Using Artificial Neural Network techniques to improve the description and prediction of household financial ratios

Wookjae Heo, Jae Min Lee, Narang Park and John E. Grable

Journal of Behavioral and Experimental Finance, 2020, vol. 25, issue C

Abstract: The purpose of the study described in this paper was to shed light on the need for alternative methods to improve descriptions and predictions of household financial ratios. Using data from the 2013, 2015, and 2017 Panel Study of Income Dynamics (PSID), this study examined the descriptive and predictive power of an Artificial Neural Network (ANN) model and an Ordinary Least Squares (OLS) model when evaluating household savings-to-income ratios and debt-to-asset ratios cross-sectionally and across time. Results suggest that ANN models provide a better overall model fit when describing and forecasting financial ratios. Findings confirm that machine learning procedures can provide a robust, efficient, and effective analytic method when an educator, researcher, financial service professional, lender, or policy maker needs to describe and/or predict a household’s future financial situation. Suggestions for the implementation of ANN modeling procedures by household finance researchers, practitioners, and policy makers are provided.

Keywords: Artificial Neural Networks (ANN); Machine learning; Ordinary Least Squares (OLS) regression; Prediction; Financial ratios; Panel Study of Income Dynamics (PSID) (search for similar items in EconPapers)
JEL-codes: C18 C53 D12 D14 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:beexfi:v:25:y:2020:i:c:s2214635019302230

DOI: 10.1016/j.jbef.2020.100273

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