Conditional Portfolio Optimization: Using Machine Learning to Adapt Capital Allocations to Market Regimes

Abstract

Conditional Portfolio Optimization is a portfolio optimization technique that adapts to market regimes via machine learning. Traditional portfolio optimization methods take summary statistics of historical constituent returns as input and produce a portfolio that was optimal in the past, but may not be optimal going forward. Machine learning can condition the optimization on a large number of market features and propose a portfolio that is optimal under the current market regime. We call this Conditional Portfolio Optimization (CPO). Applications on portfolios in vastly different markets suggest that CPO can outperform traditional optimization methods under varying market regimes.

Keywords: portfolio optimization, machine learning, factor models

JEL Classification: G11, G17