Portfolio Optimization Efficiency Test Considering Data Snooping Bias

Background: In the portfolio optimization area, most of the research is focused on insample portfolio optimization. One may ask a rational question of what the efficiency of the portfolio optimization strategy is and how to measure it.Objectives: The objective of the paper is to propose the approach to measuring the efficiency of the portfolio strategy based on the hypothesis inference methodology and considering a possible data snooping bias. The proposed approach is demonstrated on the Markowitz minimum variance model and the fuzzy probabilities minimum variance model.Methods/Approach: The proposed approach is based on a statistical test. The null hypothesis is that the analysed portfolio optimization strategy creates a portfolio randomly, while the alternative hypothesis is that an optimized portfolio is created in such a way that the risk of the portfolio is lowered.Results: It is found out that the analysed strategies indeed lower the risk of the portfolio during the market’s decline in the global financial crisis and in 94% of the time in the 2009-2019 period.Conclusions: The analysed strategies lower the risk of the portfolio in the out-of-sample period.

Thousands of Alpha Tests

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation.