Why and how systematic strategies decay

TL;DR

This paper identifies ex-ante predictors of out-of-sample performance decay in stock anomalies, highlighting publication year and overfitting measures as key factors influencing Sharpe ratio decline over time.

Contribution

It introduces a set of predictive characteristics based on hypotheses of decay, improving understanding of factors influencing out-of-sample performance drops.

Findings

Publication year explains 30% of Sharpe decay variance.

Overfitting measures account for an additional 15% of variance.

Arbitrage-related variables have marginal predictive power.

Abstract

In this paper, we propose ex-ante characteristics that predict the drop in risk-adjusted performance out-of-sample for a large set of stock anomalies published in finance and accounting academic journals. Our set of predictors is generated by hypotheses of OOS decay put forward by McLean and Pontiff (2016): arbitrage capital flowing into newly published strategies and in-sample overfitting linked to multiple hypothesis testing. The year of publication alone - compatible with both hypotheses - explains 30% of the variance of Sharpe decay across factors: Every year, the Sharpe decay of newly-published factors increases by 5ppt. The other important variables are directly related to overfitting: the number of operations required to calculate the signal and two measures of sensitivity of in-sample Sharpe to outliers together add another 15% of explanatory power. Some arbitrage-related variables are statistically significant, but their predictive power is marginal.