Does Academic Research Destroy Stock Return Predictability?

McLean, R. David; Pontiff, Jeffrey

doi:10.1111/jofi.12365

Cited by 1,223 publications

(277 citation statements)

References 68 publications

(89 reference statements)

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“…The effectiveness of the journal review process finds support in recent empirical studies that suggest that stock market anomalies are real (McLean and Pontiff (2016), Jacobs and Müller (2016), Yan and Zheng (2017)). …”

Section: Introductionmentioning

confidence: 89%

Publication Bias and the Cross-Section of Stock Returns

Chen

Zimmermann

2018

FEDS

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We develop an estimator for publication bias and apply it to 156 hedge portfolios based on published cross-sectional return predictors. Publication bias adjusted returns are only 12% smaller than in-sample returns. The small bias comes from the dispersion of returns across predictors, which is too large to be accounted for by data-mined noise. Among predictors that can survive journal review, a low t-stat hurdle of 1.8 controls for multiple testing using statistics recommended by Harvey, Liu, and Zhu (2015). The estimated bias is too small to account for the deterioration in returns after publication, suggesting an important role for mispricing. * We thank Rebecca Wasyk for excellent scientific programing and Chris Hollrah for excellent research assistance. This paper originated from a conversation with Frank Schorfheide. We also thank

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Section: Introductionmentioning

confidence: 89%

Publication Bias and the Cross-Section of Stock Returns

Chen

Zimmermann

2018

FEDS

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“…Some of these anomalies disappear after publication (McLean and Pontiff, 2015), but a large number of the predictive variables remain significant, with persistent predictive power. Moreover, Stambaugh, Yu, and Yuan (2012) show that profitability of the strategies 11 is mainly driven by overpriced stocks in the short leg.…”

Section: Calendar Time Portfolio Approachmentioning

confidence: 99%

Dissecting Short-Sale Performance: Evidence from Large Position Disclosures

Jank

Smajlbegovic

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractShort sellers are perceived as informed, sophisticated investors. Yet little is known about their actual performance and trading strategies. Using a novel, hand-collected data set of daily position disclosures in Europe, we identify the entry, change, and exit dates of large short-sale positions for a wide cross section of stocks and investors. We find that hedge funds, the predominant investor group, generate an annualized Fama and French (1993) risk-adjusted return of about 5.5%, outperforming other investors. Evidence indicates that hedge funds act as arbitrageurs, generating their returns by trading on the mispricing-related factors, e.g. momentum, betting-against-beta, and quality-minus-junk. In the cross section of hedge funds, local, diversified, and active funds outperform their counterparts. On the position level, we document a first-mover advantage. The profitability of short sales also varies significantly with investors' holding period, location, and industry experience.

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“…McLean and Pontiff (2013) bring the tally up to 82 variables, and Green et al (2013) to 333 variables. Further overviews are provided by Ilmanen (2011) and Harvey et al (2013).…”

Section: Loss Function For Portfolio Selectionmentioning

confidence: 99%

Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks

Ledoit

Wolf

2017

The Review of Financial Studies

234

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Markowitz (1952) portfolio selection requires estimates of (i) the vector of expected returns and (ii) the covariance matrix of returns. Many proposals to address the first question exist already. This paper addresses the second question. We promote a new nonlinear shrinkage estimator of the covariance matrix that is more flexible than previous linear shrinkage estimators and has 'just the right number' of free parameters (that is, the Goldilocks principle). In a stylized setting, the nonlinear shrinkage estimator is asymptotically optimal for portfolio selection. In addition to theoretical analysis, we establish superior real-life performance of our new estimator using backtest exercises. Terms of use: Documents in EconStor may

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Does Academic Research Destroy Stock Return Predictability?

Cited by 1,223 publications

References 68 publications

Publication Bias and the Cross-Section of Stock Returns

Publication Bias and the Cross-Section of Stock Returns

Dissecting Short-Sale Performance: Evidence from Large Position Disclosures

Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks

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