Presenting a New Bankruptcy Prediction Model Based on Adjusted Financial Ratios According to the General Price Index

Document Type : Research Paper


1 Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran

2 Department of Accounting, Faculty of Economics and Management,University of Qom, Qom, Iran



In a volatile economic environment, financial decision making is always associated with risk. Bankruptcy, as one of the most important risks, has a significant impact on the interests of the firm's stakeholders, so presenting appropriate bankruptcy forecasting patterns is of the utmost importance. In this study, after reviewing the theoretical literature and selecting the financial ratios used in previous bankruptcy prediction models as the variable input of the initial model, the financial ratios were adjusted based on the price index and then, using the LARS algorithm, the ratios that have the highest ability to differentiate between bankrupt and non-bankrupt firms were identified, and finally, using the SVM and Naive Bayesian algorithms, the final bankruptcy prediction model was developed. For this purpose, the data of 50 companies listed in Tehran Stock Exchange who had experienced bankruptcy for at least one year from 2008 to 2018 under Article 141 of the Commercial Code were used. The results show that the adjusted financial ratios based on the price index in the model presented by SVM algorithm can be a very good predictor for bankruptcy of companies with an accuracy of 99.4%.


[1] Altman, E.I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 1968, 23(4), P. 589- 609. Doi:org/10.1111/j.1540-6261.1968.tb00843.x
[2] Altman, E.I., Haldeman, R.G., Narayanan, p., ZETA analysis: a new model to identify bankruptcy risk of corporations, Journal of Banking and Finance, 1977, 1(1), P. 29- 54. Doi:org/10.1016/0378-4266(77)90017-6.
[3] Beaver, W., Financial Ratios as Predictors of Failures, In Empirical Research in Accounting, selected studies supplement to the Journal of Accounting research 4, 1966, P. 71- 111.
[4] Beaver, W.H., Kennelly, J.W., Predictive Ability as a Criterion for the Evaluation of Accounting Data, The Accounting Review, 1968, P. 675.
[5] Buggakupta, S., The Development of Thai DA Model in Predicting Fallure of Thai listed Companies, Thai. J. Index, 2003, 22(1), P. 9- 10.
[6] Campbell, J.Y., Hilscher, J., Szilagyi, J., IN search of distress risk, The Journal of Finance, 2008, 63 (6), P. 2899- 2939. Doi:org/10.1111/j.1540-6261.2008.01416.x
[7] Christidis, A., Gregory, A., Some new models for Financial distress prediction in the UK, Xfi-Centre for Finance and Investment Discussion, 2010, P. 10. DOI: 10.2139/ssrn.1687166.
[8] Charalambakis, E., Espenlaub, S., Garrett, I., On the Of Prediction Financial Distress for UK Firms: Does the Choice of Accounting and Market Information Matter, the University of Manchester, 2009.
[9] Cook, H., The relationship between Earnings Changes and Balance Sheet Composition, M.Sc, 2005, (in Persian)
[10] Dastgir, M., Zafari, F., The Role of Accounting Information in Forecasting Stock Returns, Journal of Stock Exchange, 1388, 85, P. 48- 55, (in Persian)
[11] Davoodi, A., Dadashi, I., Stock price prediction using theChaid rule-based algorithm and particle swarm optimization, Advances in Mathematical Finance and Applications, 2020, 5(2), P. 197-213.Doi: 10.22034/amfa.2019.585043.1184
[12]Davoodi, A., Dadashi, I., Azinfar, K., Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in lin-ear and nonlinear mode), Advances in Mathematical Finance and Applications, 2020, Doi: 10.22034/amfa.2019.1869838.1232
