We apply the procedure of Lee et al. to the problem of performing inference on the signal-noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivariate analogue of the commonly used approximate standard error of the Sharpe ratio to use in this conditional estimation procedure. We also consider several alternative procedures, including the simple Bonferroni correction for multiple hypothesis testing, which we fix for the case of positive common correlation among assets, the chi-bar square test against one-sided alternatives, Follman's test, and Hansen's asymptotic adjustments. Testing indicates the conditional inference procedure achieves nominal type I rate, and does not appear to suffer from non-normality of returns. The conditional estimation test has low power under the alternative where there is little spread in the signal-noise ratios of the assets, and high power under the alternative where a single asset has high signal-noise ratio. Unlike the alternative procedures, it appears to enjoy rejection probabilities monotonic in the signal-noise ratio of the selected asset, and actually maintains near-nominal rejection rates under the conditional null.

翻译：本文将Lee等人的流程应用于对在可能相关的资产集合中显示最大样本夏普比率的资产的信噪比进行推断。我们在该条件估计流程中，找到了夏普比率常用近似标准误的多元类似量。我们还考虑了若干替代流程，包括用于多重假设检验的简单Bonferroni校正（我们针对资产间正共同相关的情况进行了修正）、针对单侧备择假设的卡方棒检验、Follman检验以及Hansen渐近调整。检验表明，条件推断流程实现了名义第一类错误率，且未表现出受收益率非正态性的影响。在资产信噪比差异较小的备择假设下，条件估计检验的检验功效较低；而在单一资产具有高信噪比的备择假设下，检验功效较高。与替代流程不同，该流程的拒绝概率似乎随所选资产信噪比的增加而单调递增，并且在条件原假设下实际维持了接近名义水平的拒绝率。