Estimating the Anomaly Base Rate
62 Pages Posted: 1 Mar 2019 Last revised: 20 Nov 2019
Date Written: February 28, 2019
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: Suggested Citation