Abstract
Models for financial distress predictions of banks are increasingly important tools used as early warning signals for the whole banking systems. In this study, a model based on random subspace method is proposed to predict investment/non-investment rating grades of U.S. banks. We show that support vector machines can be effectively used as base learners in the meta-learning model. We argue that both financial and non-financial (sentiment) information are important categories of determinants in financial distress prediction. We show that this is true for both banks and other companies.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Verikas, A., Kalsyte, Z., Bacauskiene, M., Gelzinis, A.: Hybrid and Ensemble-based Soft Computing Techniques in Bankruptcy Prediction: A Survey. Soft Computing 14(9), 995–1010 (2010)
Hájek, P., Olej, V.: Predicting Firms’ Credit Ratings Using Ensembles of Artificial Immune Systems and Machine Learning – An Over-Sampling Approach. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) AIAI 2014. IFIP AICT, vol. 436, pp. 29–38. Springer, Heidelberg (2014)
Heo, J., Yang, J.Y.: AdaBoost based Bankruptcy Forecasting of Korean Construction Companies. Applied Soft Computing 24, 494–499 (2014)
Alfaro, E., García, N., Gámez, M., Elizondo, D.: Bankruptcy Forecasting: An Empirical Comparison of AdaBoost and Neural Networks. Decision Support Systems 45(1), 110–122 (2008)
Kim, M.J., Kang, D.K.: Ensemble with Neural Networks for Bankruptcy Prediction. Expert Systems with Applications 37(4), 3373–3379 (2010)
Shin, S.W., Lee, K.C., Kilic, S.B.: Ensemble Prediction of Commercial Bank Failure Through Diversification of Input Features. In: Sattar, A., Kang, B.-H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 887–896. Springer, Heidelberg (2006)
Hajek, P., Olej, V.: Credit Rating Modelling by Kernel-Based Approaches with Supervised and Semi-Supervised Learning. Neural Computing and Applications 20(6), 761–773 (2011)
Hajek, P.: Municipal Credit Rating Modelling by Neural Networks. Decision Support Systems 51(1), 108–118 (2011)
Shie, F.S., Chen, M.Y., Liu, Y.S.: Prediction of Corporate Financial Distress: An Application of the America Banking Industry. Neural Computing and Applications 21(7), 1687–1696 (2012)
Berger, A.N., Bouwman, C.H.S.: Bank Liquidity Creation. Review of Financial Studies 22, 3779–3837 (2009)
de Haan, L., van den End, J.W.: Bank Liquidity, the Maturity Ladder, and Regulation. Journal of Banking & Finance 37, 3930–3950 (2013)
Kliger, D., Levy, O.: Mood-Induced Variation in Risk Preferences. Journal of Economic Behavior and Organization 52(4), 573–584 (2003)
Lu, H.M., Tsai, F.T., Chen, H., Hung, M.W., Li, S.H.: Credit Rating Change Modeling using News and Financial Ratios. ACM Transactions on Management Information Systems 3(3), 14 (2012)
Hájek, P., Olej, V.: Evaluating Sentiment in Annual Reports for Financial Distress Prediction Using Neural Networks and Support Vector Machines. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013, Part II. CCIS, vol. 384, pp. 1–10. Springer, Heidelberg (2013)
Loughran, T., McDonald, B.: When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance 66(1), 35–65 (2011)
Chauhan, N., Ravi, V., Karthik Chandra, D.: Differential Evolution Trained Wavelet Neural Networks: Application to Bankruptcy Prediction in Banks. Expert Systems with Applications 36(4), 7659–7665 (2009)
Ravi, V., Pramodh, C.: 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)
Chen, Y.S.: Classifying Credit Ratings for Asian Banks using Integrating Feature Selection and the CPDA-based Rough Sets Approach. Knowledge-Based Systems 26, 259–270 (2012)
Orsenigo, C., Vercellis, C.: Linear versus Nonlinear Dimensionality Reduction for Banks’ Credit Rating Prediction. Knowledge-Based Systems 47, 14–22 (2013)
Gogas, P., Papadimitriou, T., Agrapetidou, A.: Forecasting Bank Credit Ratings. The Journal of Risk Finance 15(2), 195–209 (2014)
Bellotti, T., Matousek, R., Stewart, C.: A Note Comparing Support Vector Machines and Ordered Choice Models’ Predictions of International Banks’ Ratings. Decision Support Systems 51(3), 682–687 (2011)
Demyanyk, Y., Hasan, I.: Financial Crises and Bank Failures: A Review of Prediction Methods. Omega 38(5), 315–324 (2010)
Ravi Kumar, P., Ravi, V.: Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques – A Review. European Journal of Operational Research 180(1), 1–28 (2007)
DeYoung, R., Flannery, M.J., Lang, W.W., Sorescu, S.M.: The Information Content of Bank Exam Ratings and Subordinated Debt Prices. Journal of Money, Credit and Banking 33(4), 900–925 (2001)
Hajek, P., Olej, V.: Comparing Corporate Financial Performance and Qualitative Information from Annual Reports using Self-organizing Maps. In: 10th International Conference on Natural Computation (ICNC 2014), Xiamen, China, pp. 93–98 (2014)
Hajek, P., Olej, V., Myskova, R.: Forecasting Corporate Financial Performance using Sentiment in Annual Reports for Stakeholders’ Decision-Making. Technological and Economic Development of Economy (2014) (in press), doi:10.3846/20294913.2014.979456
Webb, G.I.: MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning 40(2) (2000)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: 13th Int. Conf. on Machine Learning, San Francisco, CA, pp. 148–156 (1996)
Ting, K.M., Witten, I.H.: Stacking Bagged and Dagged Models. In: 14th Int. Conf. on Machine Learning, San Francisco, CA, pp. 367–375 (1997)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation Forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)
Hajek, P., Michalak, K.: Feature Selection in Corporate Credit Rating Prediction. Knowledge-Based Systems 51, 72–84 (2013)
Platt, J.C.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1998)
Kearney, C., Liu, S.: Textual Sentiment in Finance: A Survey of Methods and Models. International Review of Financial Analysis 33, 171–185 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hájek, P., Olej, V., Myšková, R. (2015). Predicting Financial Distress of Banks Using Random Subspace Ensembles of Support Vector Machines. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Perspectives and Applications. Advances in Intelligent Systems and Computing, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-319-18476-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-18476-0_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18475-3
Online ISBN: 978-3-319-18476-0
eBook Packages: EngineeringEngineering (R0)