The Journal JOE has achieved a 2025 Impact Factor of 4.4, and Top Tier journal in Econometrics. Recently, the School of Statistics and Data Science of our university, together with the School of Mathematical Sciences, Tianjin Polytechnic University, and the School of Statistics and Data Science, Nankai University, has made research progress in statistical methods for false discovery rate control in mutual fund selection. Relevant research findings published in this prestigious journal.
Robust mutual fund selection with false discovery rate control
Hongfei Wang, Ping Zhao, Long Feng, Zhaojun Wang
Abstract: In this article, we address the challenge of identifying well-performing mutual funds among a large pool of candidates, utilizing the linear factor pricing model. Assuming observable factors with a weak correlation structure for the idiosyncratic error, we propose a spatial-sign based multiple testing procedure (SS-BH). When latent factors are present, we first extract them using the elliptical principle component method (He et al. 2022) and then propose a factor-adjusted spatial-sign based multiple testing procedure (FSS-BH). Simulation studies demonstrate that our proposed FSS-BH procedure performs exceptionally well across various applications and exhibits robustness to variations in the covariance structure and the distribution of the error term. Additionally, a real data application further highlights the superiority of the FSS-BH procedure.
Link: Robust mutual fund selection with false discovery rate control - ScienceDirect
