Performance Attribution for Portfolio Constraints

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 ESG 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 JEL Classification: C10, G11, G12, Q56

JEL Classification: C10, G11, G12, Q56