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.

翻译：本文探讨了随机化控制技术在实证资产定价和绩效评估中的应用。文章引入了几何随机游走——一类马尔可夫链蒙特卡洛方法——用于构建灵活的控制组，即遵循投资者约束的随机投资组合。这种基于抽样的方法能够从实践角度探索学术研究中因子溢价与绩效之间的关系。在实证应用中，本研究评估了在高度约束环境下捕获规模、价值、质量和动量相关溢价的潜力，并以MSCI多元化多因子指数的投资者准则为例。此外，文章还指出了随机投资组合在绩效评估推断中的传统应用问题，揭示了高维几何复杂性所带来的挑战。