Randomized Control in Performance Analysis and Empirical Asset Pricing

ArXiv ID: 2403.00009 “View on arXiv”

Authors: Unknown

Abstract

The present article explores the application of randomized control techniques in empirical asset pricing and performance evaluation. It introduces geometric random walks, a class of Markov chain Monte Carlo methods, to construct flexible control groups in the form of random portfolios adhering to investor constraints. The sampling-based methods enable an exploration of the relationship between academically studied factor premia and performance in a practical setting. In an empirical application, the study assesses the potential to capture premias associated with size, value, quality, and momentum within a strongly constrained setup, exemplified by the investor guidelines of the MSCI Diversified Multifactor index. Additionally, the article highlights issues with the more traditional use case of random portfolios for drawing inferences in performance evaluation, showcasing challenges related to the intricacies of high-dimensional geometry.

Keywords: Randomized Control Trials, Random Portfolios, Factor Premia, Monte Carlo Methods, Performance Evaluation, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like geometric random walks and high-dimensional convex geometry (MCMC methods), while its empirical application is explicitly grounded in real-world data (MSCI index constraints) and computational implementation for portfolio analysis.

  flowchart TD
    A["Research Goal:<br/>Explore RCT methods for factor premia & performance eval"] --> B{"Methodology & Data"};
    B --> C["Geometric Random Walk<br/>(MCMC method) for random portfolios"];
    B --> D["Data:<br/>Equity Universe & Constraints<br/>(e.g., MSCI Multifactor)"];
    C --> E["Computational Process:<br/>Construct flexible control groups"];
    D --> E;
    E --> F{"Empirical Application & Outcomes"};
    F --> G["Findings:<br/>Identify premia for size, value, quality, momentum<br/>under constraints"];
    F --> H["Highlight challenges:<br/>High-dimensional geometry limits<br/>traditional inference"];