Investigating and Comparing the Performance of Conventional and Hybrid Models of Predicting Financial Distress

Document Type : Research Paper


1 Ph.D. Candidate, Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Prof., Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Associate Prof., Department of Financial Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.

4 Associate Prof., Department of Financial Management, Faculty of Management, Tehran Central Branch, Islamic Azad University, Tehran, Iran.


Objective: Financial distress, which is defined as the uncertainty about the company's ability to meet its obligations and repay its debts, has been estimated by different models divided into three groups of fundamental models (based on accounting or financial data), structural models (based on the company's capital structure or market information) and hybrid models.  Accurately predicting financial distress is still a major point of challenge for financial researchers. Scholars acknowledge that financial distress will be experienced when it happens. Therefore, the best thing to do is to initially estimate the probability of a company's financial distress. In this regard, in the current study, first, a hybrid model was presented to investigate the ability of financial distress prediction models. Next, in order to compare the hybrid model with accounting-based models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based models, Merton's model was used in three groups including healthy, distressing, and distressed companies.
Methods: In this research, by reviewing past studies, 47 variables affecting financial distress, such as accounting variables, market variables, and macroeconomic indicators were identified. Afterward, considering the frequency and successful performance of these variables in past studies, 19 variables were selected. In the next step, using the Stepwise regression test, among the 19 variables, 10 variables with probability values ​​smaller than 0.05 were chosen. Also, to determine the dependent variable, the European option pricing model (Merton's model) was used. Finally, by the use of the Multinomial logit model and identifying the relationship between the dependent and independent variables, the hybrid model for predicting financial distress was designed. In order to compare the produced hybrid model with accounting-based fundamental models, the second version of Altman's Z model known as the Z˝ model was used. To compare the hybrid model with market-based structural models, Merton's model was used. Moreover, in order to test the ability of financial distress prediction models, a sample including 100 companies listed on the Tehran Stock Exchange (TSE) or Iran FaraBourse (IFB) was selected. Then, considering the defined criteria, these companies were divided into three groups consisting of healthy, distressing, and distressed companies. Finally, the ability of the above-mentioned models in predicting financial distress was investigated.
Results: Research findings indicated that in the hybrid model, the ratios of Net Working Capital to Total Assets (WCTA), Operating Cash Flow to Total Assets (OCTA), Sales to Total Assets (STA), Net Income to Total Assets (NITA), Short-term and Long-term Debts to Equity (TLTE), Price to Earnings Per Share (P/E) and Price to Sales (P/S), and the variable of Interest Rate (INT) had significant relations with company's financial distress probability. Also, a comparison of the hybrid model and conventional models revealed that in the group of financially distressed companies, respectively, the Z˝ model with 100% accuracy, Merton's model with 85% accuracy, and the hybrid model with 90% accuracy had correctly predicted the financial situation of the companies. While, in the group of financially distressing companies, the accuracy of the Z˝ model, Merton's model, and the hybrid model in predicting the financial situation of the companies, stood at 50%, 85%, and 85%, respectively. In addition, in the group of healthy companies, these models were able to correctly predict 95%, 85%, and 90% of the companies' financial situation, respectively.
Conclusion: According to achieved results, the Z˝ model has higher predictive power on healthy and distressed companies, compared to the hybrid and Merton models. While, the hybrid and Merton models are better at predicting the financial situation of distressing companies than the Z˝ model. Therefore, considering that the performance of the market-based model of Merton in predicting the financial situation of the companies is weaker than those of the Z˝ and that the hybrid models which are mainly formed by financial or accounting ratios, and also in regard to the findings of past studies which proved the inefficiency of the stock market in Iran, it can be concluded that it is better to use accounting variables in future research in the field of predicting financial distress.


Ahmadpour, A., Mirzaie Asrami, H. (2013). Compared with Multiple Discriminate Analysis Model and neural network Models in Predicting Bankruptcy of the listed Companies in Tehran Stock Exchange. Accounting and Auditing Research, 5(19), 4-21. (in Persian)
Altman, E. I. & Saunders, A. (1993). Credit risk measurement: Developments over the last 20 years. Journal of Banking & Finance, 21(11(, 1721-1742.
Altman, E.I. (1968). Financial ratios, discriminate analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ashtab, A., Haghighat, H., Kordestani, GH. (2018). Investigating the Relationship between Predicted Financial Distress and Earnings management Approaches Based on Structural Equations. Financial Research Journal, 20(4), 467-488. (in Persian)
Aydin, A.D. & Cavdar, S.C. (2015). Two different points of view through artificial intelligence and vector autoregressive models for ex post and ex ante forecasting. Journal of Computational Intelligence and Neuroscience, 20(3), 1-11.
Bateni, L. & Asghari, F. (2016). Bankruptcy prediction using logit and genetic algorithm models: A Comparative analysis. Retrieved from:
Beaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111.
Botshekan, M.H, Salimi, M.J, Falahatgar Mottahedjoo, S. (2018). Developing a hybrid approach for financial distress prediction of listed companies in Tehran stock exchange. Financial Research Journal, 20(2), 173-192. (in Persian)
Brédart, X. (2014). Bankruptcy prediction model using neural networks. Accounting and Finance Research, 3(2), 124-148.

