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Identifying Hidden Factors Associated with Household Emergency Fund Holdings: A Machine Learning Application

Author

Listed:
  • Wookjae Heo

    (Division of Consumer Science, White Lodging-J.W. Marriot Jr. School of Hospitability & Tourism Management, Purdue University, West Lafayette, IN 47907, USA)

  • Eunchan Kim

    (College of Business Administration, Seoul National University, Seoul 08826, Republic of Korea)

  • Eun Jin Kwak

    (Department of Accounting and Finance, University of Wisconsin-Green Bay, Green Bay, WI 54311, USA)

  • John E. Grable

    (Department of Financial Planning, Housing, and Consumer Economics, University of Georgia, Athens, GA 30602, USA)

Abstract

This paper describes the results from a study designed to illustrate the use of machine learning analytical techniques from a household consumer perspective. The outcome of interest in this study is a household’s degree of financial preparedness as indicated by the presence of an emergency fund. In this study, six machine learning algorithms were evaluated and then compared to predictions made using a conventional regression technique. The selected ML algorithms showed better prediction performance. Among the six ML algorithms, Gradient Boosting, k NN, and SVM were found to provide the most robust degree of prediction and classification. This paper contributes to the methodological literature in consumer studies as it relates to household financial behavior by showing that when prediction is the main purpose of a study, machine learning techniques provide detailed yet nuanced insights into behavior beyond traditional analytic methods.

Suggested Citation

  • Wookjae Heo & Eunchan Kim & Eun Jin Kwak & John E. Grable, 2024. "Identifying Hidden Factors Associated with Household Emergency Fund Holdings: A Machine Learning Application," Mathematics, MDPI, vol. 12(2), pages 1-38, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:182-:d:1314100
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    References listed on IDEAS

    as
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