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The Journal of Financial Data Science

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A Machine Learning Approach in Regime-Switching Risk Parity Portfolios

A. Sinem Uysal and John M. Mulvey
The Journal of Financial Data Science Spring 2021, 3 (2) 87-108; DOI: https://doi.org/10.3905/jfds.2021.1.057
A. Sinem Uysal
is a PhD student in the Operations Research and Financial Engineering Department at Princeton University in Princeton, NJ
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John M. Mulvey
is a professor in the Operations Research and Financial Engineering Department at Princeton University in Princeton, NJ
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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.

TOPICS: Big data/machine learning, portfolio construction, performance measurement

Key Findings

  • ▪ We examine a regime prediction problem with supervised learning approaches and implement regime-switching risk parity portfolios.

  • ▪ All recession periods after 1973 are captured by the random forest model, and stock market regime predictions lead to better portfolio performance.

  • ▪ 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.

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The Journal of Financial Data Science: 3 (2)
The Journal of Financial Data Science
Vol. 3, Issue 2
Spring 2021
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A Machine Learning Approach in Regime-Switching Risk Parity Portfolios
A. Sinem Uysal, John M. Mulvey
The Journal of Financial Data Science Apr 2021, 3 (2) 87-108; DOI: 10.3905/jfds.2021.1.057

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A Machine Learning Approach in Regime-Switching Risk Parity Portfolios
A. Sinem Uysal, John M. Mulvey
The Journal of Financial Data Science Apr 2021, 3 (2) 87-108; DOI: 10.3905/jfds.2021.1.057
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    • LITERATURE REVIEW
    • REGIME-BASED ASSET ALLOCATION FRAMEWORK
    • COMPUTATIONAL RESULTS
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