Inference-Based Performance Evaluation Using the Sortino Ratio

ADIA Lab Research Paper Series

Authors
Olivier Ledoit, University of Zurich - Department of Economics
Michael Wolf, University of Zurich - Department of Economics; ADIA Lab

Date published: September 18, 2026

The Sharpe ratio remains the most widely used measure of risk-adjusted performance, but in many practical settings the Sortino ratio is more appropriate because it focuses exclusively on downside risk-the type of risk investors care about most. For industry practitioners and applied researchers who rely on the Sortino ratio, we develop statistical methods for three fundamental inference problems: inference for the difference between two Sortino ratios; inference for a single Sortino ratio, and multiple testing based on Sortino ratios. Although sample estimates often suggest meaningful differences in performance, valid conclusions about population quantities require appropriate statistical inference. Our proposed methods accommodate key empirical features of financial returns, including non-normality, skewness, heavy tails, and serial dependence. We illustrate their practical relevance through several empirical applications based on real financial data.