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An Empirical Analysis of Private SMEs' Insolvency in Thailand Using Machine Learning

Author

Listed:
  • Jaitang, Chalerm

    (Faculty of Economics, Prince of Songkla University, Thailand and Department of Financial and Business Systems, Lincoln University, New Zealand)

  • Li, Zhaohua

    (Department of Financial and Business Systems, Lincoln University, New Zealand)

  • Gan, Christopher

    (Department of Financial and Business Systems, Lincoln University, New Zealand)

Abstract

We investigate the insolvency of private Thai SMEs in the manufacturing, trading, and service sectors from 2017 to 2021. We model insolvency as a function of industry-relative financial ratios, firm characteristics, and local economic conditions using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. The analysis shows the significant influence of financial ratios on the probability of insolvency for all sectors, particularly inventory turnover, accounts payable turnover, assets to equity, and debt to assets ratio. The service sector shows a unique positive effect of working capital to total assets on insolvency risk, implying that firms with high current assets or very low current liabilities are more prone to insolvency. Medium-sized firms, those registered as juristic ordinary partnerships, owned by foreigners, and located in less competitive areas are less likely to face insolvency.

Suggested Citation

  • Jaitang, Chalerm & Li, Zhaohua & Gan, Christopher, 2024. "An Empirical Analysis of Private SMEs' Insolvency in Thailand Using Machine Learning," Asian Journal of Applied Economics/ Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, vol. 31(2), pages 1-30, July-Dece.
    • Handle: RePEc:ris:apecjn:0099
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    More about this item

    Keywords

    SMEs; insolvency; financial ratios; LASSO; Thailand;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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