Detection of false investment strategies using unsupervised learning methods
提出一种无监督学习算法，用于估计投资策略发现中有效不相关试验的数量，从而计算族系错误率并过滤虚假策略。
In this paper we address the problem of selection bias under multiple testing in the context of investment strategies. We introduce an unsupervised learning algorithm that determines the number of effectively uncorrelated trials carried out in the context of a discovery. This estimate is critical for computing the familywise false positive probability, and for filtering out false investment strategies.