Does Academic Research Destroy Stock Return Predictability?

• Author: R. DAVID MCLEAN,JEFFREY PONTIFF
• DOI: http://doi.org/10.1111/jofi.12365
• Published date: 01 February 2016
• Date: 01 February 2016

THE JOURNAL OF FINANCE •VOL. LXXI, NO. 1 •FEBRUARY 2016

Does Academic Research Destroy Stock Return

Predictability?

R. DAVID MCLEAN and JEFFREY PONTIFF∗

ABSTRACT

We study the out-of-sample and post-publication return predictability of 97 vari-

ables shown to predict cross-sectional stock returns. Portfolio returns are 26% lower

out-of-sample and 58% lower post-publication. The out-of-sample decline is an up-

per bound estimate of data mining effects. We estimate a 32% (58%–26%) lower

return from publication-informed trading. Post-publication declines are greater for

predictors with higher in-sample returns, and returns are higher for portfolios con-

centrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfo-

lios exhibit post-publication increases in correlations with other published-predictor

portfolios. Our findings suggest that investors learn about mispricing from academic

publications.

FINANCE RESEARCH HAS UNCOVERED many cross-sectional relations between pre-

determined variables and future stock returns. Beyond their historical insights,

these relations are relevant to the extent that they provide insights into the

future. Whether the typical relation continues outside a study’s original sample

is an open question, the answer to which can shed light on why cross-sectional

return predictability is observed in the first place.1Although several papers

∗R. David McLean is at DePaul University and Jeffrey Pontiff is at Boston College and is an

unpaid director at a non-profit called the Financial Research Association. We are grateful to the Q

Group, the Dauphine-Amundi Chair in Asset Management, and SSHRC for financial support. We

thank participants at the Financial Research Association’s 2011 early ideas session, Auburn Uni-

versity,Babson College, Bocconi University, Boston College, Brandeis University,CKGSB, Georgia

State University, HBS, HEC Montreal, MIT, Northeastern University, Simon Fraser, University

of Georgia, University of Maryland, University of South Carolina, University of Toronto, Univer-

sity of Wisconsin, Asian Bureau of Finance and Economic Research Conference, City University

of Hong Kong International Conference, Finance Down Under Conference 2012, Wilfred Lau-

rier, University of WashingtonSummer Conference, European Finance Association (Copenhagen),

1st Luxembourg Asset Management Conference, Ivey Business School, and Pontificia Universi-

dad Catholica de Chile and Pierluigi Balduzzi, Turan Bali, Brad Barber, Mark Bradshaw, David

Chapman, Shane Corwin, Alex Edmans, Lian Fen, Wayne Ferson, Francesco Franzoni, Xiaohui

Gao, Thomas Gilbert, Robin Greenwood, Bruce Grundy, Cam Harvey, Clifford Holderness, Darren

Kisgen, Owen Lamont, Borja Larrain, Juhani Linnainmaa, Jay Ritter,Ronnie Sadka, Paul Schultz,

Andrei Shleifer, Ken Singleton, Bruno Skolnik, Jeremy Stein, Noah Stoffman, Matti Suominen,

Allan Timmermann, Michela Verado, Artie Woodgate, Jianfeng Yu, William Ziemba, three anony-

mous referees, and an anonymous Associate Editor for helpful comments.

1Similar to Mittoo and Thompson’s(1990) study of the size effect, we use a broad set of predictors

to focus on out-of-sample cross-sectional predictability. For an analysis of the performance of

DOI: 10.1111/jofi.12365

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6The Journal of Finance R

note whether a specific cross-sectional relation continues out-of-sample, no

study compares in-sample returns, post-sample returns, and post-publication

returns for a large sample of predictors. Moreover, previous studies produce

contradictory messages. As examples, Jegadeesh and Titman (2001) show that

the relative returns to high momentum stocks increased after the publication of

their 1993 paper, whereas Schwert (2003) argues that, since the publication of

the value and size effects, index funds based on these variables fail to generate

alpha.2

In this paper, we synthesize information for 97 characteristics shown to pre-

dict cross-sectional stock returns in peer-reviewed finance, accounting, and

economics journals. Our goal is to better understand what happens to return

predictability outside a study’s sample period. We compare each predictor’s

returns over three distinct periods: (i) the original study’s sample period,

(ii) the period after the original sample but before publication, and (iii) the

post-publication period. Previous studies attribute cross-sectional return pre-

dictability to statistical biases, rational pricing, and mispricing. By comparing

the return predictability of the three periods, we can better differentiate be-

tween these explanations.

A. Statistical Bias

If return predictability in published studies results solely from statistical

biases, then predictability should disappear out of sample. We use the term

“statistical biases” to describe a broad array of biases inherent to research.

Fama (1991, p. 1585) addresses this issue when he notes that “With many clever

researchers on both sides of the efficiency fence, rummaging for forecasting

variables, we are sure to find instances of ‘reliable’ return predictability that

are in fact spurious.” To the extent that the results of the studies in our sample

are driven by such biases, we should observe a decline in return predictability

out-of-sample.

B. Rational Expectations Versus Mispricing

Differences between in-sample and post-publication returns can be deter-

mined by both statistical biases and the extent to which investors learn from

out-of-sample time-series predictability, see LeBaron (2000) and Goyal and Welch (2008). For an

analysis of cross-sectional predictability using international data, see Fama and French (1998),

Rouwenhorst (1998), and McLean, Pontiff, and Watanabe (2009). For an analysis of calendar

effects, see Sullivan, Timmermann, and White (2001).

2Lewellen (2014) uses 15 variables to produce a singular rolling cross-sectional return proxy

and shows that it predicts, with decay, next period’s cross section of returns. Haugen and Baker

(1996) and Chordia, Subrahmanyan, and Tong (2013) compare characteristics in two separate

subperiods. Haugen and Baker show that each of their characteristics produces statistically sig-

nificant returns in their second subperiod, whereas Chordia, Subrahmanyan, and Tong show that

none of their characteristics are statistically significant in their second subperiod. Green, Hand,

and Zhang (2013) identify 300 published and unpublished characteristics but they do not estimate

characteristic decay parameters as a function of publication or sample-end dates.