A Novel Hybrid Regression Model for Banking Loss Estimation

Yıl 2024, , 91 - 105, 27.06.2024

Pınar Karadayı Ataş

https://doi.org/10.33399/biibfad.1391666

Öz

Given the critical need to identify financial risks in the banking sector early, this study presents a novel approach that uses historical financial ratios from the FDIC database to predict bank failures in the United States. Accurate estimation of potential losses is essential for risk management and decision-making procedures. We present a novel hybrid approach to loss estimation in the context of bank failures in this study. ElasticNet regression and relevant data extraction techniques are combined in our method to improve prediction accuracy. We conducted thorough experiments and evaluated our hybrid approach's performance against that of conventional regression techniques. With a remarkably low Mean Squared Error (MSE) of 0.001, a significantly high R-squared value of 0.98, and an Explained Variance Score of 0.95, our proposed model demonstrates superior performance compared to existing methodologies. The accuracy of our method is further demonstrated by the Mean Absolute Error (MAE) of 1200 units. Our results highlight the potential of our hybrid approach to transform loss estimation in the banking and finance domain, offering superior predictive capabilities and more accurate loss estimations.

Anahtar Kelimeler

Financial Risk Analysis, Financial Stability Assessment, Bank Risk Management, Machine Learning.

Bankacılık Zarar Tahmini için Yeni Bir Hibrit Regresyon Modeli

Öz

Bankacılık sektöründeki finansal risklerin erken dönemde belirlenmesine yönelik kritik ihtiyaç göz önüne alındığında, bu çalışma, Amerika Birleşik Devletleri'ndeki banka başarısızlıklarını tahmin etmek için FDIC veri tabanındaki tarihsel finansal oranları kullanan yeni bir yaklaşım sunmaktadır. Potansiyel kayıpların tahmini önemlidir. Bu çalışmada banka iflasları bağlamında zarar tahminine yönelik yeni bir hibrit yaklaşım sunuyoruz. Tahmin doğruluğunu artırmak için ElasticNet regresyon ve ilgili veri çıkarma teknikleri önerdiğimiz yöntemde birleştirilmiştir. Önerilen hibrit yaklaşımın performansı kapsamlı deneyler yapılarak geleneksel regresyon tekniklerine göre değerlendirilmiştir. 0,001'lik son derece düşük Ortalama Kare Hatası (MSE), 0,98'lik oldukça yüksek R-kare değeri ve 0,95'lik açıklanan varyans skoru ile önerdiğimiz model mevcut metodolojilere kıyasla üstün performans sergilemektedir. Yöntemimizin doğruluğu 1200 birimlik ortalama mutlak hata (MAE) ile gösterilmektedir. Sonuçlarımız, üstün tahmin yetenekleri ve daha doğru kayıp tahminleri sunan, bankacılık ve finans alanında zarar tahminini dönüştürmeye yönelik hibrit yaklaşımımızın potansiyelini vurgulamaktadır.

Anahtar Kelimeler

Finansal Risk Analizi, Finansal İstikrar Değerlendirmesi, Banka Risk Yönetimi, Makine Öğrenimi.