Fadaei Nejad, M.E., Shahriyari, S., Salim, F. (2015). An analysis of the relationship between financial distress risk and equity returns. Accounting and Auditing Review, 22(2), 243-262. (in Persian)

Fallahpour, S., Raei, R., Norouzian Lakvan, E. (2018). Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market. Financial Research Journal, 20(3), 283-304. (in Persian)
Fernandez, A., Antonio, J., Campos, S. & Alaminos, D. (2020). European Country Heterogenity in Financial Distress Prediction. Economic Modelling, 3(88), 398 -407.
Fulmer, J.(1984). A Bankruptcy classification Model For Small Firms, Journal of Commercial bank Lending, 44(9),130-148.
Gholizadeh Salteh, T., Eghbalnia, M., Aghababaei, M.E. (2019). Grey Wolf Optimization Evolving Kernel Extreme Learning Machine: Application to Bankruptcy Prediction. Financial Research Journal, 21(2), 187-212. (in Persian)
Hamilton, B. (2014). The 9 biggest financial warning signs. Retrieved from:
Hillegeist, S., Keating, E., Cram, D. & Lundstedt, K. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 35(9), 5-34.
Isquierdo, N., Erkki, K.L., Pascual, D. (2020). Does Audit Report Information Improve Financial Distress Prediction over Altman Traditional Z -score Model? Journal of International Financial, Management & Accounting, 1(31), 65 -97.
Jones, S. & Hensher, D. A. (2004). Predicting firm financial distress: A mixed Logit model, The Accounting Review, 79(4), 1011-1038.
Keasey, K. and Watson, R. (1986). The prediction of small company failure: some behavioural evidence for the UK. Accounting and Business Research, 56 (4), 49–57.
Li, M.L. & Miu, P. (2010). A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: a binary quantile regression approach. Journal of Empirical Finance, 17(4), 818-833.
López Iturriaga, F.J. & Sanz, I.P. (2015). Bankruptcy visualization and prediction using
neural networks: A study of US commercial banks. Expert Systems with Applications, 42(4), 2857-2869.

Mansourfar, Gh., Ghayour, F., Lotfi, B. (2015). The Ability of Support Vector Machine (SVM) in Financial Distress Prediction. Journal of Empirical Research In Accounting, 5(17), 177-195. (in Persian)

Masoudi, F. (2018). The effect of corporate governance mechanisms on audit quality in healthy, gray and financial distress. M.S. Thesis. Islamic Azad University. Qazvin. (in Persian)

Mensah, Y. (1983). The differential bankruptcy predictive ability of specific price level adjustments: some empirical evidence. Accounting Review, 58(2), 228–246.
Mertens, R. L., Poddig, T. & Fieberg, C. (2016). Forecasting corporate defaults in the German stock market. Working Paper, Retrieved from:
Merton, R.C. (1974). On the pricing of corporate debt: the risk structure of interest rates. Journal of Finance, 45(29), 449-470.

Mohammadzadeh, P., Jalili Marand, A. (2012). Prediction Of Bankruptcy Using Mixed Logit Model . Journal of Economic Modeling Research, 2(8), 1-21. (in Persian)

Nabavi Chashmi, A., Ahmadi, M., Mahdavi Farahabadi, S.(2011). Investing In A Prediction Of Firm Bankruptcy With Logic Model. Financial Engineering and Securities Management  5(1), 1-21. (in Persian)
Öcal, N., Ercan, M. K. & Kadıoğlu, E. (2015). Predicting financial failure using decision tree algorithms: An empirical test on the manufacturing industry at Borsa Istanbul. International Journal of Economics and Finance, 7(7), 189-206.
Ohlson, J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(2), 109-131.
Pindado, J., Rodrigues, L. F. & De la Torre, C. (2008). Estimating financial distress likelihood. Journal of Business Research, 61, (125), 995-1003.
Rostami, M.R., Fallahshams, M.F., Eskandari, F. (2011). The Assessment Of Financial Distress In Tehran Stock Exchange: A Comparative Study Between Data Envelopment Analysis (Dea) And Logistic Regression (Lr). Management Research in Iran, 3(15), 129-147.
(in Persian)
Sabela, W.S. (2016). A Three-Tier Approach To Determine Financial Distress Of Companies Listed on the Johannesburg Stock Exchange. Ph.D Thesis. University of Pretoria.
Sadeghi, H., Rahimi, P., Salmani, Y. (2014). The Effect of Macroeconomic and Governance Factors on Financial Distress in Manufacture Firms Listed in Tehran Stock Exchange. Monetary & Financial Economics, 21(8), 107-127. (in Persian)
Santomero, A. and Vinso, J. (1977). Estimating the probability of failure for firms in the banking system. Journal of Banking and Finance, 21(2), 185–205. 
Sehhat,S. (2014). The comparative analysis of food Companies Bankruptcy by DEA-Additive and DEA-DA. Empirical studies in Financial accounting, 43(11), 153-184. (in Persian)
Senthil, A., Radhakrishna, G.S., Sridevi, P. (2019). Modeling Corporate Financial Distress Using Financial and Non Financial Variables. International Journal of Law and Management 61(3), 457 -484.
Springate, G.L.V. (1978).Predicting the possibility of failure in a Canadian firm (Unpublished master’s thesis). Simon Fraser University, Canada.
Wang, Y. (2011). Corporate default prediction: models, drivers and measurements. Ph.D. Thesis. The University of Exeter.
Westerberg, M., Singh, J., Hackner, E. & Hoonyoung, L. (1997). Bankruptcy prediction using case-based reasoning, neural networks, and discriminate analysis. Expert Systems with Applications, 13(2), 97-108.
Wilcox, J. (1973). A prediction of business failure using accounting data. Empirical research in accounting: selected studies. Journal of Accounting Research, 11(4), 163–179.
Xia, Y. (2016). The real effects of stock market liquidity. Ph.D. Thesis, The University of Hong Kong.
Zebardast, M., Javid, D. & Taherinia, M. (2014). The use of artificial neural network in predicting bankruptcy and its comparison with genetic algorithm in firms accepted in Tehran stock exchange. Journal of Novel Applied Sciences, 3(2), 151-160.
Zmijewski, M. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of accounting research, 22(3),59-82.