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.
Du, Y.; Huang, W. Portfolio Allocation with Medical Expenditure Risk-A Life Cycle Model and Machine Learning Analysis. Journal of Regional Economics, 2023, 2, 10. https://doi.org/10.58567/jre02010005
Du Y, Huang W. Portfolio Allocation with Medical Expenditure Risk-A Life Cycle Model and Machine Learning Analysis. Journal of Regional Economics; 2023, 2(1):10. https://doi.org/10.58567/jre02010005
Du, You; Huang, Weige 2023. "Portfolio Allocation with Medical Expenditure Risk-A Life Cycle Model and Machine Learning Analysis" Journal of Regional Economics 2, no.1: 10. https://doi.org/10.58567/jre02010005
Du, Y., & Huang, W. (2023). Portfolio Allocation with Medical Expenditure Risk-A Life Cycle Model and Machine Learning Analysis. Journal of Regional Economics, 2(1), 10. https://doi.org/10.58567/jre02010005
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