Performance Attribution for Portfolio Constraints

Andrew W. Lo () and Ruixun Zhang ()
Additional contact information
Andrew W. Lo: Sloan School of Management, Laboratory for Financial Engineering, and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; and Santa Fe Institute, Santa Fe, New Mexico 87501
Ruixun Zhang: School of Mathematical Sciences, Center for Statistical Science, National Engineering Laboratory for Big Data Analysis and Applications, and Laboratory for Mathematical Economics and Quantitative Finance, Peking University, Beijing 100871, China

Management Science, 2025, vol. 71, issue 9, 7537-7559

Abstract: 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.

Keywords: portfolio theory; performance attribution; constraints; information; ESG investing; socially responsible investing (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2024.05365 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:9:p:7537-7559

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().