Performance Attribution for Portfolio Constraints

• 成果类型: Article
• 署名作者: Lo, Andrew W.; Zhang, Ruixun
• 署名单位: Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); The Santa Fe Institute; Peking University; Peking University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.05365
• 发表日期: 2025
• 关键词: portfolio theory; performance attribution; constraints; INFORMATION; ESG investing; Socially responsible investing

摘要

We propose a new performance attribution framework that decomposes a constrained portfolio's holdings, expected returns, variance, expected utility, and realized returns into components attributable to (1) the unconstrained mean-variance optimal portfolio; (2) individual static constraints; and (3) information, if any, arising from those constraints. A key contribution of our framework is the recognition that constraints may contain information that is correlated with returns, in which case imposing such constraints can affect performance. We extend our framework to accommodate estimation risk in portfolio construction using Bayesian portfolio analysis, which allows one to select constraints that improve-or are least detrimental to-future performance. We provide simulations and empirical examples involving constraints on environmental, social, and governance portfolios. Under certain scenarios, constraints may improve portfolio performance relative to a passive benchmark that does not account for the information contained in these constraints.