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Journal of Current Science and Technology

ISSN 2630-0656 (Online)

Identifying an SME’s debt crisis potential by using logistic regression analysis

  • Kanitsorn Terdpaopong, Faculty of Accountancy, Rangsit University, Patum Thani, Thailand, Corresponding author; E-mail:


The overall financial stability of the business sector has been a major concern of people involved with the economy, such as policy makers, financial institutions, and investors. The growth of a business, or a lack thereof, will directly affect the stability of that business, and thus the economy in which it operates. The aims of this article are to determine whether a statistical model can identify a firm’s debt crisis. The groups focused on in this article are Small and Medium-sized Enterprises (SMEs) for both those financially distressed and non-financially distressed in the Thai economic market. A total sample of one hundred and fifty-nine firms, comprising both financially distressed and nonfinancially distressed firms has been chosen for this study. Parametric t-test and Mann-Whitney U test were undertaken to distinguish the differences between the financial characteristics of the two SME groups. This study employs a logistic regression analysis to predict the likelihood of survival or failure of SMEs by developing a predictive model called ?Thai-SME. This model achieves a result of 95.6 per cent regarding the classification accuracy of a business, and shows that liquidity and leverage ratios are the most predictive characteristics of Thai SMEs in financial distress. The results suggest that Thai SME failure is largely related to a business developing a debt crisis. The implication of the model could be employed by several stakeholders to identify financially distressed firms and provide an early warning in order to establish the foundations needed to make an informed decision regarding the allocation of resources.

Keywords: financial distress; financial characteristics; Small and Medium-sized Enterprises (SMEs); bankruptcy; logistic regression analysis

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DOI: 10.14456/rjas.2011.22


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