Forecasting Bank Failure: Base Learners, Ensembles and Hybrid Ensembles

Aykut Ekinci () and Halil İbrahim Erdal ()
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Aykut Ekinci: Development Bank of Turkey
Halil İbrahim Erdal: Turkish Cooperation and Coordination Agency

Computational Economics, 2017, vol. 49, issue 4, No 7, 677-686

Abstract: Abstract The prediction of bankruptcy for financial companies, especially banks, has been extensively researched area and creditors, auditors, stockholders and senior managers are all interested in bank bankruptcy prediction. In this paper, three common machine learning models namely Logistic, J48 and Voted Perceptron are used as the base learners. In addition, an attribute-base ensemble learning method namely Random Subspaces and two instance-base ensemble learning methods namely Bagging and Multi-Boosting are employed to enhance the prediction accuracy of conventional machine learning models for bank failure prediction. The models are grouped in the following families of approaches: (i) conventional machine learning models, (ii) ensemble learning models and (iii) hybrid ensemble learning models. Experimental results indicate a clear outperformance of hybrid ensemble machine learning models over conventional base and ensemble models. These results indicate that hybrid ensemble learning models can be used as a reliable predicting model for bank failures.

Keywords: Financial crisis; Bank failure; Bagging; Hybrid classifier ensembles; Logistic regression; J48; Multi-boosting; Random sub-spaces; Voted perceptron (search for similar items in EconPapers)
JEL-codes: C11 C13 E37 E44 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s10614-016-9623-y

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