Estimating the Anomaly Base Rate

62 Pages Posted: 21 Nov 2019

See all articles by Alex Chinco

Alex Chinco

University of Illinois at Urbana-Champaign - College of Business

Andreas Neuhierl

University of Notre Dame - Department of Finance

Michael Weber

University of Chicago - Finance

Multiple version iconThere are 3 versions of this paper

Date Written: November 17, 2019

Abstract

The academic literature literally contains hundreds of variables that seem to predict the cross-section of expected returns. This so-called ‘anomaly zoo’ has caused many to question whether researchers are using the right tests of statistical significance. But, here’s the thing: even if researchers use the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors — i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly.

So, what are the right priors? What is the correct anomaly base rate?

We develop a first way to estimate the anomaly base rate by combining two key insights: #1) Empirical-Bayes methods capture the implicit process by which researchers form priors based on their past experience with other variables in the anomaly zoo. #2) Under certain conditions, there is a one-to-one mapping between these prior beliefs and the best-fit tuning parameter in a penalized regression. We study trading-strategy performance to verify our estimation results. If you trade on two variables with similar one-month-ahead return forecasts in different anomaly-base-rate regimes (low vs. high), the variable in the low base-rate regime consistently underperforms the otherwise identical variable in the high base-rate regime.

Keywords: Return Predictability, Data Mining, Empirical Bayes, Penalized Regressions

JEL Classification: C12, C52, G11

Suggested Citation

Chinco, Alexander and Neuhierl, Andreas and Weber, Michael, Estimating the Anomaly Base Rate (November 17, 2019). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2019-135. Available at SSRN: https://ssrn.com/abstract=3490076 or http://dx.doi.org/10.2139/ssrn.3490076

Alexander Chinco

University of Illinois at Urbana-Champaign - College of Business ( email )

Champaign, IL 61820
United States

HOME PAGE: http://www.alexchinco.com

Andreas Neuhierl

University of Notre Dame - Department of Finance ( email )

P.O. Box 399
Notre Dame, IN 46556-0399
United States

Michael Weber (Contact Author)

University of Chicago - Finance ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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