Identifying household finance heterogeneity via deep clustering

Yoontae Hwang, Yongjae Lee () and Frank J. Fabozzi ()
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Yoontae Hwang: Ulsan National Institute of Science and Technology (UNIST)
Yongjae Lee: Ulsan National Institute of Science and Technology (UNIST)
Frank J. Fabozzi: EDHEC Business School

Annals of Operations Research, 2023, vol. 325, issue 2, No 20, 1255-1289

Abstract: Abstract Households are becoming increasingly heterogeneous. While previous studies have revealed many important insights (e.g., wealth effect, income effect), they could only incorporate two or three variables at a time. However, in order to have a more detailed understanding of complex household heterogeneity, more variables should be considered simultaneously. In this study, we argue that advanced clustering techniques can be useful for investigating high-dimensional household heterogeneity. A deep learning-based clustering method is used to effectively handle the high-dimensional balance sheet data of approximately 50,000 households. The employment of appropriate dimension-reduction techniques is the key to incorporate the full joint distribution of high-dimensional data in the clustering step. Our study suggests that various variables should be used together to explain household heterogeneity. Asset variables are found to be crucial for understanding heterogeneity within wealthy households, while debt variables are more important for those households that are not wealthy. In addition, relationships with sociodemographic variables (e.g., age, education, and family size) were further analyzed. Although clusters are found only based on financial variables, they are shown to be closely related to most sociodemographic variables.

Keywords: Household finance; Heterogeneous household; High-dimensional data; Clustering; Machine learning; Deep learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04900-3

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