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

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

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.

Suggested Citation

  • Heo, Wookjae & Lee, Jae Min & Park, Narang & Grable, John E., 2020. "Using Artificial Neural Network techniques to improve the description and prediction of household financial ratios," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    • Handle: RePEc:eee:beexfi:v:25:y:2020:i:c:s2214635019302230
    DOI: 10.1016/j.jbef.2020.100273
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    Cited by:

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    2. Tomasz Korol, 2021. "Evaluation of the Macro- and Micro-Economic Factors Affecting the Financial Energy of Households," Energies, MDPI, vol. 14(12), pages 1-14, June.
    3. Jaewon Park & Minsoo Shin, 2022. "An Approach for Variable Selection and Prediction Model for Estimating the Risk-Based Capital (RBC) Based on Machine Learning Algorithms," Risks, MDPI, vol. 10(1), pages 1-20, January.
    4. Jaewon Park & Minsoo Shin & Wookjae Heo, 2021. "Estimating the BIS Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting by Variable Selection Using Random Forest Algorithms," Risks, MDPI, vol. 9(2), pages 1-19, February.
    5. Surjaningsih, Ndari & Werdaningtyas, Hesti & Rahman, Faizal & Falaqh, Romadhon, 2022. "Predicting Household Resilience Before and During Pandemic with Classifier Algorithms," OSF Preprints w5q9g, Center for Open Science.

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    More about this item

    Keywords

    Artificial Neural Networks (ANN); Machine learning; Ordinary Least Squares (OLS) regression; Prediction; Financial ratios; Panel Study of Income Dynamics (PSID);
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance

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