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date: 17 November 2019

Abstract and Keywords

This article addresses the problem of fund selection from a statistical point of view. The analysis is based solely on the track records of individual managers. The statistical problems that need to be addressed in order to implement the solution effectively include the non-normality of hedge fund returns, the time series nature of hedge fund returns, and the choice of the individual performance measures. One other issue that needs to be considered is accounting for the dependency across managers in order to improve the power of the statistical method, that is, its ability to detect skilled managers. It would be possible to rank the fund managers simply according to their non-studentized test statistics that is, according to the “raw” alpha estimates. Ranking by test statistics does not account for the varying risks taken on by the various fund managers. Once the test statistics have been obtained, it is the task of the multiple testing methods to compute a cutoff value, denoted by d, from the joint track records of all managers in the investment universe and then the task is to declare those managers as skilled for which test statistics is greater than the cutoff value. This has to be done in a way such that the familywise error rate (FEW) is controlled.

Keywords: test statistics, fund selection, time series, statistical method, non-studentized test statistics, hedge fund returns, familywise error rate

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