Taming the Factor Zoo

Feng, Guanhao; Giglio, Stefano; Xiu, Dacheng

doi:10.2139/ssrn.2934020

Cited by 53 publications

(48 citation statements)

References 25 publications

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“…The asset pricing literature has uncovered hundreds of patterns in the crosssection of stock returns. Recent research has aimed to place some order on this zoo of anomalies (Cochrane (2011), Harvey, Liu, and Zhu (2015), Kozak, Nagel, and Santosh (2017), Feng, Giglio, and Xiu (2017)).…”

Section: Implications For the Anomaly Zoomentioning

confidence: 99%

Publication Bias and the Cross-Section of Stock Returns

Chen

Zimmermann

2019

The Review of Asset Pricing Studies

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We develop an estimator for publication bias-adjusted returns and apply it to 156 published long-short portfolios. Our adjustment uses only in-sample data and provides sharper inferences than out-of-sample tests. Bias-adjusted returns are only 12.3% smaller than in-sample returns with a standard error of 1.7 percentage points. The small bias comes from the dispersion of returns across predictors, which is too large to be explained by data-mined noise. The bias is much smaller than post-publication decay (p-value ¡.0001), suggesting mispricing is important. Our results offer a different perspective about recent papers that find most published predictors are likely false. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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Section: Implications For the Anomaly Zoomentioning

confidence: 99%

Publication Bias and the Cross-Section of Stock Returns

Chen

Zimmermann

2019

The Review of Asset Pricing Studies

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“…Recently, Feng, Giglio, and Xiu (2017), Freyberger, Neuhierl, and Weber (2017), and Kozak, Nagel, and Santosh (2017) apply LASSO-type methods to various finance problems. In this paper, we follow the methods used by Rapach, Strauss, Tu, and Zhou (2018) and apply them to the 94 characteristics in the cross-section of stock returns to identify the relevant predictors each month.…”

Section: Introductionmentioning

confidence: 99%

How Many Firm Characteristics Drive US Stock Returns?

Han

Rapach

et al. 2018

SSRN Journal

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“…They propose new characteristics and innovative methodologies, and sometimes confirm or contradict previous findings. The literature on these socalled anomalies is thus rather vast and has its own meta-studies (see, e.g., Subrahmanyam, 2010;Green et al, 2013;Harvey et al, 2016;McLean and Pontiff, 2016;Feng et al, 2017). The major industrial impact of this blossoming academic field is the rapid growth of smart-beta indices.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Systematic Equity Allocation: A Model Comparison

Guida¹,

Coqueret²

2018

Wilmott

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Recent criticisms suggest that Machine Learning-based approaches only suit predicting very short-term price movements. Tony Guida and Guillaume Coqueret apply well-known ML algorithms to systematic equity investment, presenting a methodology which shows a critical stage of feature and label engineering, a step that helps uncover hidden structures in the equity market space. Only then, the authors argue, can a modern quantitative approach make accurate long-term predictions. Machine Learning 26wilmott magazine

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Taming the Factor Zoo

Cited by 53 publications

References 25 publications

Publication Bias and the Cross-Section of Stock Returns

Publication Bias and the Cross-Section of Stock Returns

How Many Firm Characteristics Drive US Stock Returns?

Machine Learning in Systematic Equity Allocation: A Model Comparison

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