A Machine Learning Approach in Regime-Switching Risk Parity Portfolios

A. Sinem Uysal, John M. Mulvey

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)87-108
Number of pages22
JournalJournal of Financial Data Science
Volume3
Issue number2
DOIs
StatePublished - Mar 1 2021

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Information Systems
  • Finance
  • Business and International Management
  • Strategy and Management
  • Business, Management and Accounting (miscellaneous)
  • Information Systems and Management

Keywords

  • Big data/machine learning
  • Performance measurement
  • Portfolio construction

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