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An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio
Abstract
Financial institutions, policy makers and regulatory authorities need to implement stress tests in order to test both resilience and the consequences of adverse shocks. The European Central Bank and the European Banking Authority regularly conduct these tests, whose importance is more and more evident after the financial crisis of 2007-2008. The stress tests’ nonlinear features of variables and scenarios triggered the need of general and robust strategies to perform this task. In this paper we want to introduce an adaptive Neural Network approach to predict the Capital Adequacy Ratio (CAR), which is one of the main ratios monitored to retrieve useful information along many stress test procedures. The Neural Network approach is based on a comparison between feed-forward and recurrent networks, and is run after a meaningful pre-processing operations definition. Results show that our approach is able to successfully predict CAR by using both Neural Networks and recurrent networks.
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S. Park S. Peristiani A., Estrella. Capital ratios as predictors of bank failure.Economic Policy Review, 6:33–52, 02 2000.
V. Siakoulis E. Stavroulakis N. E. Vlachogiannakis A., Petropoulos. Predicting bank insolvencies using machine learning techniques. International Journal of Forecasting, 36(3):1092 – 1113, 2020.
V. V. Acharya, D. Pierret, and S. Steffen. Introducing the “leverage ratio” in assessing the capital adequacy of european banks. ZEW Discussion Und Working Paper, 49(621):460–482, 2016.
T. Adrian, J. Morsink, and L. B. Schumacher. Stress testing at the imf. Technical report, International Monetary Fund, 2020.
Z. Affes and R. Hentati-Kaffel. Forecast bankruptcy using a blend of clustering and mars model: Case of us banks. Annals of Operations Research, 281(1):27–64, 2019.
N. M. Al-Sabbagh. Determinants of capital adequacy ratio in Jordanian banks. PhD thesis, Yarmouk University, 2004.
A. Alfadli and H. Rjoub. The impacts of bank-specific, industry-specific and macroeconomic variables on commercial bank financial performance: evidence from the gulf cooperation council countries. Applied Economics Letters, 27(15):1284–1288, 2020.
F. Audrino, A. Kostrov, and J.P. Ortega. Predicting u.s. bank failures with midas logit models. Journal of Financial and Quantitative Analysis, 54(6):2575–2603, 2019.
A. Bahrammirzaee. A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8):1165–1195, 2010.
H. B. Barlow. Unsupervised learning: introduction. In G. E. Hinton and T. J. Sejnowski, editors, Unsupervised Learning: Foundations of Neural Computation, pages 1–17. Bradford Company Scituate, MA, USA, 1999.
J. R. Barth and G. Caprio. Approaches to bank supervision. Political institutions and financial development, page 156, 2008.
L. Bateni, H. Vakilifard, and F. Asghari. The influential factors on capital adequacy ratio in iranian banks. International Journal of Economics and Finance, 6(11):108–116, 2014.
C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 2005.
K. Bourkhis and M. S. Nabi. Islamic and conventional banks’ soundness during the 2007–2008 financial crisis. Review of Financial Economics, 22(2):68– 77, 2013.
J. A. Chattha and S. Archer. Solvency stress testing of islamic commercial banks: assessing the stability and resilience. Journal of Islamic Accounting and Business Research, 2016.
M. Cˇ iha´k. Introduction to applied stress testing. IMF Working Papers, pages 1–74, 2007.
M. Corazza, D. De March, and G. di Tollo. Design of adaptive elman networks for credit risk assessment. Quantitative Finance, 21(2):323–340,2021.
R. Dakovic, C. Czado, and D. Berg. Bankruptcy prediction in norway: a comparison study. Applied Economics Letters, 17(17):1739–1746, 2010.
G. di Tollo. Reti neurali e rischio di credito: stato dell’arte e analisi sperimentale. Technical Report R-2005-003, Dipartimento di Scienze, University “G. D’Annunzio” Chieti–Pescara, 2005.
P. Dua and H. Kapur. Macro stress testing and resilience assessment of indian banking. Journal of Policy Modeling, 40(2):452–475, 2018.
E.Angelini, G.di Tollo, and A. Roli. A neural net approach for credit-scoring. Quarterly Review of Economics and Finance, 48:733–755, 2008.
LM. Fu. Neural Networks in Computer Intelligence. McGraw-Hill, Inc., USA, 1994.
P. Gai, A. Haldane, and S. Kapadia. Complexity, concentration and contagion. Journal of Monetary Economics, 58(5):453–470, 2011.
N. Gambetta, M. A. Garc´ıa-Benau, and A. Zorio-Grima. Stress test impact and bank risk profile: Evidence from macro stress testing in europe. International Review of Economics & Finance, 61:347–354, 2019.
M. G. Gulaliyev, N. P. Ashurbayli-Huseynova, A. A. Gubadova, B. N. Ahmedov, G. M. Mammadova, and R. T. Jafarova. Stability of the banking sector: deriving stability indicators and stress-testing. Polish Journal of Management Studies, 19, 2019.
L. H.Wang J. P. Yin Chen P. H. Chen H. F. Zhang H. F., Zhang. Performance of the levenberg–marquardt neural network approach in nuclear mass prediction. Journal of Physics G: Nuclear and Particle Physics, 44(4):045110, mar 2017.
A. Hadjixenophontos and C. Christodoulou-Volos. Financial crisis and capital adequacy ratio: A case study for cypriot commercial banks. Journal of Applied Finance and Banking, 8(3):87–109, 2018.
