Comparative Temporal Analysis of SVM-Based Machine Learning Techniques for Bank Risk Assessment

Research Article
Open access

Comparative Temporal Analysis of SVM-Based Machine Learning Techniques for Bank Risk Assessment

Tingxu Liu 1 , Yubo Ma 2* , Juntong Yang 3 , Zhuowei Ye 4
  • 1 School of Gifted Young, University of Science and Technology of China, Hefei, 230026, China    
  • 2 2School of Economics, University of Nottingham, Ningbo, 315000, China    
  • 3 3Trinity Hall, University of Cambridge, Cambridge, CB2 1TJ, United Kingdom    
  • 4 School of Computer Science and Engineering, Central South University, Changsha, 410083, China    
  • *corresponding author biyym24@nottingham.edu.cn
AEMPS Vol.97
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-83558-505-4
ISBN (Online): 978-1-83558-506-1

Abstract

This paper introduces an innovative approach that utilizes support vector machines (SVMs) to predict the bankruptcy of financial institutions within the United States. The study aims to identify influential factors that contributed to bankruptcy during two significant peri-ods: the 2008 financial crisis and post-2013. The goal is to highlight both shared and distinc-tive characteristics between these time frames. The proposed method incorporates a meticu-lous feature selection procedure to identify the most critical variables for assessing a bank’s financial stability. Subsequently, the SVM model is fed with data containing these key vari-ables from various banks, initiating both the training and testing phases. Specifically, two SVM models were trained: one utilizing a linear kernel, and the other employing a non-linear kernel. The objective was to assess their effectiveness in distinguishing between solvent and insolvent banks. Moreover, a neural network model was developed and subjected to a com-parative analysis alongside the aforementioned SVM models, all with the aim of identifying the optimal method for bankruptcy prediction. The training dataset comprised data from the ten quarters preceding bank failures post-2013, as well as the eight quarters leading up to bank failures in 2010, during 2008 financial crisis. The SVM models were implemented us-ing Scikit-Learn, while the neural network model was trained using PyTorch. Through this comprehensive approach, the paper contributes to the advancement of predictive methodolo-gies for identifying potential financial institution bankruptcies.

Keywords:

Machine learning; Support Vector Machine; Bank failures; Stress testing; Fore-casting

Liu,T.;Ma,Y.;Yang,J.;Ye,Z. (2024). Comparative Temporal Analysis of SVM-Based Machine Learning Techniques for Bank Risk Assessment. Advances in Economics, Management and Political Sciences,97,63-88.
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References

[1]. Ahn, S. and Choi, W. (2009) ’The role of bank monitoring in corporate governance: Evidence from borrowers’ earnings management behavior,’ Journal of banking & finance, 33(2), pp. 425–434.

[2]. Le, H.H. and Viviani, J.-L. (2018) ’Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios,’ Research in international business and finance, 44, pp. 16–25.

[3]. Gogas, P., Papadimitriou, T. and Agrapetidou, A. (2018) ’Forecasting bank failures and stress testing: A machine learning approach,’ International journal of forecasting, 34(3), pp. 440–455.

[4]. Greg Feldberg and Carey (2023) The 2023 Banking Crisis: Lessons about Bail-in, https: //som.yale.edu/story.

[5]. Boyacioglu, M.A., Kara, Y. and Baykan, Ömer K. (2009) ’Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey,’ Expert systems with applications, 36(2), pp. 3355–3366.

[6]. Green, M.J., Medley, G.F. and Browne, W.J. (2009) ’Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression.’ Stoltzfus, J.C. (2011) ’Logistic Regression: A Brief Primer,’ Academic emergency medicine, 18(10), pp. 1099–1104.

[7]. Montesi, G. and Papiro, G. (2018) ‘Bank Stress Testing: A Stochastic Simulation Framework to Assess Banks’ Financial Fragility’, Risks (Basel), 6(3), p. 82. doi:10.3390/risks6030082.

[8]. Awaworyi Churchill, S. (2019) ‘The macroeconomy and microfinance outreach: a panel data analysis’, Applied economics, 51(21), pp. 2266–2274. doi:10.1080/00036846.2018.1540857.

[9]. Katsafados, A.G. et al. (2023) ‘Machine learning in bank merger prediction: A text-based ap-proach’, European journal of operational research [Preprint]. doi:10.1016/j.ejor.2023.07.039.

[10]. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297.

[11]. Domingos, P. (1999). "Metacost: A General Method for Making Classifiers Cost-sensitive." In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155–164. San Diego, CA: ACM Press.

[12]. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Chapter 5: Credibility: Evaluating What’s Been Learned. Morgan Kaufmann.

