Estimating the Anomaly Baserate

46 Pages Posted: 1 Mar 2019

See all articles by Alexander Chinco

Alexander 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

Date Written: February 28, 2019

Abstract

The academic literature contains literally 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 for statistical significance. But, here’s the thing: even if a researcher is using the right tests, he will still be drawing the wrong conclusions from his analysis if he is starting out with the wrong priors—i.e., if he is starting out with incorrect beliefs about the ex ante probability of discovering a tradable anomaly prior to seeing any test results.

So, what are the right priors to start out with? What is the correct anomaly baserate?

We propose a new statistical approach to answer this question. The key insight is that, under certain conditions, there’s a one-to-one mapping between the ex ante probability of discovering a tradable anomaly and the best-fit tuning parameter in a penalized regression. When we apply our new statistical approach to the cross-section of monthly returns, we find that the anomaly baserate has fluctuated substantially since the start of our sample in May 1973. The ex ante probability of discovering a tradable anomaly was much higher in 2003 than in 1990. As a proof of concept, we construct a trading strategy that invests in previously discovered predictors and show that adjusting this strategy to account for the prevailing anomaly baserate boosts its performance.

Keywords: Return Predictability, Data Mining, Penalized Regression

JEL Classification: C12, C52, G11

Suggested Citation

Chinco, Alexander and Neuhierl, Andreas and Weber, Michael, Estimating the Anomaly Baserate (February 28, 2019). Chicago Booth Research Paper No. 19-10; Fama-Miller Working Paper. Available at SSRN: https://ssrn.com/abstract=3344499 or http://dx.doi.org/10.2139/ssrn.3344499

Alexander Chinco

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

Champaign, IL 61820
United States

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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