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Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 30th Sept 2023, 07:49:31am CEST

Session Overview
4A: Risk factors, anomalies, or just stories?
Tuesday, 16/May/2023:
12:00pm - 1:30pm

Session Chair: Irina Zviadadze
Location: Room Z014

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Peer-reviewed theory does not help predict the cross-section of stock returns

Andrew Y. Chen1, Alejandro Lopez-Lira2, Tom Zimmermann3

1Federal Reserve Board; 2University of Florida; 3University of Cologne, Germany

Discussant: Yinan Su (Johns Hopkins University)

To examine whether theory helps predict the cross-section of returns, we combine text analysis of publications with out-of-sample tests. Based on the original texts, only 18% predictors are attributed to risk-based theory. 58% are attributed to mispricing and 24% have uncertain origins. Post-publication, risk-based predictability decays by 65%, compared to 50% for non-risk predictors. Out-of-sample, risk-based predictors fail to outperform data-mined accounting predictors that are matched on in-sample summary statistics. Published and data-mined returns rise before in-sample periods end and fall out-of-sample at similar rates. Overall, peer-reviewed research adds little information about future mean returns above naive back testing.

Anomaly or Possible Risk Factor? Simple-To-Use Tests

Benjamin Holcblat1, Abraham Lioui2, Michael Weber3

1University of Luxembourg; 2EDHEC Business School.; 3Booth School of Business, University of Chicago, CEPR, and NBER.

Discussant: Carter Davis (Indiana University)

Basic asset pricing theory predicts high expected returns are a compensation for risk. However, high expected returns might also represent an anomaly due to frictions or behavioral biases. We propose two complementary, simple-to-use tests to assess whether risk can explain differences in expected returns. We provide general-equilibrium foundations for the tests. We study tests properties, and investigate them in simulations. The tests account for any risk disliked by risk-averse individuals, including high-order moments and tail risks. The tests do not rely on the validity of a factor model or other parametric statistical models. We provide a simple model, in which a factor is not due to risk, but to a friction. Empirically, we find risk cannot explain a large majority of variables predicting differences in expected returns. In particular, value, momentum, operating profitability, and investment appear to be anomalies.

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