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Portfolio Allocation with Medical Expenditure Risk-A Life Cycle Model and Machine Learning Analysis

Author

Listed:
  • You Du

    (Department of Economics, Finance, and Accounting, Robbins College of Business and Entrepreneurship, Fort Hays State University, Hays, KS, USA)

  • Weige Huang

    (Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, China)

Abstract
This paper explores how the medical expenditure risk affects the households’ portfolio choice across health status theoretically in a life cycle model and empirically using machine learning methods. Medical expenditure risk, as a background risk, has the potential to influence households’ financial decisions. A higher medical expenditure risk leads to a larger fluctuation and more uncertainty in households’ consumption and therefore utility. As a result, risk-free assets become more attractive. Our machine learning analysis provides evidence that aligns with the predictions of the theoretical life cycle model. Specifically, households with better health hold a larger proportion of stocks in their portfolios. Furthermore, when facing increased medical expenditure risk, households in good health demonstrate a greater willingness to invest in safe assets.

Suggested Citation

  • You Du & Weige Huang, 2023. "Portfolio Allocation with Medical Expenditure Risk-A Life Cycle Model and Machine Learning Analysis," Journal of Regional Economics, Anser Press, vol. 2(1), pages 53-68, October.
    • Handle: RePEc:bba:j00009:v:2:y:2023:i:1:p:53-68:d:234
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    References listed on IDEAS

    as
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