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

You Du and Weige Huang
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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

Journal of Regional Economics, 2023, vol. 2, issue 1, 53-68

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.

Keywords: Household finance; medical expenditure risk; life cycle model; machine learning (search for similar items in EconPapers)
Date: 2023
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