[13]. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Jour-nal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. DOI: 10.1111/j.1467-9868.2005.00503.x

[14]. Hoerl, A. E., Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12, 55-67.

[15]. Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.

[16]. Hsu, C.-W., Lin, C.-J. (2002). A Comparison of Methods for Multi-class Support Vector Ma-chines. IEEE Transactions on Neural Networks, 13(2), 415-425.

[17]. Iturriaga, F. J. L., Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: a study of US commercial banks. Expert Systems with Applications, 42(6), 2857–2869

[18]. Erkens, D.H., Hung, M. and Matos, P. (2012) ‘Corporate governance in the 2007–2008 finan-cial crisis: Evidence from financial institutions worldwide,‘ Journal of corporate finance (Ams-terdam, Netherlands), 18(2), pp. 389–411.

[19]. Ross, E.J., Shibut, L. (2021) ‘Loss Given Default, Loan Seasoning and Financial Fragility: Evidence from Commercial Real Estate Loans at Failed Banks,’ The journal of real estate finance and economics, 63(4), pp. 630–661.

Cite this article

Liu,T.;Ma,Y.;Yang,J.;Ye,Z. (2024). Comparative Temporal Analysis of SVM-Based Machine Learning Techniques for Bank Risk Assessment. Advances in Economics, Management and Political Sciences,97,63-88.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

ISBN：978-1-83558-505-4(Print) / 978-1-83558-506-1(Online)
Editor：Javier Cifuentes-Faura
Conference website: https://2023.icftba.org/
Conference date: 8 November 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.97
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Ahn, S. and Choi, W. (2009) ’The role of bank monitoring in corporate governance: Evidence from borrowers’ earnings management behavior,’ Journal of banking & finance, 33(2), pp. 425–434.

[2]. Le, H.H. and Viviani, J.-L. (2018) ’Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios,’ Research in international business and finance, 44, pp. 16–25.

[3]. Gogas, P., Papadimitriou, T. and Agrapetidou, A. (2018) ’Forecasting bank failures and stress testing: A machine learning approach,’ International journal of forecasting, 34(3), pp. 440–455.

[4]. Greg Feldberg and Carey (2023) The 2023 Banking Crisis: Lessons about Bail-in, https: //som.yale.edu/story.

[5]. Boyacioglu, M.A., Kara, Y. and Baykan, Ömer K. (2009) ’Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey,’ Expert systems with applications, 36(2), pp. 3355–3366.

[6]. Green, M.J., Medley, G.F. and Browne, W.J. (2009) ’Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression.’ Stoltzfus, J.C. (2011) ’Logistic Regression: A Brief Primer,’ Academic emergency medicine, 18(10), pp. 1099–1104.

[7]. Montesi, G. and Papiro, G. (2018) ‘Bank Stress Testing: A Stochastic Simulation Framework to Assess Banks’ Financial Fragility’, Risks (Basel), 6(3), p. 82. doi:10.3390/risks6030082.

[8]. Awaworyi Churchill, S. (2019) ‘The macroeconomy and microfinance outreach: a panel data analysis’, Applied economics, 51(21), pp. 2266–2274. doi:10.1080/00036846.2018.1540857.

[9]. Katsafados, A.G. et al. (2023) ‘Machine learning in bank merger prediction: A text-based ap-proach’, European journal of operational research [Preprint]. doi:10.1016/j.ejor.2023.07.039.

[10]. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297.

[11]. Domingos, P. (1999). "Metacost: A General Method for Making Classifiers Cost-sensitive." In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155–164. San Diego, CA: ACM Press.

[12]. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Chapter 5: Credibility: Evaluating What’s Been Learned. Morgan Kaufmann.

[13]. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Jour-nal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. DOI: 10.1111/j.1467-9868.2005.00503.x

[14]. Hoerl, A. E., Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12, 55-67.

[15]. Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.

[16]. Hsu, C.-W., Lin, C.-J. (2002). A Comparison of Methods for Multi-class Support Vector Ma-chines. IEEE Transactions on Neural Networks, 13(2), 415-425.

[17]. Iturriaga, F. J. L., Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: a study of US commercial banks. Expert Systems with Applications, 42(6), 2857–2869

[18]. Erkens, D.H., Hung, M. and Matos, P. (2012) ‘Corporate governance in the 2007–2008 finan-cial crisis: Evidence from financial institutions worldwide,‘ Journal of corporate finance (Ams-terdam, Netherlands), 18(2), pp. 389–411.

[19]. Ross, E.J., Shibut, L. (2021) ‘Loss Given Default, Loan Seasoning and Financial Fragility: Evidence from Commercial Real Estate Loans at Failed Banks,’ The journal of real estate finance and economics, 63(4), pp. 630–661.

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