Abstract
<p class="MsoNormal" style="margin-top: 12.0pt;"><span lang="EN-US" style="font-family: verdana, geneva, sans-serif;">This paper explores how the medical expenditure risk affects the households&rsquo; 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&rsquo; financial decisions. A higher medical expenditure risk leads to a larger fluctuation and more uncertainty in households&rsquo; 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.</span></p>
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