Noise Fit, Estimation Error and a Sharpe Information Criterion

Dirk Paulsen and Jakob S\"ohl

Papers from arXiv.org

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 measure of fit.

Date: 2016-02, Revised 2019-12
New Economics Papers: this item is included in nep-ecm
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Published in Quant. Finance 20(6) (2020) 1027-1043

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1602.06186

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