Abstract
The purpose of this paper is to investigate the accuracy of the discriminant function in the process of predicted financial failure before it occurs. Using the economic definition (profit and loss) and on this basis, the companies have been classified as a failure and Non-failure for three consecutive years. This paper also tries to discover when bankruptcy phenomenon. Therefore, the multi-step analysis was used to achieve this purpose to shed more light on the validity of the discriminant function in the forecasting process with different locations and different economic sectors except for the financial sector of the listed firms in Tehran Stock Exchange during 2016-2018 with the companies traded 101 companies classified to 11 failure companies And 90 non-failure companies with 25 ratios listed under the title of liquidity, solvency, activity and profitability, and comparing the results for three years. In the end, this paper found the success of The Discriminant function in predicting financial failure for all years of study, In other markets to predict financial position. The sample belonging to the Tehran Stock Exchange (TSE) showed that the financial ratios affecting the formation of the legal discriminatory function (model) were different from year to year. The final financial position of companies that lost three consecutive years was also explained by comparison with 2019, Bankruptcy phenomenon of 64% of financially failed companies (losers).
Keywords
References
- Altman, E., & Ratios, F. (1968). Discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.Google Scholar ↗
- Altman Edward, I., Haldeman Robert, G., & Narayanan, P. (1977). Zeta analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Financial, 7, 29-54.Google Scholar ↗
- Yap, B. C. F., Yong, D. G. F., & Poon, W. C. (2010). How well do financial ratios and multiple discriminant analysis predict company failures in Malaysia. International Research Journal of Finance and Economics, 54(13), 166-175.Google Scholar ↗
- Bhunia, A., & Sarkar, R. (2011). A study of financial distress based on MDA. Journal of Management Research, 3 (2), 1.Google Scholar ↗
- Maricica, M., & Georgeta, V. (2012). Business failure risk analysis using financial ratios. Procedia-Social and Behavioral Sciences, 62, 728-732.Google Scholar ↗
- Bhandari, S. B. (2014). Two discriminant analysis models of predicting business failure: A contrast of the most recent with the first model. American Journal of Management, 14 (3), 1-11.Google Scholar ↗
- Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38 (1), 63-93.Google Scholar ↗
- 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 (2), 65-74.Google Scholar ↗
- Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of accounting research, 167-179.Google Scholar ↗
- Ardakani, M. N., Mehrjerdi V.,Z.(2017). Prediction of Firms Bankruptcy based on industry characteristics, Journal of Accounting Research, 7(25), 122-139.( with Persian language).Google Scholar ↗
- Pringle,J. & Harris.S.R,(1948) Essentials of managerial finance , Scott foreman, United State of America.Google Scholar ↗
- Kušter, D. (2023). Construction of bankruptcy prediction model using discriminant analysis and financial ratios. Ekonomija: teorija i praksa, 16(1), 1-22.Google Scholar ↗
- Chada, N., & Hensawang, S. (2022). Bankruptcy Prediction Model using Multiple Discriminant Analysis: Case of Real Estate Companies in Thailand. วารสาร สันติ ศึกษา ปริทรรศน์ ม จร, 10(6), 2336-2346.Google Scholar ↗
- Charles, E., Brown. (1998). Multiple Discriminant Analysis. 115-128. doi: 10.1007/978-3-642-80328-4_10DOI ↗Google Scholar ↗