How to Use the Sharpe Ratio

Abstract

The Sharpe ratio is the dominant metric for evaluating investment skill, yet inference based on it is routinely flawed—often leading to false confidence, incorrect conclusions, and costly decisions. This paper proposes a new standard for Sharpe ratio inference and reporting by diagnosing common sources of error and providing practical corrections grounded in modern statistical theory. We identify five recurring pitfalls: (i) reporting point estimates without statistical significance; (ii) biased inference caused by wrongly assuming independent and identically distributed Normal returns; (iii) ignoring test power and minimum sample length requirements; (iv) misinterpreting p-values as probabilities that the null is true; and (v) failing to correct for multiple testing and selection effects. To address these issues, we solve a long-standing open problem in financial econometrics: the derivation of a closed-form approximation to the sampling distribution of the Sharpe ratio estimator when returns are jointly non-Normal and serially correlated. Monte Carlo experiments confirm that the proposed framework yields more reliable inference than classical t-statistics and standard multiple-testing adjustments. The key message is straightforward: the Sharpe ratio remains useful for manager ranking, strategy selection, portfolio construction, and asset allocation, but only when paired with a comprehensive inference framework and disciplined reporting—otherwise it becomes a powerful generator of false discoveries. Results can be replicated using the code available at https://github.com/zoonek/2025-sharpe-ratio.

Keywords: Sharpe Ratio, Statistical Inference, Non-Normality, Power, P-Value, Bayesian FDR, FWER

JEL Classification: G0, G1, G2, G15, G24, E44