Impact of Estimating Fair Values of Bank Loans Using the Approach of the International Financial Reporting Standards (Case Study: An Iranian Bank)

Document Type : Research Paper

Authors

1 1. Ph.D. Student , Faculty of Economics and Social Science, Alzahra University, Tehran, Iran

2 2. Prof., Faculty of Economics and Social Science, Alzahra University, Tehran, Iran

3 Ph.D., Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran

4 Assistant Prof., Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran

Abstract

In this paper, fair value and impairment of an Iranian bank's loan portfolio is estimated using the approach of International Financial Reporting Standards and the result is compared with values using the approach of Central Bank of Iran which is based on reporting historical cost and incurred loss. Present value of future cashflows and expected credit loss are used for calculating fair value. Expected credit loss is estimated through predicting probability of default and loss given default based on models developed using neural network method and data from loans paid during years 2007 to 2016. The results of fair value and expected credit loss from 208 loan contracts, which comprise 82 percent of bank's total loan portfolio in 2017, show that the ratio of expected credit loss to incurred loss is 2/3 which is considerable, but the ratio of the fair value to historical cost is 97 percent which is not considerable. Furthermore, findings show that the approach of IFRS has an impact on the capital adequacy ratio of the bank and reduces it.

Keywords


Akhbari, M., Akhbari, M. (2010). Artificail intelligence approach for predicing credit rating of bank customers. Quarterly Journal of Money and economy, 3, 157-182. (in Persian)
Altman, E.I. (1968). Financial ratios, discriminate analysis and the prediction of corporate bankruptcy. Journal of Finance, 4 (23), 589-609.
Angelini, E., Tollo, G. & Roli, A. (2008). A neutral network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4), 733-755.
Arabmazar, A., Roointan, P. (2006). Credit rating factors: A case study of Agricultural Bank of Iran. Journal of Economic Essays, 3(6), 45-80.
(in Persian)
Basel Committee on Banking Supervision (BCBS). (2015). Guidance on credit risk and accounting for expected credit losses. Bank for International Settlements.
Bastos, J. A. (2010). Forecasting bank loans loss-given-default. Journal of Banking & Finance, 34(10), 2510–2517.
Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accountnig Research, 4, 71-111.
Bellalah, M., Zouari, S. & Levyne, O. (2014). The performance of hybrid models in the assessment of default risk. Economic Modelling, 52(PA), 259-265.
Bellotti, T. & Crook, J. (2012). Loss given default models incorporating macroeconomic variables for credit cards. International Journal of Forecasting, 28(1), 171–182.
Cantrell, B. W., McInnis, J. M. & Yust, C. G. (2014). Predicting credit losses: loan fair values versus historical costs. The Accounting Review, 89(1), 147–176.
Central Bank of Iran. (2003). Capital adequacy ratio. Central Bank Instruction.
(in Persian)
Central Bank of Iran. (2006). Loans and credit classification and loss reserves calculation for financial institutes. Central Bank Instruction. (in Persian)
Central Bank of Iran. (2012). Loss reserves calculation for financial institutes. Central Bank Instruction. (in Persian)
Cummings, J.R., Durrani, K.J. (2016). Effect of the Basel Accord capital requirements on the loan-loss provisioning practices of Australian banks, Journal of Banking & Finance, 67(2), 23-36.
Dadmohammadi, D., Ahmadi, A. (2014). Credit rating of bank customers using laterally connected neural network. Quarterly Journal of Money and Economy Management Development, 2 (3), 1-28. (in Persian)
Ebrahimi Kordlor, A. & Mohammadi Shad, Z. (2014). Investigating the relationship between default risk and earning response coefficient (ERC). The Iranian Accounting and Auditing Review, 21(1), 1-18. (in Persian)
Frontczak, R. & Rostek, S. (2015). Modeling loss given default with stochastic collateral. Economic Modelling, 44, 162–170.
