Abstract
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 language | English |
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Pages (from-to) | 1027-1043 |
Number of pages | 17 |
Journal | Quantitative Finance |
Volume | 20 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- AIC
- Akaike information criterion
- Backtesting
- Estimation error
- Model selection
- Noise fit
- Overfit
- Sharpe ratio
- Sharpe ratio information criterion
- SRIC