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J. Risk Financial Manag. 2018, 11(1), 12; https://doi.org/10.3390/jrfm11010012

Ensemble Learning or Deep Learning? Application to Default Risk Analysis

1
Graduate School of Economics, Kobe University, Kobe 657-8501, Japan
2
Department of Economics, Kobe University, Kobe 657-8501, Japan
*
Author to whom correspondence should be addressed.
Received: 19 January 2018 / Revised: 24 February 2018 / Accepted: 28 February 2018 / Published: 5 March 2018
(This article belongs to the Special Issue Empirical Finance)
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Abstract

Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout. View Full-Text
Keywords: credit risk; ensemble learning; deep learning; bagging; random forest; boosting; deep neural network credit risk; ensemble learning; deep learning; bagging; random forest; boosting; deep neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Hamori, S.; Kawai, M.; Kume, T.; Murakami, Y.; Watanabe, C. Ensemble Learning or Deep Learning? Application to Default Risk Analysis. J. Risk Financial Manag. 2018, 11, 12.

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