EARLY WARNING OF PROBLEMATIC FIRMS LISTED IN THE STOCK EXCHANGE OF THAILAND: AN INVESTIGATION OF FINANCIAL RATIOS

Main Article Content

Pornthip Manodamrongsat
Supa Tongkong
Wachira Boonyanet

Abstract

This paper aims to study early warning signs of financial ratios in problematic companies listed on the Stock Exchange of Thailand. The study samples consisted of 122 companies. The researcher observed 61 companies marked C (Caution) and NC (Non-Compliance) from listed companies on the Stock Exchange of Thailand during 2013-2019, represented by group 1, as problematic companies. In addition, the sample group included 61 unmarked C and NC companies, which were considered to be non-problem companies, represented by group 0. In this study, the researcher used the financial ratios calculated from the financial statements obtained from the SETSMART (SET Market Analysis and Reporting Tool) database on the website of the Stock Exchange of Thailand. The study found that financial ratios were statistically significant for demonstrating the pre-warning signs of problematic companies. Specifically for three years before the company was marked with C and NC, financial ratios included debt ratio, debt to equity ratio, return on equity and return on assets were found to be the warning signs. Two years before the company was marked with C and NC, the warning signs were given by 6 financial ratios: current ratio, debt to equity ratio, return on equity, asset turnover ratio, return on assets, and fixed asset turnover ratio. However, one year before the company was marked with C and NC, it was found that the four financial ratios demonstrated as the warning signs were: debt ratio, assets turnover ratio, return on assets, and fixed asset turnover ratio. These factors can explain the change in the dependent variable, namely a problematic company being, 81.1 percent, 83.6 percent, and 86.9 percent respectively.

Article Details

Section
บทความวิจัย (Research Article)

References

Alifiah, M. N. (2014). Prediction of financial distress companies in the trading and services sector In Malaysia using macroeconomic variables. Procedia-Social and Behavioral Sciences, 129, 90-98.

Altman, E.I (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609.

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E.K, Suvas, A. (2014). Distressed Firm and Bankruptcy prediction in an international context: a review and empirical analysis of Altman’s Z-Score Model. Working paper.

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E.K, Suvas, A. (2016). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model. Journal of International Financial Management & Accounting, DOI: 10.1111/jifm.12053.

Ashraf, S., GS Félix, E., & Serrasqueiro, Z. (2019). Do traditional financial distress prediction models predict the early warning signs of financial distress?. Journal of Risk and Financial Management, 12(2), 55.

Bagher, A.N., and Milad, S. (2016). Designing a Bankruptcy Prediction Model Based on Account, Market and Macroeconomic Variables (Case Study: Cyprus Stock Exchange). Iranian Journal of Management studies. 9(1), 125-147.

Bauer, J., and Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches?.A comprehensive test. Journal of Banking & Finance, 40, 432-442.

Bauweraerts, J. (2016). Predicting Bankruptcy in Private Firms: Towards a Stepwise Regression Procedure. International Journal of Financial Research, 7(2). DOI: 10.5430/ijfr.v7n2p147

Bunn, P., & Redwood, V. (2003). Company accounts based modelling of business failures and the implications for financial stability. Bank of England. Working Paper, no.210.

Ghozali, I. (2006). Aplikasi analisis multivariate dengan program SPSS. Badan Penerbit Universitas Diponegoro.

Herliansyah, Y., Nugroho, L., Ardilla, D., & Putra, Y. M. (2020). The Determinants of Micro, Small and Medium Enterpreneur (MSME) Become Customer of Islamic Banks (Religion, Religiousity and Location of Islamic Banks). In The 1st Annual Conference Economics, Business, and Social Sciences (Vol. 2).

Laitinen, E.K. & Suvas, A. (2016). Financial distress prediction in an international context:

Moderating effects of Hofstede’s original cultural dimensions. Journal of Behavioral and Experimental Finance, DOI: 10.1016/j.jbef.2015.11.003.

Liang, D., Lu, C.C, Tsai, C.F, Shih, G.A. (2016). Financial ratios and corporate governance Indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561–572.

Keasey, K.,&Mcguinness, P. (1990). The failure of UK industrial firms for the period 1976–1984, logistic analysis and entropy measures. Journal of Business Finance & Accounting, 17(1), 119–135.

Kennedy, P. (1998). A guide to econometrics. The United Kingdom: TJ International.

Kholisoh, S. N., & Dwiarti, E. (2020). The analysis of fundamental variables and macroeconomic variables in predicting financial distress. Management Analysis Journal, 9(1), 81-90. doi:10.15294/MAJ.V9I1.36395.

Klepac, V., & Hampel D. (2017). Predicting financial distress of agriculture companies in EU. Agricultural Economics – Czech, 63, 347-355. doi: 10.17221/374/2015-AGRICECON.

Manodamrongsat, P. (2019). Early Warning Signs of Problem Firms and Their Turnaround Strategies. Doctoral Dissertation, Rajamangala University of Technology Thanyaburi. Faculty of Business Administration. Business Administration.

Manodamrongsat, P., Tongkong, S., & Boonyanet, W. (2020). Early Warning Signs of Problem Firms. International Journal of Innovation, Creativity and Change, 14(3), 127-150.

Maricica, M., & Georgeta, V. (2012). Business failure risk analysis using financial ratios. Procedia-Social and Behavioral Sciences, 62, 728-732.

Midi, H., Sarkar, S. K., & Rana, S. (2010). Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics, 13(3), 253-267.

Mselmi, N., Lahiani, A., & Hamza, T. (2017). Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis, 50, 67-80.

Okwoche, V. A. O., Eziehe, J. C., & Agabi, V. (2015). Determinants of job satisfaction

among extension agents in Benue State Agricultural and Rural Development Authority (BNARDA), Benue State, Nigeria. European Journal of Physical and Agricultural Sciences Vol, 3(2).

Sari, I. G. A. D. I., & Dwirandra, A. A. N. B. (2019). The ability of organization commitment and moderate worked motivation by the effect of budget goal clarity in budgetary inaccuracy. International Research Journal of Management, IT and Social Sciences, 6(3), 11-17.

Zhai, S. S., Choi, J. G., & Kwansa, F. (2015). A financial ratio-based predicting model for hotel business failure. Global Business & Finance Review (GBFR), 20(1), 71-86.