[13] Deakin, E.B., A discriminant analysis of predictors of Business failure, Journal of Accounting Reaserch, 1972, 10, P. 167- 179. Doi:10.2307/2490225.
[14] Delen, D., Kuzey, C., Uyar, A., Measuring firm performance using financial ratios: A decision tree approach, Expert systems with application, 2013, 40, P. 3970- 3985. Doi:org/10.1016/j.eswa.2013.01.012.
[15] Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic multiplicative DEA model for measuring the efficiency and ranking of DMUs under VRS technology, Indian Journal of Science and Technology, 2014, 7 (11), P. 1765–1773. Doi: 10.17485/ijst/2014/v7i11.19
[16] Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic Modified MAJ Model for Measuring the Efficiency and Ranking of DMUs, Indian Journal of Science and Technology, 2015, 8(8), P. 1-7, Doi: 10.17485/ijst/2015/v8iS8/71505
[17] Efron, B., Hstie, T., Johnstone. I., Tibshirani, R., Least Angle Regresion. The Annals of Statistics, 2004, 32(2), P. 407- 499.
[18] Farajzadeh Dehkordi, H., Application of Genetic Algorithm in Bankruptcy Prediction, M.Sc., Tarbiat Modarres University, 2007, (in Persian)
[19] Fulmer , J.G., et al., A Bankruptcy Classification Model for Small Firms, Journal of commercial Bank Lending, 1984, P. 25- 37.
[20] Grice, J, Ingram, R, Tests of the generalizability of Altman’s Bankruptcy prediction model, Journal of Business Research, 2001, 54(13), P. 53 -61. doi:org/10.1016/S0148-2963(00)00126-0.
[21] Hui, X., Sun, J., An application of support vector machine to companies financial distress prediction, Lect. Notes Artif Intell, 2006, 5, P. 274-282.
[22] Jae, K., Predictiting financial distress of the South Korean Manufacturing industries, Expert Systems with Applications, 2012, 39, P. 9159-9165. Doi: 10.1016/j.eswa.2012.02.058.
[23] Kamijani, A., Saadatfar, J. Application of Neural Network Models in Predicting Economic Bankruptcy of Stock Exchange Companies, Economic Research Quarterly, 2006, 6(6), (in Persian)
[24] Khanqah Barzegari, J., Jamali, Z., Predicting Stock Returns Using Financial Ratios, Journal of Accounting and Auditing Research, 2016, 6(2), no. 2, P. 71- 71. (in Persian)
[25] Kurdestani, G., Tatli, R., Assessing the Predictive Capacity of Bankruptcy Models, Quarterly Audit Knowledge, 2014, 14(55). (in Persian)
[25] Kouki, M., Elkhaldi, A., Toward a Predicting Model of Firm Bankruptcy, Evidence from the Tunisian Context. Middle Eastern Finance and Economics, 2011, 14. P. 26- 43.
[26] Lee, M., To, C., Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress, International Journal of Artificial Intelligence & Applications, 2010, 1(3). P. 31-43. Doi: 10.5121/ijaia.2010.1303
[26] Legault, J., C.A.- Score, A Warning System for Small Business Failures, Bilanas, 1987, P. 29- 31.
[27] Min , H.J, Lee , C.y., Bankruptcy prediction usingsupport vector machine with optimal choice of kernel function parameters, Expert Systems with Applications, 2005, 28, P. 603 -614. Doi:org/10.1016/j.eswa. 2004. 12.008.
[28] Mutchler, J., McKeown, J. & Hopwood, W., towards an explanation of auditor failure to modify the audit opinion of bankrupt companies, Auditing: A Journal of Practiceand Theory, 1991, 10, P. 1- 13.
[29] Odom, M., Sharda, R., A Neural Network Model for Bankruptcy Prediction, Proceedings of the IEEE International Conference on Neural Networks, 1990, 2, P. 163-168.Doi: 10.1109/IJCNN.1990.137710.
[30] Ohlson, J.A., Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, 1980, 18(1), P. 109- 131. Doi: 10.2307/2490395.

 [31] Pawełek, B., Baryła, M., Pociecha, J.,Study of the classification accuracy measures for predicting corporate bankruptcy taking into account changes in the economic environment, Argumenta Oeconomica, 2020, 1(44), P. 5-17. Doi:10.15611/aoe.2020.1.01


