Evaluation of empirical attributes for credit risk forecasting from numerical data

  • Published March 31, 2017
  • Author(s)
    Augustinos I. Dimitras , Stelios Papadakis ,
    E-mail:
    Alexandros Garefalakis
  • DOI
    http://dx.doi.org/10.21511/imfi.14(1).2017.01
  • Article Info
    Volume 14 2017, Issue #1, pp. 9-18
  • TO CITE АНОТАЦІЯ
  • Cited by
    5 articles
  • 3546 Views
  • 1425 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric models. Our analysis revealed that building simplified model by using only three out of the thirty five initially selected features one can achieve the same or slightly better forecasting accuracy when compared to the one achieved by the model uses all the initial features. Also, experimentally verified claim that universally accepted criteria can’t be globally used to achieve optimal results is discussed.

view full abstract hide full abstract
    • Fig. 1. Tenfold cross-validation
    • Table 1. Key steps in a bank’s loan decision
    • Table 2. Areas of business analysis
    • Table 3. The selected attributes presented in descending order according to their |r| value
    • D’ Agostino, R. B. (1971). An omnibus test of normality for moderate and large size samples. Biometrika, 58, 341-348.
    • Basel Committee on Banking Supervision. (2006). Observed range of practice in key elements of Advanced Measurement Approaches (AMA).
    • Benos, A., and Papanastasopoulos, G. (2007). Extending the Merton model: A hybrid approach to assessing credit quality. Mathematical and computer modelling, 46, 47-68.
    • Bowman, K. O., and Shenton, L. R. (1975). Omnibus test contours for departures from normality based on b1 and b2, Biometrika, 62, 243-250.
    • Corder, G. W., and Foreman, D. I. (2009). Nonparametric statistics for non-statisticians: a step-by-step approach. John Wiley and Sons.
    • Daniels, K., and Ramirez, G. (2008). Information, credit risk, lender specialization and loan pricing: Evidence from the DIP financing market. Journal of Financial Services Research, 34, 35-59.
    • Diamantaras, K. I., and Kung, S. Y. (1996). Principal component neural networks: theory and applications, John Wiley and Sons, Inc.
    • Doumpos, M., and Zopounidis, C. (2001). Assessing financial risks using a multicriteria sorting procedure: the case of country risk assessment. Omega, 29, 97-109.
    • Duff, A., and Einig, S. (2009). Credit ratings quality: The perceptions of market participants and other interested parties. The British Accounting Review, 41, pp. 141-53.
    • Fernandes, J. (2005). Corporate credit risk modeling: Quantitative rating system and probability of default estimation. Available at SSRN 722941.
    • Garefalakis, A. Dimitras, A. Floros, C., Lemonakis, C. (2016). How Narrative Reporting changed the Business World: Providing a new measurement tool. Corporate Ownership and Control, 13, 317-334.
    • Grimm, L. G., and Yarnold, P. R. (1995) Reading and understanding multivariate statistics. American Psychological Association.
    • Hall, M. A., and Smith, L. A. (1998). Practical feature subset selection for machine learning.
    • Huang, C. L., Chen, M. C., and Wang, C.J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33(4), 847-56.
    • Kohavi, R., and John, G.H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97, 273-324.
    • Kosmidou, K., Pasiouras, F., Tsaklanganos, A. (2007). Domestic and multinational determinants of foreign bank profits: The case of Greek banks operating abroad. Journal of Multinational Financial Management, 17, 1-15.
    • Kung, S. Y., and Diamantaras, K. I. (1991). Neural networks for extracting unsymmetric principal components. Neural Networks for Signal Processing [1991], Proceedings of the 1991 IEEE Workshop (pp. 50-59). IEEE.
    • Pasiouras, F., Gaganis, C., Zopounidis, C. (2006). The impact of bank regulations, supervision, market structure and bank characteristics on individual bank ratings: a cross-country analysis. Review of Quantitative Finance and Accounting, 27(4), 403-438.
    • Pasiouras, F., Tanna, S. (2010). The Prediction of bank acquisition targets with discriminant and logit analyses: methodological issues and empirical evidence Research in International Business and Finance, 24, 39-61.
    • Sieczka, P., and Hołyst, J. A. (2009). Collective firm bankruptcies and phase transition in rating dynamics. The European Physical Journal B-Condensed Matter and Complex Systems, 71(4), 461-466.
    • Smola, A. J., and Schölkopf, B. (1998). Learning with kernels. Citeseer.
    • Sola, J., and Sevilla, J. (1997). Importance of input data normalization for the application of neural networks to complex industrial problems. Nuclear Science, IEEE Transactions on, 44, 1464-1468.
    • Song, F., Guo, Z., and Mei, D. (2010). Feature selection using principal component analysis, 1, 27-30. Presented at the System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2010 International Conference on, IEEE.
    • Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. Routledge.
    • Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.
    • Vapnik, V. (1992). Principles of risk minimization for learning theory. Advances in neural information processing systems, 831-838.
    • Vapnik, V. (2000). The nature of statistical learning theory. Springer Science and Business Media.
Related Articles