Kaynakça

• Aguilar-Rivera, R., Valenzuela-Rendón, M., & Rodríguez-Ortiz, J. J. (2015). Genetic algorithms and Darwinian approaches in financial applications: A survey. Expert Systems with Applications, 42(21), 7684-7697.
• Ahmad, M. W., Akram, M. U., Ahmad, R., Hameed, K., & Hassan, A. (2022). Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights. ISA Transactions, 129, 355-371. doi: 10.1016/j.isatra.2022.01.014.
• Alzayed, N., Eskandari, R., & Yazdifar, H. (2023). Bank failure prediction: Corporate governance and financial indicators. Review of Quantitative Finance and Accounting, 61(2), 601-631. doi: 10.1007/s11156-023-01158-z.
• Anand, M., Velu, A., & Whig, P. (2022). Prediction of loan behaviour with machine learning models for secure banking. Journal of Computer Science and Engineering (JCSE), 3(1), 1-13. doi: 10.36596/jcse.v3i1.237.
• Awad, M., & Khanna, R. (2015). Support vector regression. In Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers (pp. 67-80).
• Borup, D., Christensen, B. J., Mühlbach, N. S., & Nielsen, M. S. (2023). Targeting predictors in random forest regression. International Journal of Forecasting, 39(2), 841-868.
• Carmona, P., Dwekat, A., & Mardawi, Z. (2022). No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure. Research in International Business and Finance, 61, 101649. doi: 10.1016/j.ribaf.2022.101649.
• Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1-16. doi: 10.1016/j.ejor.2022.04.027.
• Emmert-Streib, F., & Dehmer, M. (2019). Evaluation of regression models: Model assessment, model selection and generalization error. Machine Learning and Knowledge Extraction, 1(1), 521-551.
• Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., Qian, J., & Yang, J. (2023). Glmnet: Lasso and elastic-net regularized generalized linear models. Astrophysics Source Code Library, ascl-2308.
• Hafeez, B., Li, X., Kabir, M. H., & Tripe, D. (2022). Measuring bank risk: Forward-looking z-score. International Review of Financial Analysis, 80, 102039. doi: 10.1016/j.irfa.2022.102039.
• Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453-497. Heitz, A. R. (2023). Failed bank loss-sharing with the FDIC.
• Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14(1), 1-24.
• James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Linear regression. In An Introduction to Statistical Learning: With Applications in Python (pp. 69-134). Springer.
• Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659.
• Le, H. H., Viviani, J. L., & Fauzi, F. (2023). Why do banks fail? An investigation via text mining. Cogent Economics & Finance, 11(2), 2251272.
• McAvaney, B. J., Covey, C., Joussaume, S., Kattsov, V., Kitoh, A., Ogana, W., Pitman, A. J., Weaver, A. J., Wood, R. A., & Zhao, Z. C. (2001). Model evaluation. In Climate Change 2001: The scientific basis. Contribution of WG1 to the Third Assessment Report of the IPCC (TAR) (pp. 471-523). Cambridge University Press.
• Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons. Nasir, I. M., Raza, M., Ulyah, S. M., Shah, J. H., Fitriyani, N. L., & Syafrudin, M. (2023). ENGA: Elastic net-based genetic algorithm for human action recognition. Expert Systems with Applications, 227, 120311.
• Nazareth, N., & Reddy, Y. Y. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 119640.
• Otchere, D. A., Ganat, T. O. A., Ojero, J. O., Tackie-Otoo, B. N., & Taki, M. Y. (2022). Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. Journal of Petroleum Science and Engineering, 208, 109244.
• Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384.
• Pulakkazhy, S., & Balan, R. S. (2013). Data mining in banking and its applications-a review. Journal of Computer Science, 9(10), 1252.
• Shoar, S., Chileshe, N., & Edwards, J. D. (2022). Machine learning-aided engineering services’ cost overruns prediction in high-rise residential building projects: Application of random forest regression. Journal of Building Engineering, 50, 104102.
• Sipper, M., & Moore, J. H. (2022). AddGBoost: A gradient boosting-style algorithm based on strong learners. Machine Learning with Applications, 7, 100243.
• Su, X., Yan, X., & Tsai, C. L. (2012). Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3), 275-294.
• Veganzones, D., Séverin, E., & Chlibi, S. (2023). Influence of earnings management on forecasting corporate failure. International Journal of Forecasting, 39(1), 123-143. doi: 10.1016/j.ijforecast.2021.09.006.
• Wang, B., Liu, J., Alassafi, M. O., Alsaadi, F. E., Jahanshahi, H., & Bekiros, S. (2022). Intelligent parameter identification and prediction of variable time fractional derivative and application in a symmetric chaotic financial system. Chaos, Solitons & Fractals, 154, 111590. doi: 10.1016/j.chaos.2021.111590.
• Zhang, F., & O’Donnell, L. J. (2020). Support vector regression. In Machine learning (pp. 123-140). Elsevier.
• Zhao, K., Coco, G., Gong, Z., Darby, S. E., Lanzoni, S., Xu, F., Zhang, K., & Townend, I. (2022). A review on bank retreat: Mechanisms, observations, and modeling. Reviews of Geophysics, 60(2), e2021RG000761. doi: 10.1029/2021RG000761.
• Zou, Y., Gao, C., & Gao, H. (2022). Business failure prediction based on a cost-sensitive extreme gradient boosting machine. IEEE Access, 10, 42623-42639. doi: 10.1109/ACCESS.2022.3168857.