A. Haldane. Constraining discretion in bank regulation. Central Banking at a Crossroads, page 15, 2013.
M. K. Hassan, O. Unsal, and H. E. Tamer. Risk management and capital adequacy in turkish participation and conventional banks: A comparative stress testing analysis. Borsa Istanbul Review, 16(2):72–81, 2016.
JB Heaton, N. G. Polson, and J. H. Witte. Deep learning in finance. arXiv preprint arXiv:1602.06561, 2016.
H. Husna and R. Rahman. Financial distress–detection model for islamic banks. International Journal of Trade, Economics and Finance, pages 158– 163, 01 2012.
A. Jamali. Modeling effects of banking regulations and supervisory practices on capital adequacy state transition in developing countries. Journal of Financial Regulation and Compliance, 2019.
K. Kumar A. Gepp K., Halteh. Financial-distress prediction of islamic banks using tree-based stochastic techniques. Managerial Finance, Special Issue in the Role of Islamic Finance in Mainstream Finance, 08 2017.
R. A. A. Karim. The impact of the basle capital adequacy ratio regulation on the financial and marketing strategies of islamic banks. International Journal of Bank Marketing, 1996.
A. Khashman. Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications, 37(9):6233 – 6239, 2010.
S. B. Kotsiantis. Supervised machine learning: A review of classification techniques. Informatica, 31(3):249–268, 2007.
K. Koˇciˇsov´a and M. Miˇsankov´a. Discriminant analysis as a tool for forecasting company’s financial health. Procedia - Social and Behavioral Sciences,110:1148 – 1157, 2014. The 2-dn International Scientific conference Contemporary Issues in Business, Management and Education 2013“.
N. Laila and F. Widihadnanto. Financial distress prediction using bankometer model on islamic and conventional banks: Evidence from indonesia. International Journal of Economics and Management, 11:169–181, 01 2017.
D. Martin. Early warning of bank failure : A logit regression approach. Journal of Banking & Finance, 1(3):249–276, November 1977.
D. Mayes and H. Stremmel. The effectiveness of capital adequacy measures in predicting bank distress. SUERF, 2014/1, 02 2014.
P. McCullagh and J.A. Nelder. Generalized Linear Models, Second Edition. Chapman and Hall/CRC Monographs on Statistics and Applied Probability Series. Chapman & Hall, 1989.
M. Mehreen, Maran M., S. Ariffin A. Karim, and Amin J. Proposing a multidimensional bankruptcy prediction model: An approach for sustainable islamic banking. Sustainability, 12:3226, 04 2020.
E. Montero, M. C. Riff, and B. Neveu. A beginner’s guide to tuning methods. Appl. Soft Comput., 17:39–51, April 2014.
G. E. Morgan. On the adequacy of bank capital regulation. Journal of Financial and Quantitative Analysis, 19(2):141–162, 1984.
D. M. Nachane and S. Ghosh. Credit rating and bank behaviour in india: Possible implications of the new basel accord. The Singapore Economic Review, 49(01):37–54, 2004.
A. K. NOVOKMET and A. BANOVIC´ . Why do the minimum capital adequacy ratios vary across europe? Journal of Applied Economic Sciences, 11(3):41, 2016.
H. Oloo, M. Wanjiru, and K. Newell-Jones. Female genital mutilation practices in kenya: the role of alternative rites of passage. a case study of kisii and kuria districts. 2011.
T. Loughran B. McDonald P., Gandhi. Using annual report sentiment as a proxy for financial distress in u.s. banks. Journal of Behavioral Finance, 20(4):424–436, 2019.
J. Park, M. Shin, and W. Heo. Estimating the bis capital adequacy ratio for korean banks using machine learning: Predicting by variable selection using random forest algorithms. Risks, 9(2), 2021.
J. Park, M. Shin, and W. Heo. Estimating the bis capital adequacy ratio for korean banks using machine learning: Predicting by variable selection using random forest algorithms. Risks, 9(2):32, 2021.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
A. Petropoulos, V. Siakoulis, K. Panousis, T. Christophides, 2020. A Deep Learning Approach for Dynamic Balance Sheet Stress Testing
V. Ravi and C. Pramodh. Threshold accepting trained principal component neural network and feature subset selection: Application to bankruptcy prediction in banks. Applied Soft Computing, 8(4):1539 – 1548, 2008. Soft Computing for Dynamic Data Mining.
P. Ravi Kumar and V. Ravi. Bankruptcy prediction in banks and firms via statistical and intelligent techniques – a review. European Journal of Operational Research, 180(1):1 – 28, 2007.
B. Robitaille, B. Marcos, M. Veillette, and G. Payre. Modified quasi-newton methods for training neural networks. Computers Chemical Engineering, 20(9):1133–1140, 1996.
R. Rojas. The backpropagation algorithm. In Neural networks, pages 149– 182. Springer, 1996.
R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018.
N. Vunjak, N. Milenkovi´c, J. Andraˇsi´c, and M. Pjani´c. Stress test model for measuring the effects of the economic crisis on the capital adequacy ratio.
D.Worrell. Stressing to breaking point: Interpreting stress test results. 2008.
X. Yan and X. G. Su. Linear Regression Analysis: Theory and Computing. World Scientific Publishing Co., Inc., USA, 2009
DOI: http://dx.doi.org/10.23755/rm.v43i0.841
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