barcelona, Kent, London School of Economics, Lugano, Mannheim,

Michigan State, Nottingham, Reading, and Vienna. Verardo gratefully acknowledges financial

support from the Paul Woolley Centre at the London School of Economics. The authors have no

conflicts of interest to disclose.

1See, for example, Scharfstein and Stein (1990) for a model of reputational herding and

Bikhchandani, Hirshleifer, and Welch (1992) and Banerjee (1992) for models of informational

cascades. The relevant theoretical literature on herding behavior is reviewed in Section IV.

DOI: 10.1111/jofi.12699

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2230 The Journal of Finance R

their buying and selling decisions.2Second, an extensive empirical literature

on mutual fund performance analyzes the returns and investment decisions of

mutual fund managers in an attempt to measure unobservable skill.3

To address the question of whether investors can identify skilled and un-

skilled mutual fund managers by observing their tendency to herd, we create

a dynamic measure of fund-level herding that captures the tendency of a fund

manager to imitate the trading decisions of the institutional crowd. We then

test whether differences in herding behavior across funds predict mutual fund

performance and whether skill drives the link between herding and future

performance.

In line with the theoretical literature, our measure of fund herding is based

on the intertemporal correlation between the trades of a given fund and the

collective trading decisions that institutional investors have made in the past.4

Each quarter we estimate the relation between a fund’s trades and past insti-

tutional trades. We then average this relation over previous periods in the life

of the fund to obtain a measure of herding tendency. We control for a stock’s

market capitalization, book-to-market ratio, and past returns to account for po-

tential correlated trading induced by common investing styles. After filtering

out these common information components, our measure of herding captures a

fund’s tendency to imitate the past trading decisions of the crowd.

Our estimates of fund herding reveal a large degree of heterogeneity in

herding behavior, with some funds exhibiting a tendency to follow the crowd

while others show a propensity to trade in the opposite direction. These dif-

ferences in fund herding have strong predictive power for the cross section of

mutual fund returns. The top-decile portfolio of herding funds underperforms

the bottom-decile portfolio of antiherding funds by 2.28% on an annualized

basis, both before and after expenses. We obtain similar results when we ac-

count for exposures to factors such as the market risk premium, size, value,

momentum, and liquidity: The alphas from different multifactor models vary

between 1.68% and 2.52% on an annualized basis. Accounting for time-varying

factor exposures yields a predicted performance gap of 2.04% per year. In mul-

tivariate predictive regressions, fund herding can predict four-factor alphas

2Papers that document herding behavior among money managers and relate it to stock returns

include Lakonishok, Shleifer,and Vishny (1992), Grinblatt, Titman, and Wermers (1995), Nofsinger

and Sias (1999), Wermers (1999), Sias (2004), and Dasgupta, Prat, and Verardo(2011a).

3Fama and French (2010) find evidence of inferior and superior performance in the extreme

tails of the cross section of mutual fund alphas. Studies that develop measures of skill that identify

extreme performers include Kacperczyk, Sialm, and Zheng (2005,2008), Cohen, Coval, and P´

astor

(2005), Kacperczyk and Seru (2007), Cremers and Petajisto (2009), Cohen, Polk, and Silli (2010),

and P´

astor, Stambaugh, and Taylor(2017), among others.

4Models of herding behavior are inherently dynamic and involve an agent making a decision

after observing the actions of other agents (e.g., Scharfstein and Stein (1990), Bikhchandani,

Hirshleifer, and Welch (1992)). In empirical studies, institutional herding is typically measured

either as the aggregate propensity of institutional investors to buy a given stock at the same

time, or as the correlation of aggregate institutional demand over adjacent quarters. With these

measures, it is difficult to capture both the nature and the implications of sequential decision

making for individual funds.

Does Herding Behavior Reveal Skill? 2231

after controlling for fund size, age, turnover, expense ratios, net flows, and past

performance. Furthermore, fund herding remains a strong predictor of mutual

fund performance when we control for determinants of herding behavior that

have been shown to predict mutual fund performance.5Taken together, our

results strongly support the view that herding behavior captures unobservable

skill.

How do differences in skill lead to differences in herding behavior? Theoret-

ical models of sequential decision making suggest that differences in ability

or information quality can drive differences in herding tendencies. For exam-

ple, reputational herding models predict that, while managers tend to follow

their predecessors to enhance the market’s perception of their ability, managers

with superior ability might choose to antiherd, “going against market trends”

(Avery and Chevalier (1999)). Models of sequential information acquisition

predict that earlier informed investors anticipate the actions of later informed

investors and hence can profit by reversing their positions, thus exhibiting an-

tiherding behavior (Hirshleifer, Subrahmanyam, and Titman (1994)). Models

of informational cascades predict that, while agents tend to disregard their

information signals to follow the crowd, higher precision individuals are more

likely to use their information (Bikhchandani, Hirshleifer, and Welch (1992)).6

We conduct a number of tests to deepen our understanding of the link be-

tween heterogeneity in herding behavior and skill. First, we test whether an-

tiherding funds consistently make better investment decisions than herding

funds, irrespective of the decisions of the institutional crowd. Specifically, we

analyze the performance of mutual funds’ investment choices for the subset of

stocks that are not heavily traded by institutions. The results show that stocks

that constitute large bets by antiherding funds outperform stocks held mostly

by herding funds: The difference in returns is large and significant, with an

average Carhart alpha of 38 bps per month. Antiherding funds therefore make

better investment decisions than their herding peers, even on stocks that are

not subject to potential price pressure caused by institutional herds.

Second, we examine time-series variation in the performance gap between

herding and antiherding funds. If differences in skill drive differences in herd-

ing behavior, we should observe a widening of the performance gap in times

of greater investment opportunities in the mutual fund industry, which skilled

funds would be better able to exploit. Using stock return dispersion, average

idiosyncratic volatility, and investor sentiment to capture time-varying invest-

ment opportunities, we find that the performance gap between herding and

antiherding funds is indeed significantly larger during and after periods in

which opportunities for active managers are more valuable.

5We consider active share (Cremers and Petajisto (2009)), reliance on public information

(Kacperczyk and Seru (2007)), and similarity to funds with good past performance (Cohen,

Coval, and P´

astor (2005)).

6More recently, Eyster and Rabin (2014) show that rational agents who observe the actions of

multiple predecessors become aware of the information redundancy conveyed by past herds and

form beliefs of the opposite sign, exhibiting anti-imitation behavior.