How to Use the Sharpe Ratio
ADIA Lab Research Paper Series
How to Use the Sharpe Ratio by Marcos López de Prado, Alexander Lipton, and Vincent Zoonekynd offers a detailed guide to correctly interpreting one of finance’s most common performance measures. The Sharpe ratio is widely used to judge how efficiently an investment delivers returns relative to its risk, but most analyses of it are statistically flawed. The authors identify five major problems: assuming returns follow a Normal distribution, ignoring statistical significance and required sample size, failing to consider the power of the test, confusing p-values with the probability that an investment is truly effective, and neglecting to adjust for multiple testing. These issues mean that many reported Sharpe ratios give investors a false sense of confidence and lead to poor decisions.
To address these problems, How to Use the Sharpe Ratio reviews and extends several statistical tools designed to make Sharpe ratio inference more accurate. These include the Probabilistic Sharpe Ratio, which expresses an observed ratio as a probability adjusted for skewed or non-Normal data; the Minimum Track Record Length, which determines how much data is needed before a result can be trusted; and the Deflated Sharpe Ratio, which corrects for the inflation that occurs when multiple strategies are tested and the best one is chosen. The authors also introduce Bayesian false discovery rate methods and a new hybrid framework that combines academic and industrial approaches to control for false positives in both single and multiple tests.
Through Monte Carlo experiments, the authors show that these corrected methods outperform traditional t-tests and common multiple-testing corrections. They argue that the Sharpe ratio remains a valuable and practical tool for assessing investment performance, but only when properly adjusted for non-Normality, small sample bias, and selection effects. Without these corrections, the metric can mislead researchers and practitioners alike, turning a measure of efficiency into a source of systematic error.
