Financial Well-Being in Older Adults: A Machine Learning Analysis of 47 Potential Predictors

Abstract

This study examined potential predictors of financial well-being in older adults. Data were drawn from the Health and Retirement Study, focusing on psychosocial, demographic, and lifestyle variables. Random Forest analysis was performed to assess the relative importance of 47 potential predictors, offering a data-driven evaluation of which factors are most strongly associated with subjective financial well-being. Results showed that psychological variables (particularly chronic stress, life satisfaction, perceived control, and optimism) were stronger predictors than demographic indicators. Among demographic variables, education was the most important. The results suggest that financial well-being reflects individuals’ ability to maintain a sense of satisfaction, optimism, and agency in the face of life challenges, rather than being determined solely by economic or demographic conditions.

Keywords

financial well-being predictors random forest machine learning HRS

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