Performance Attribution for Portfolio Constraints
提出一个业绩归因框架，将受约束投资组合的持仓、预期收益、方差、期望效用和实现收益分解为无约束最优组合、单个静态约束以及约束所含信息的影响，并扩展至贝叶斯分析以选择改善未来表现的约束。
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. This paper was accepted by Kay Giesecke, finance. Funding: Research funding from the National Key Research and Development Program of China [Grant 2022YFA1007900], the National Natural Science Foundation of China [Grants 12271013, 72342004], Peking University’s Fundamental Research Funds for the Central Universities, and the MIT Laboratory for Financial Engineering is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05365 .