Measuring the Credit Risk of Bank Based on Z-Score And KMV- Merton Models: Evidence from Iran
محورهای موضوعی : Risk ManagementMohammad Roshandel 1 , Mirfeiz Fallahshams 2 , Fereydoun Rahnama Roodposhti 3 , hashem nikoumaram 4
1 - PhD Student in Department of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Associate Professor, Department of Business Management, Central Tehran Branch , Islamic Azad University, Tehran, Iran
3 - Science and Research Branch
4 - Professor of Accounting Department, Tehran Sciences and Researches Branch, Islamic Azad University, Tehran, Iran
کلید واژه: KMV-Merton, Bank, Z score, Credit Risk,
چکیده مقاله :
This paper examines the credit risk in the Iranian banks during 2008 to 2018 through the Z-score (Accounting based data) and the KMV-Merton (Market based information) models. In the Merton model, equity is equal to call option on underlying value of the bank’s asset. The market value of assets is estimated by share price. The value of assets is then compared to the value of liabilities. Therefore, default when occurs that the market value of assets is less than the book value of debts. so, value of equity becomes negative. In the Z-score model, Return on Assets and Equity to Assets as the numerator and standard deviation of ROA as the denominator are applied. If the mentioned ratios of numerator increase and the denominator decrease, the probability of default decline. As well as, Independent variables are divided into five groups: leverage, management efficiency, profitability quality, financial health, and liquidity. As a result, capital adequacy and profitability have a greater impact on both models. Also, the ANOVA table proves the validity of two models. The value of ROC test in both models is above average (0.5) which are efficient and their efficiency is 99.48% and 92.68%, respectively. Also, in terms of Voung’s test, the KMV is more efficient than the Z-score.
This paper examines the credit risk in the Iranian banks during 2008 to 2018 through the Z-score (Accounting based data) and the KMV-Merton (Market based information) models. In the Merton model, equity is equal to call option on underlying value of the bank’s asset. The market value of assets is estimated by share price. The value of assets is then compared to the value of liabilities. Therefore, default when occurs that the market value of assets is less than the book value of debts. so, value of equity becomes negative. In the Z-score model, Return on Assets and Equity to Assets as the numerator and standard deviation of ROA as the denominator are applied. If the mentioned ratios of numerator increase and the denominator decrease, the probability of default decline. As well as, Independent variables are divided into five groups: leverage, management efficiency, profitability quality, financial health, and liquidity. As a result, capital adequacy and profitability have a greater impact on both models. Also, the ANOVA table proves the validity of two models. The value of ROC test in both models is above average (0.5) which are efficient and their efficiency is 99.48% and 92.68%, respectively. Also, in terms of Voung’s test, the KMV is more efficient than the Z-score.
[1] Abinzano, I., Gonzalez-Urteaga, A., Muga, Luis, Race across mud: The best choice measuring credit risk SSRN.195907, 2018;1-10. doi: 10.2139/ssrn.3195907
[2] Altman, E.I., Iwanicz-Drozdowska, M., Laitinen, E.K., Suvas, A., Financial and Non-Financial Varia-bles as Long-Horizon Predictors of Bankruptcy, Journal of Credit Risk, 2015; 12(4): 4-12. doi: 10.2139/ssrn.2669668
[3] Ohlson, J.A., Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, 1980; 18(1): 109-131. doi:10.2307/2490395
[4] Avramov, D., Chordia, T., Jostova, G., Pilipov, A., Momentum and Credit Rating, Journal of Finance, 62(5): 2503- 2520.
[5] Abinzano, I., Muga, L., Santamaria, R., Is default risk the hidden factor in momentum returns? Some empirical results, Accounting and Finance, 2014; 54(3): 671 - 698. doi: 10.1111/acfi.12021
[6] Agarwal, V., Taffler, R., Does Financial Distress Risk Drive the Momentum Anomaly? Financial Man-agement, 2008; 37(3): 461-484. doi: 10.1111/j.1755-053X.2008.00021.x
[7] Niklis, D., Doumpos, M., Zopounidis, C., Combining Market and Accounting based Models for Credit Scoring Using a Classification Scheme Based on Support Vector Machine, Technical University of Crete, 2012; 234:1-8. doi: 10.1016/j.amc.2014.02.028
[8] Thomas, L.C., A survey of credit and behavioral scoring: Forecasting financial risk of lending to con-sumers, International Journal of Forecasting, 2000; 149–172. doi: 10.1016/S0169-2070(00)00034-0
[9] Papageorgiou, D., Doumpos, M., and Zopounidis, C., Credit rating systems: Regulatory framework and comparative evaluation of existing methods, Handbook of Financial Engineering, 2008; 457–488.
[10] Abdou, H.A., and Pointon, J., Credit scoring, statistical techniques and evaluation criteria: A review of the literature, Intelligent Systems in Accounting, Finance and Management, 2011;18(2–3): 59–88. doi: 10.1002/isaf.325
[11] Agarwal, V., Taffler, R., Comparing the performance of market-based and accounting-based bank-ruptcy prediction models, Journal of Banking and Finance, 2008; 32(8):1541–1551. doi:10.1016/j.jbankfin.2007.07.014
[12] Hillegeist, S., Keating, E., Cram, D., and Lundstedt, K., Assessing the Probability of Bankruptcy, Re-view of Accounting Studies, 2004; 9(1): 5–34.
