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.
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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
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DOI: https://doi.org/10.1007/978-3-319-18476-0_14
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