Sharpe Ratio Inference: A New Standard for Decision Making and Reporting
诊断了夏普比率推断中的五个常见错误，推导了非正态、序列相关收益下夏普比率估计量的抽样分布闭式近似，提出了更可靠的推断框架，对基金经理、策略选择者和资产配置者具有实用价值。
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 article 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: 1) reporting point estimates without statistical significance; 2) biased inference caused by wrongly assuming independent and identically distributed Normal returns; 3) ignoring test power and minimum sample length requirements; 4) misinterpreting <italic>p</italic>-values as probabilities that the null is true; and 5) 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 <italic>t</italic>-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.