[13] Li, M-Y.L. and Miu, P., 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, 2010; 17(4); 818–833. doi:10.1016/j.jempfin.2010.04.004
[14] Yeh, C-C., Lin, F., and Hsu, C-Y., A hybrid KMV model, random forests and rough set theory ap-proach for credit rating, Knowledge-Based Systems, 2012; 33:166–172. doi: 10.1016/j.knosys.2012.04.004
[15] Syversten, B.D.H., How accurate are credit risk models in their predictions concerning Norwegian enterprises, Norges Bank Economic Bulletin, 2004; 4:150–156.
[16] Boumediene, A., Is Credit Risk Really Higher in Islamic Banks? SSRN.1689885, 2011;7(3): 97-129.doi: 10.21314/JCR.2011.128
[17] Paul Wood, A., the Performance of Insolvency Prediction and Credit Risk Models in the UK: A Com-parative Study, Development and Wider Application, the British Accounting Review, 2013; 45(3):100-131. doi: 10.1016/j.bar.2013.06.009
[18] Abedifar, P., Molyneux, P., Tarazi, A., Risk in Islamic Banking. Review of Finance, 2013; 17(6):2035–2096, doi:10.1093/rof/rfs041.
[19] Rajhi, W., Hassairi, S., Islamic Banks and Financial Stability, A Comparative Empirical Analysis be-tween Mena and Southeast Countries, SSRN.2010126, 2013;150-160
[20] Nurul MD, K., Wothington, A., Do Islamic Banks Have Higher Credit Risk, Pacific-Basin Finance Journal, 2015; 34:5-35. doi: 10.2139/ssrn.2479136
[21] Boateng, K., Credit Risk Management and Performance of Banks in Ghana: the CAMELS Rating Model Approach, International Journal of Business and Management Invention, 2019; 8(2): 5-35
[22] Mascia, D., Keasey, K., Vallascas, F., Internal Rating Based Models: Do They Matter for Bank Profit Margins, SSRN.3410461, 2019: 29-30. doi:10.2139/ssrn.3410461
[23] Taleb, L., Khouaja, D., The Subprime crisis once again, Z-score or Rating: A Study on Banks of the Euro Zone, SSRN.3145214, 2015; 34:5-35.
[24] Cihak, M., Hesse, H., Islamic Banks and Financial Stability: An Empirical Analysis, IMF Working Paper, 2008; 16: 1-20. doi: 10.5089/9781451868784.001
[24] Cihak, M., Demirgus kunt, A., Feyen, E., Levibe, R., Benchmarking Financial Systems around the World, Policy Research Working Paper, World Bank, Washington,2012; 6175:5-15.
[25] Avino, D., Salvador, E., Contingent Claims and Hedging of Credit Risk with Equity Options, SSRN.3184004, 2018;1-30. doi: 10.2139/ssrn.3184004
[26] Harada, k., Ito, and Takahashi, S., Is the distance to default a good measure in predicting bank fail-ures? Case studies: National Bureau of Economic Research, 2010; 16182: 15-21. doi: 10.3386/w16182
[27] Kealhofer, S., Quantifying Credit Risk I: Default Prediction, Financial Analysts Journal, 2003; 59(1): 30- 44
[28] Gharghori, P, Chan, H., Faff, R., Investigating the performance of alternative default-risk models: Option-based versus accounting-based approaches, Australian Journal of Management, 2006; 33(1):1-23. doi: 10.1177/031289620603100203
[29] Hilsher, J., Wilson, M., Credit ratings and credit risk: Is one measure enough? Management Science, 2016; 63(10):10-15. doi: 10.1287/mnsc.2016.2514
[30] Martin, D., Early warning of bank failure: A logit regression approach, Journal of Banking and Fi-nance, 1977; 1(3): 249-76. doi:10.1016/0378-4266(77)90022-X
[31] Ioannidis, C., Pasiouras, F., and Zopounidis, C., Assessing bank soundness with classification tech-niques, Omega, 2010; 38(5): P.345-357. doi: 10.1016/j.omega.2009.10.009
[32] Bongini, P., Claessens, S., and Ferri G., The political economy of distress of East Asian financial insti-tutions. Policy Research Working Paper, 2000; 2265, Banque mondiale. doi: 10.1596/1813-9450-2265
[33] Wang, D., Bank Failure and Credit Ratings Term Paper for Financial Markets and Institutions, Work-ing paper, SSRN. 3530031, 2012; 22-23
[34] Danilov, k., Corporate Bankruptcy: Assessment, Analysis and Prediction of Financial Distress, Insol-vency, and Failure, MIT Sloan School of Management, 2014; 39-65.
[35] Kiriri, N. P., Small and Medium Enterprises (SMEs): Valuating Life Cycle Stage Determinants, Strathmore University, Working Paper, 1990; 10-25.
[36] Dickinson, V., Firm Life Cycle and Future Profitability and Growth, School of Business, University of Wisconsin – Madison, Working Paper, june 2005, 2-15.