Investing in Disappearing Anomalies

Review of Finance, Forthcoming

35 Pages Posted: 15 May 2003 Last revised: 16 Aug 2015

See all articles by Christopher S. Jones

Christopher S. Jones

University of Southern California - Marshall School of Business - Finance and Business Economics Department

Lukasz Pomorski

AQR Capital Management, LLC

Date Written: December 1, 2013

Abstract

We argue that anomalies may experience prolonged decay after discovery and propose a Bayesian framework to study how that impacts portfolio decisions. Using the January effect and short-term index autocorrelations as examples of disappearing anomalies, we find that prolonged decay is empirically important, particularly for small- cap anomalies. Papers that document new anomalies without accounting for such decay may actually underestimate the original strength of the anomaly and imply an overstated level of the anomaly out of sample. We show that allowing for potential decay in the context of portfolio choice leads to out-of-sample outperformance relative to other approaches.

Keywords: anomalies, Bayesian analysis, out-of-sample return predictability, asset allocation, structural breaks, January effect, return autocorrelation, value effect

JEL Classification: G12, G11

Suggested Citation

Jones, Christopher S. and Pomorski, Lukasz, Investing in Disappearing Anomalies (December 1, 2013). Review of Finance, Forthcoming. Available at SSRN: https://ssrn.com/abstract=357860 or http://dx.doi.org/10.2139/ssrn.357860

Christopher S. Jones (Contact Author)

University of Southern California - Marshall School of Business - Finance and Business Economics Department ( email )

Marshall School of Business
Los Angeles, CA 90089
United States

Lukasz Pomorski

AQR Capital Management, LLC ( email )

Greenwich, CT
United States

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