When the in-sample Sharpe ratio is obtained by optimizing over a k-dimensional parameter space, it is a biased estimator for what can be expected on unseen data (out-of-sample). We derive (1) an unbiased estimator adjusting for both sources of bias: noise fit and estimation error. We then show (2) how to use the adjusted Sharpe ratio as model selection criterion analogously to the Akaike Information Criterion (AIC). Selecting a model with the highest adjusted Sharpe ratio selects the model with the highest estimated out-of-sample Sharpe ratio in the same way as selection by AIC does for the log-likelihood as a measure of fit.

Original languageEnglish
Pages (from-to)1027-1043
Number of pages17
JournalQuantitative Finance
Issue number6
Publication statusPublished - 2020

    Research areas

  • AIC, Akaike information criterion, Backtesting, Estimation error, Model selection, Noise fit, Overfit, Sharpe ratio, Sharpe ratio information criterion, SRIC

ID: 71197519