![Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5943/TOC-Subject-Sample-2021-Mathematics-Statistics.jpg"})
Abstract
We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the statistical tool of symmetrized data aggregation, which makes it robust to the strength of potential factors and distribution type of the error terms. We then establish the asymptotic validity of the proposed procedure in terms of both the false discovery rate and true discovery proportion under some mild regularity conditions. Furthermore, we demonstrate the advantages of the proposed procedure over some existing methods through extensive Monte Carlo experiments. In an empirical application, we illustrate the practical utility of the proposed procedure in the context of selecting skilled funds, which clearly has much more satisfactory performance than its main competitors.
Supplementary Materials
Contain the extension to the two-sided test and the technical proofs.
Acknowledgments
Long Feng was partially supported by National Natural Science Foundation of China under grant no. 12271271, the Fundamental Research Funds for the Central Universities under grant no. ZB22000105. Binghui Liu was partially supported by National Natural Science Foundation of China under grant no. 12171079 and the National Key R&D Program of China under grant no. 2020YFA0714102. Yanyuan Ma was partially supported by grants from National Sciences Foundation and National Institute of Health.