A Machine Learning Approach in Regime-Switching Risk Parity Portfolios

Abstract

The authors present a machine learning approach to regime-based asset allocation. The framework consists of two primary components: (1) regime modeling and prediction and (2) identifying a regime-based strategy to enhance the performance of a risk parity portfolio. For the former, they apply supervised learning algorithms, including the random forest, based on a large macroeconomic database to estimate the probability of an upcoming recession or a stock market contraction. Out-of-sample tests show the reliability of these predictions, especially for recessions in the United States, over the period 1973 to 2020. The probability estimates are linked to a dynamic investment overlay strategy. The combined approach improves risk-adjusted returns by a substantial amount over nominal risk parity in two-asset and multi-asset test cases, even during rising interest rates in the late 1970s. <bold>TOPICS:</bold> <ext-link>Big data/machine learning</ext-link>, <ext-link>portfolio construction</ext-link>, <ext-link>performance measurement</ext-link> <bold>Key Findings</bold> <list><list-item> ▪ We examine a regime prediction problem with supervised learning approaches and implement regime-switching risk parity portfolios. </list-item><list-item> ▪ All recession periods after 1973 are captured by the random forest model, and stock market regime predictions lead to better portfolio performance. </list-item><list-item> ▪ Regime-switching models enhance risk parity portfolios, even during a rising interest rate period. Regime-based overlay strategies provide higher risk-adjusted returns in risk parity strategies. </list-item></list>