Benchmarking M6 Competitors: An Analysis of Financial Metrics and Discussion of Incentives

TL;DR

This paper evaluates the performance of M6 competition competitors using industry metrics and factor models, revealing challenges in identifying skill and discussing incentives for long-term investment success.

Contribution

It introduces new strategies based on recent performance and compares competitors to various benchmarks, highlighting limitations in skill detection and incentive structures.

Findings

Competitors with extreme performance are less benchmark-dependent.

Most competitors do not outperform random long-only or long-short portfolios.

Identifying skill among managers remains challenging.

Abstract

The M6 Competition assessed the performance of competitors using a ranked probability score and an information ratio (IR). While these metrics do well at picking the winners in the competition, crucial questions remain for investors with longer-term incentives. To address these questions, we compare the competitors' performance to a number of conventional (long-only) and alternative indices using standard industry metrics. We apply factor models to measure the competitors' value-adds above industry-standard benchmarks and find that competitors with more extreme performance are less dependent on the benchmarks. We also uncover that most competitors could not generate significant out-performance compared to randomly selected long-only and long-short portfolios but did generate out-performance compared to short-only portfolios. We further introduce two new strategies by picking the competitors with the best (Superstars) and worst (Superlosers) recent performance and show that it is challenging to identify skill amongst investment managers. We also discuss the incentives of winning the competition compared to professional investors, where investors wish to maximize fees over an extended period of time.