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


  • Kanitsorn Terdpaopong Faculty of Accountancy, Rangsit University, Patumthani 12000, Thailand


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


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 non-financially 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 ŶThai-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.


Ahn, B.S., Cho, S.S., & Kim, C.Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18, 65-74.

Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23 (4), 589-609.

Altman, E.I., & Sabato, G. (2007). Modelling credit risk for SMEs: Evidence from the U.S. market. Abacus Journal, 43 (3), 332-357.

Altman, E.I., Sabato, G. & Wilson, N. (2008). The value of qualitative information in SME risk management. Retrieved December 1, 2010, from

Bàkiewicz, A. (2005). Small and medium enterprises in Thailand. Following the leader. Asia and Pacific Studies, 2, 131-151.

Bank of Thailand. (2008). NPLs and Loans. Retrieved July 2, 2008, from

Beaver, W.H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research [Empirical research in Accounting: Selected Studies], 4, 71-111.

Beaver, W.H. (1968). Alternative accounting measures as predictors of failure. The Accounting Review Journal, 43 (January), 113-122.

Bernanke, B.S., & Campbell, J.Y. (1988). Is there a corporate debt crisis?, Washington, D.C., U.S.: Brookings Institution Press, 1988 (1), 83-139.

Blum, M. (1974). Failing company discriminant analysis. Journal of Accounting Research, 12 (1), 1-25.

Davidson, W.N., & Dutia, D. (1991). Debt, liquidity and profitability problems in small firms. Entrepreneurship: Theory and Practice, 16 (1), 53-64.

Deakin, E.B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10 (2), 167-179.

Deakin, E.B. (Ed.). (1977). Business failure prediction: An empirical analysis, New York: Wiley.

Edmister, R.O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative Analysis, 7 (March), 1477-1493.

European Commission. (2003). Commission recommendation: Definition of small & medium sized enterprises. Official Journal of the European Union, C(2003) 1422, L 124/39. Retrieved July 5, 2010, from

Fitzpatrick, P.J. (1931). Symptoms of industrial failures. Washington, D.C.: Catholic University of America Press.

Fulmer, J.G., Moon, J.E., Gavin, T.A., & Erwin, J.M. (1984). A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 11 (July), 25-37.

Gentry, J., Newbold, P., & Whitford, D.T. (1985). Classifying bankrupt with fund flow components. Journal of Accounting Research, 23 (1), 146-160.

Hair, J F., Anderson, R.E., Tatham, R.L., & Black, W. (1998). Multivariate data analysis, 5th ed., New Jersey: Prentice Hall.

Holmes, S., Hutchinson, P., Forsaith, D., Gibson, B., & McMahon, R. (2003). Small enterprise finance, Australia: John Wiley & Sons Australia, Ltd.

Holmes, S., & Kent, P. (1991). An empirical analysis of the finanical structure of small and large Australian manufacturing enterprises. Journal of Small Business Management, 1 (2), 141-154.

Horrigan, J.O. (1968). A short history of financial ratio analysis. The Accounting Review Journal, 43 (April), 284-289.

Hutchinson, P., & Michaelas, N. (Eds.). (2000). The current state of business disciplines (Vol. 3). Rohtak, India: Spellbound Publications Ltd.

Institute for Small and Medium Enterprises Development. (2006). Definition of SMEs (in Thai).

Jackendoff, N. (1962). A study of published industry financial and operating ratios. Economics and Business Bulletin (Temple University), 14 (3), 34.

Jen, F.C. (1963). The determinants of the degree of insufficiency of bank credit to small business. Journal of Finance, 18 (December), 694-695.

Khader, S.A., & Gupta, C.P. (Eds.). (2002). Enhancing SME competitiveness in the age of globalization. Tokyo: National Statistical Coordination Board, Asian Productivity Organization.

Kraus, A., & Litzenberger, R.H. (1973). A state-preference model of optimal financial leverage. Journal of Finance, 28 (4), 911-922.

Lee, K.C., Han, I., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18 (1), 63-72.

McGurr, P.T., & Devaney, S.A. (1998). Predicting business failure of retail firms: An analysis using mixed industry models. Journal of Business Research, 43 (3), 169-176.

Office of Advocacy. (1984). The state of small business: A Report of the President. Washington D.C.: Small Business Administration.

Office of SMEs Promotion. (2006). White Paper, in Export and Import by SMEs.

Office of SMEs Promotion. (2007). White Paper, in Export and Import by SMEs.

Office of The National Economic and Social Development Board. (2001). The Ninth National Economic and Social Development Plan (2001 - 2006). Retrieved November 2, 2010, from

Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18 (1), 109-131.

Platt, H.D., & Platt, M.B. (1990). Development of a class of stable predictive variables: The case of bankruptcy prediction. Journal of Business Finance and Accounting, 17 (1), 31-51.

Platt, H.D., & Platt, M.B. (1991). A note on the use of industry-relative ratios in bankruptcy prediction. Journal of Banking and Finance, 15 (6), 1183-1194.

Ross, S.A., Westerfield, R.W., & Jordan, B.D. (2008). Corporate finance fundamentals, New York: McGraw-Hill Irwin.

Swierczek, F.W., & Ha, T.T. (2003). Entrepreneurial orientation, uncertainty avoidance and firm performance: An analysis of Thai and Vietnamese SMEs. The International Journal of Entrepreneurship and Innovation, 4 (1), 46-58.

Tabachnick, G.B., & Fidell, S.L. (2001). Using multivariate statistics, 4th ed., Needham Heights, MA: Allyn & Bacon.

Tam, K.Y., & Kiang, M.Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38 (7), 926-947.

Veskaisri, K. (2007). The relationship between strategic planning and growth in small and medium enterprises (SMEs) in Thailand. RU International Journal, 1 (1), 55-67.

Warner, J.B. (1977). Bankruptcy costs: Some evidence. The Journal of Finance, 2 (May), 337-347.




How to Cite

Kanitsorn Terdpaopong. (2023). Identifying an SME’s debt crisis potential by using logistic regression analysis. Journal of Current Science and Technology, 1(1), 17–26. Retrieved from



Research Article