Testing Out-of-sample Portfolio Performance

Abstract

This paper studies the quality of portfolio performance tests based on out-of-sample returns. By disentangling the components of out-of-sample performance we show that observed differences are driven to a large extent by the differences in estimation risk. Our Monte Carlo study reveals that the puzzling empirical ndings of inferior performance of theoretically superior strategies mainly result from the low power of these tests. Thus our results provide an explanation why the null hypothesis of equal performance of the simple equally weighted portfolio compared to many alternatives, theoretically superior strategies cannot be rejected in many out-of-sample horse races. Our fi ndings turn out to be robust with respect to different designs and the implementation strategies of the tests. For the applied researcher we provide some guidance to cope with the problem of low power. In particular, we show by the means of a novel pretest-based portfolio strategy, how the information of performance tests can be used optimally.