Portfolio Optimization Constrained by Performance Attribution

TL;DR

This paper explores how constraining portfolio optimization with performance attribution measures, specifically asset allocation and selection effect, can improve risk-adjusted returns and reduce drawdowns in stock portfolios.

Contribution

It introduces a novel approach of using performance attribution measures as constraints in portfolio optimization to enhance performance.

Findings

SE constraints lead to larger performance improvements

Constraints improve risk-adjusted measures like Sharpe and Rachev ratios

Optimizations reduce maximum drawdown

Abstract

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize expected tail loss and investigate both asset allocation (AA) and the selection effect (SE) as hard constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index; the benchmark is an equi-weighted portfolio of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures maximum drawdown, Sharpe ratio, and Rachev ratio. The results suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE, with SE constraints producing the larger performance enhancement.