Quantitative Benchmarks: An Algorithmic Evaluation of Mutual Fund Manager Skill and Decision-Making

Abstract

This study attempts to separate mutual fund returns into those based on qualitative manager decision-making and those generated by the quantitative tendencies of the fund. To determine if managers exhibit any qualitative skill, this analysis tests whether the fifty best performing mutual funds over the period from 2007 to 2016 were able to outperform quantitative benchmarks formulated based on the previous fundamental characteristics of the fund’s portfolio. Two different algorithms are used to create these benchmarks. Most funds demonstrate a substantial overlap in holdings with the benchmark. Remarkably, no fund is able to demonstrate significantly positive alpha in any model, even when accounting for greater diversification in the benchmark and potential issues with implementation. Instead, some funds underperform with significance, and the quantitative models demonstrate a notable, yet imperfect, ability to replicate the underlying mutual fund returns. Therefore, even the ex-post best mutual fund performance can be attributed to predictable exposure to certain fundamental characteristics as encapsulated in the quantitative benchmarks. These results are strongly robust, providing resilient evidence against qualitative manager skill.