Ensemble Learning or Deep Learning? Application to Default Risk Analysis

被引:52
作者
Hamori, Shigeyuki [1 ]
Kawai, Minami [2 ]
Kume, Takahiro [2 ]
Murakami, Yuji [2 ]
Watanabe, Chikara [2 ]
机构
[1] Kobe Univ, Grad Sch Econ, Kobe, Hyogo 6578501, Japan
[2] Kobe Univ, Dept Econ, Kobe, Hyogo 6578501, Japan
关键词
credit risk; ensemble learning; deep learning; bagging; random forest; boosting; deep neural network;
D O I
10.3390/jrfm11010012
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
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页数:14
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