Grunert, J. & Weber, M. (2009). Recovery rates of commercial lending: empirical evidence for german companies. Journal of Banking & Finance, 33(3), 505–513.
Gupton, G. M. & Stein, R. M. (2002). Model for predicting loss given default (LGD), Moody’s KMV Company.
Gürtler, M. & Hibbeln, M. (2013). Improvements in loss given default forecasts for bank loans. Journal of Banking & Finance, 37(7), 2354–2366.
Han, C. & Jang, Y. (2013). Effects of debt collection practices on loss given default. Journal of Banking & Finance, 37(1), 21-31.
Hanson, S. & Schuermann, T. (2006), Confidence intervals for probabilities of default. Journal of Banking & Finance, 30(8), 2281–2301.
Hartmann-Wendels, T., Miller, P. & Töws, E. (2014). Loss given default for leasing: Parametric and nonparametric estimations. Journal of Banking & Finance, 40(C), 364–375.
International Accounting Standards Board (IASB). (2003). Accounting Policies, Changes in Accounting Estimates and Errors (AIS7). IFRS Foundation.
International Accounting Standards Board (IASB). (2011). Financial Instruments: Disclosures (IFRS 7). IFRS Foundation.
International Accounting Standards Board (IASB). (2013). Fair Value Measurement (IFRS 13). IFRS Foundation.
International Accounting Standards Board (IASB). (2014), Financial Instruments (IFRS 9). IFRS Foundation.
Khieu, H. D., Mullineaux, D. J. & Yi, H. (2012). The determinants of bank loan recovery rates. Journal of Banking & Finance, 36(4), 923–933.
Knott, S., Richardson, P., Rismanchi, K. & Sen, K. (2014). Understanding the fair value of banks’ loans. Financial Stability Paper, 31, 1-17.
Kothari, J. & Barone, E. (2010). Advanced Financial Accounting: An International Approach. (First edition). Pearson.
Leow, M. & Crook, J. (2014). The stability of survival model parametere stimates for predicting the probability of default: Empirical evidence over the credit crisis. European Journal of Operational Research, 000, 1–8.
Leow, M. (2010). Credit risk models for mortgage loan loss given default. PhD Thesis. University of Southampton, School of management.
Loterman, G., Brown, I., Martens, D., Mues, C. & Baesens, B. (2012). Benchmarking regression algorithms for loss given default modeling. International Journal of Forecasting, 28(1), 161–170.
Masaru, T., Yoichi, I. & Satoshi, M. (2007). Genetic algorithm approach to design covariates of binomial logit model for estimation of default probability. Congress on Evolutionary Computation, 25-28 Sept. Available online, 4344- 4349.
Mehrara, M., Musaee, M., Tasavori, M., Hassanzade, A. (2011). Credit rating of corporate customers of Parsian Bank. Quarterly Journal of Money and Economic Modelling, 2 (3), 121-150. (in Persian)
Min, Q. & Zhao, X. (2011). Comparison of modeling methods for Loss Given Default. Journal of Banking & Finance, 35(11), 2842–2855.
Ohlson, J. (1980). Financial ratios and the probabilistic of prediction of bankruptcy. Journal of Accountnig Research, 18(1), 109-131.
Schuermann, T. (2004). What do we know about loss given default? Wharton Financial Institutions Center, Working Paper, No. 04-01.
Tehrani, R., Fallah Shams, M. (2005). Planning and explaining credit risk models in Iraning banking system. Journal of Shiraz Univarsity in Human and Social Science, 43, 45-60. (in Persian)
Tobback, E., Martens, D., Van Gestel, T. & Baesens, B. (2014). Forecasting loss given default models: Impact of account characteristics and the macroeconomic state. Journal of the Operational Research Society, 65(3), 376-392.
Yao, X., Crook, J. & Galina, A. (2015). Support vector regression for loss given default modeling. European Journal of Operational Research, 240, 528-538.
Yashkir O. & Yashkir Y. (2013). Loss given default modelling: A comparative analysis. Journal of Risk Model Validation, 7(1), 25–59.
Yeh, I. & Lien, C. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473–2480.
Zhang, J. (2011). Modelling examples of Loss Given Default and Probability of Default. Phd Thesis.University of Southampton, School of management.