Estimating The Anomaly Base Rate / Alexander M. Chinco, Andreas Neuhierl, Michael Weber.

Format:

Book

Author/Creator:

Chinco, Alexander M.

Contributor:

National Bureau of Economic Research.

Neuhierl, Andreas.

Weber, Michael (Professor of finance)

Series:

Working Paper Series (National Bureau of Economic Research) no. w26493.

NBER working paper series no. w26493

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2019.

Summary:

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.

Notes:

Print version record

November 2019.