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

ArXiv ID: 2406.19105 “View on arXiv”

Authors: Unknown

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.

Keywords: M6 Competition, Ranked Probability Score, Information Ratio, Factor Models, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical methods (factor models, hypothesis testing on performance metrics) and shows rigorous empirical analysis using competition data, benchmark comparisons, and detailed risk metrics.

  flowchart TD
    A["Research Goal<br/>Assess M6 Competitors' Investment Skill<br/>Beyond Competition Metrics"] --> B["Data & Inputs<br/>M6 Competitor Predictions<br/>Industry Benchmarks<br/>Factor Model Data"]
    B --> C["Methodology<br/>1. Calculate Standard Metrics (IR, etc.)<br/>2. Apply Factor Models<br/>3. Compare to Random Portfolios<br/>4. Create 'Superstar'/'Superloser' Strategies"]
    C --> D["Computational Analysis<br/>Benchmark-adjusted returns<br/>Statistical significance testing"]
    D --> E{"Key Findings & Outcomes"}
    E --> F["Winners lacked<br/>significant alpha vs. benchmarks<br/>Skill identification is challenging"]
    E --> G["Extreme performers<br/>less benchmark-dependent"]
    E --> H["Incentive misalignment:<br/>Competition vs. Long-term investor goals"]