A study of Performance of the Hybrid Model in Assessing the Default Risk for Companies Listed on the Tehran Stock Exchange

Document Type : Research Paper

Authors

1 Assistant Professor, Kharazmi University.Tehran.Iran

2 Master of Business Management, Kharazmi University, Tehran, Iran

Abstract

Measuring credit risk and estimating the likelihood of companies failing is one of the most important challenges in the credit sector. Structural and non-structural models are the two main frameworks for estimating default risk and credit risk. However, each of these models has its own strengths and weaknesses. It seems that combining these two frameworks and providing a hybrid model can provide a more accurate prediction of companies' default risk. In the present study, a hybrid model has been used to measure the default risk of listed and over-the-counter companies in the period between 2007-2017 when they have been transferred to the basic market based on the Iranian capital market laws. First, the Merton model (structural models) was used to measure the risk of default of these companies, and then the results of this model were compared with the Z-Altman model (from non-structural models). Then, by regression analysis of different financial ratios, significant variables were identified and Morton's and Z-Altman models were statistically and comparatively compared. The findings show that the hybrid model offers a more accurate prediction of the risk of default than structural and non-structural models. By entering the results of each of these two models into the hybrid model, the statistical power of the hybrid model increases. Therefore, using a combined model will help banks and credit institutions to provide resources to healthier companies with less risk

Keywords

References

راموز، نجمه و محمودی، مریم.(1396). پیش‌بینی ریسک ورشکستگی مالی با استفاده از مدل ترکیبی در بورس اوراق بهادار تهران. راهبرد مدیریت مالی، 5(1)، صص. 51-75.
صالحی، مهدی و بذرگر، حمید.(1394). رابطه بین کیفیت سود و ریسک ورشکستگی. راهبرد مدیریت مالی،3(1)، صص. 113-140.
فلاح‌پور، سعید و طادی، مسعود.(1395). پیش‌بینی ریسک نکول با استفاده از مدل ساختاری توسعه یافته در بورس اوراق بهادار تهران، مجله مهندسی مالی و مدیریت اوراق بهادار، 7 (28)، صص. 1-21.
قالیباف اصل، حسن و افشار، منیژه.(1393). بررسی کاربرد استفاده از مدل KMV در پیش‌بینی ریسک ورشکستگی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران و مقایسه مدل با نتایج مدل رتبه Z آلتمن،  مهندسی مالی و مدیریت اوراق بهادار، 5(21)، صص. 75-88.
نمازی، محمد، حاجیها، زهره و چناری بوکت، حسن.(1397). مدل‌بندی و تعیین اولویت معیارهای مؤثر مدیریت سود واقعی بر پیش‌بینی ورشکستگی، راهبرد مدیریت مالی، 6 (4)، صص. 1-27.
Altman, E. Haldeman, R.& Naranan, P. (1977). ZETA analysis: a new model to identify bankruptcy prediction risk of corporations. Journal of Banking & Finance, 1(1), pp.29–54.
Altman, E.I. (1993). Corporate financial distress and bankruptcy: a complete guide to predicting and avoiding distress and profiting from bankruptcy (3th ed.). New Jersey, Wiley finance edition.
Bellalah, Zouari. & Levyne (2016). The performance of hybrid models in the assessment of default risk, Economic Modeling, 52(Part A), pp.259-265.
Benos, Alexandros. & Papanastasopoulos, George. (2007). Extending the Merton Model: A hybrid approach to assessing credit quality. Mathematical and Computer Modelling.46(1–2), pp.47-68.
Beytollahi, A & Zeinali, H. (2020). Comparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black–Scholes–Merton Model. Iranian Journal of Management Studies, 13(1), pp.69-93.
Black, F & Scholes, M. (1973). On the pricing of options and corporate liabilities. Journal of Political Economy, 81, pp.637–54.
 Black, F. & Cox, J. C. (1976). Valuing corporate securities: Some effects of bond indenture provisions. The Journal of Finance, 31(2), pp.351-367.
 Brockman, Paul. & Turtle, H.J. (2002). A barrier option framework for corporate security valuation. Journal of Financial Economics. 67 (3), pp.511–529.
Emmanuel, Fadugba Sunday & Helen, Edogbanya Olaronke. (2014). On hybrid model for the valuation of credit risk. Applied and Computational Mathematics; 3(6-1), pp.8-11
Huang, J & Huang, M (2002), How much of the corporate-treasury yield spread is due to credit risk? Pennsylvania State University and Stanford University, mimeo.
 Joel, B. (2009). Risk management in banking. New Jersey, John Willey & sons publications.
Kocagil, A E, Escott, P, Glormann, F, Malzkom, W & Scott, A (2002), Moody’s riskcalcTM for private companies: UK’, Moody’s Investor Service, Global Credit Research, Rating Methodology.
Kreis, Yvonne & Leisen, P.J. (2018). Systemic risk in a structural model of bank default linkages. Journal of Financial Stability, 39, pp.221-236.
Merton, R C (1974), The pricing of corporate debt: the risk structure of interest rates, Journal of Finance, 29(2), pp.449–70.
Ohlson, James A. Spring (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), pp.109-131.
Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 42, pp.335–370.
 Saunders, A. (2002). Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms. New Jersey, John Wiley & Sons.
Sobehart, J R, Stein, R, Mikityanskaya, V & Li, L (2000), Moody’s public risk firm risk model: A hybrid approach to modeling short term default risk, Moody’s Investor Service, Global Credit Research, Rating Methodology.
Tudela, M & Young, G. (2005). A Merton-model approach to assessing the default risk of UK public companies. International Journal of Theoretical and Applied Finance. 8(6), pp.737–761.
 

Files

History

  • Receive Date: 14 May 2019
  • Revise Date: 17 November 2020
  • Accept Date: 02 December 2020

Share

How to cite

Statistics