[32] Philosophov, L., Philosophov, V., Corporate Bankruptcyprognosis; An attempt at a combined prediction of the bankruptcy eventand time interval of its occurrence, International Review of Financial Analysis, 2002, 11(3). P. 375- 406. Doi:org/10.1016/S1057-5219(02)00081-9.
[33] Pirishe, R., Dadashi Arani, H., Presenting Mathematical Model of Bankruptcy Prediction, Journal of Financial Engineering and Securities Management, 2016, 31. (in Persian)
[34] Raei, R., Fallahpour, S., Predicting financial distress of companies using artificial neural networks, MSc in Financial Management, University of Tehran, 2004. (in Persian)
[35] Raei, R., Fallahpour, S., Application of Support Vector Machine in Predicting Corporate Financial Distress Using Financial Ratios, Journal of Accounting and Auditing Reviews, 2008, 15(53), P. 17- 34. (in Persian)
[36] Rezaei, N., Javaheri, M., The Predictability Power of Neural Network and Genetic Algo-rithm from Companies’ Financial Crisis Advances in Mathematical Finance and Applications, 2020, 5(2), P. 183-196.Doi: 10.22034/amfa.2019.1863963.1195
[37] Royaee R.A., Inflation Accounting and Accounting Development Seminar in Iran, Third Iranian Accounting Seminar, 1993, 9(56). (in Persian)
[38] Sadeghi, h. Rahimi, P. Barber, Y., The Impact of Macroeconomic Factors and Governance System on the Financial Crisis of Manufacturing Companies Listed in Tehran Stock Exchange, Monetary Economics Quarterly, 2014, 21, P. 107- 127. Doi: 10.22067/pm.v21i8.45860.(in Persian)
[39] Sheikh, M.J., Investigation of Average Industry Ratios in Listed Companies, M.Sc, University of Tehran, 1996. (in Persian)
[40] Shirata Cindy Y., Financial Ratios as Predictors of Bankruptcy in Japan: An Enprical Research.. Tsukuba College of Technology, 1987. Doi:
[41] Shin, k.s et al, An application of support vactor machines in bankruptcy prediction model, Expert Systems with Applications, 2005, 28, P. 127- 135. Doi:org/10.1016/j.eswa.2004.08.009.
[42] Shumway, T., Forecasting bankruptcy more accurately: A simple hazard model, The Journal of Business, 2001, 74(1), P. 101- 124. Doi: 10.1086/209665.
[43] Springate, L., Gord, V., Predicting the possibility of failure in a Canadian firm, Unpublished M.B.A Research Project, Simon Fraser university, 1987.
[44] Tamari, M., Financial ratios as a means of forecasting bankruptcy, Management International Review, 1996, 6(4), P. 15-21.
[45] Tinoco Hernandez , M., & Wilson, N., Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables, International Review of Financial Analysis, 2013, 30, P. 394- 419. Doi:org/10.1016/j.irfa.2013.02.013.
[46] Titterington, D., Comparison Of Discrimination Techniques Applied to a Complex Data-set of Head-Injured Patients (with discussion), Journal of the Royal Statistical Society; 1981, P. 145- 175. Doi: 10.2307/2981918.
[47] Tone, K., Toloo, M., Izadikhah, M., A modified slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research, 2020, 287 (2), P. 560-571, Doi: 10.1016/j.ejor.2020.04.019.
[48] Wallace W.A., Risk Assessment by Internal Auditors Using Past Research on Bankruptcy Applying Bankruptcy Models, Working paper, University of Florida, 2004.
[49] Xie, C., Luo, C., & Yu, X., Fainancial distress prediction based on SVM and MDA methods:the case of Chinese listed companies, Qual Quant, 2011, 45, P. 671-686. Doi: 10.1007/s11135-010-9376-y.
[50] Zand Raisi, A., Financial Statements in Inflation. the world of economy, 2012, 2561. (in Persian)
[51] Zavgren, C.V., Assessing the Vulnerability to Failure of American Industrial Firms: A Logistic Analysis. Journal of Business Finance and Accounting, 1985, 12. P. 19- 45. Doi:org/10.1111/j.1468-5957.1985.tb00077.x
[52] Zmijewski, M.E., Methodological Issues Related to the Estimation of Financial Distress prediction Models, Journal of Accounting Research, 1984, 22, P. 59-82. Doi: 10.2307/2490859.
Volume 6, Issue 4
October 2021
Pages 717-732
  • Receive Date: 08 December 2019
  • Revise Date: 20 August 2020
  • Accept Date: 24 August 2020
  • First Publish Date: 01 October 2021