  • The impact of credit risk and macroeconomic factors on profitability: the case of the ASEAN banks

    Myra V. De LeonORCID doi: http://dx.doi.org/10.21511/bbs.15(1).2020.03
    Banks and Bank Systems Volume 15, 2020         Issue #1         pp. 21-29 Views: 1878 Downloads: 651 TO CITE АНОТАЦІЯ

    This study investigates the effect of credit risk and macroeconomic factors on profitability of 20 ASEAN banks, particularly from Indonesia, Malaysia, Thailand and Philippines, covering the period of 2012 to 2017. The unbalanced panel data were tested for heteroscedasticity and normality. A fixed effects model and a random effects model were utilized followed by simple ordinary least squares (OLS) regression. The obtained results show that credit risk and GDP growth negatively affect Return on Equity (ROE) at 5% level of significance. The inflation rate increases ROE by 0.323%. In terms of influence, inflation has the highest impact on ROE followed by GDP growth and credit risk. Credit risk and GDP growth negatively affect Return on Assets (ROA) at 5% level of significance. ROA was also influenced by an increase in inflation rate. Therefore, this study will help banks and bank managers, depositors, investors, policy makers and governments to identify factors affecting bank profitability.

  • Factors affecting non-performing loans in commercial banks of selected West African countries

    Thabiso Sthembiso MsomiORCID doi: http://dx.doi.org/10.21511/bbs.17(1).2022.01
    Banks and Bank Systems Volume 17, 2022         Issue #1         pp. 1-12 Views: 1819 Downloads: 1236 TO CITE АНОТАЦІЯ

    This paper examines the macro-economic and bank-specific factors affecting non-performing loans in commercial banks. Using 47 listed commercial banks from six countries, namely 19 banks from Nigeria, 14 banks from Benin, 3 banks from Burkina Faso, 3 banks from Gambia, 3 banks from Guinea, and 5 banks from Liberia for the period 2008 to 2019, fixed and random effect model was used. The Hausman test favored the selection of fixed effect model, and it was found from the estimation that the liquidity ratio, capital adequacy ratio and inflation rate significantly affect non-performing loans. As a result, it is advised that banks depend not only on their ability to achieve the capital adequacy ratio, but also guarantee that loans are thoroughly scrutinized before being issued to beneficiaries. Bank managers should guarantee that banking staff is not simply awarding loans to secure their jobs by accumulating deposits from consumers at the price of the bank’s long-term stake. In addition, the economies of West Africa should keep their inflation rates low so that repayment of loans on time is cheap and realistic.

    Acknowledgment
    I would like to appreciate Fezile Nonjabulo Gcwabaza for love and support throughout this research project.

  • Factors may drive the commercial banks lending: evidence from Jordan

    Ayman Mansour Khalaf Alkhazaleh doi: http://dx.doi.org/10.21511/bbs.12(2).2017.03
    Banks and Bank Systems Volume 12, 2017         Issue #2         pp. 31-38 Views: 1657 Downloads: 2095 TO CITE АНОТАЦІЯ

    In an attempt to shed more light on the behavior of lending in banks, especially in the environment of developing countries, this study aims at explaining the impact of some factors proposed as determinants of bank lending in Jordanian commercial banks by benefiting from the financial reports of thirteen banks during the period 2010-2016. The study, in order to achieve the objectives and to test the main hypotheses has adopted Ordinary least square model (OLS). The most important results of the study are a statistically significant adverse effect of both credit risk and liquidity on bank lending, while there is a significant positive effect of the return on assets, size of the bank measured by assets, inflation, money supply and growth in gross domestic product in determining the level of lending. In addition, the study does not show a significant statistical effect between investments, the volume of deposits and bank lending in the same time frame. The review points out that because of the negative impact of liquidity and credit risk factors, commercial banks need to focus more on reducing their impact because presence of this impact at the end will decrease the ability of these banks to provide loans and stay in the banking market.

Download Preview
Close
Investment Management and Financial Innovations
Investment Management and Financial Innovations