Ensemble Learning or Deep Learning? Application to Default Risk Analysis

Shigeyuki Hamori, Minami Kawai, Takahiro Kume, Yuji Murakami and Chikara Watanabe
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Minami Kawai: Department of Economics, Kobe University, Kobe 657-8501, Japan
Takahiro Kume: Department of Economics, Kobe University, Kobe 657-8501, Japan
Yuji Murakami: Department of Economics, Kobe University, Kobe 657-8501, Japan
Chikara Watanabe: Department of Economics, Kobe University, Kobe 657-8501, Japan

JRFM, 2018, vol. 11, issue 1, 1-14

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.

Keywords: credit risk; ensemble learning; deep learning; bagging; random forest; boosting; deep neural network (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Working Paper: Ensemble Learning or Deep Learning? Application to Default Risk Analysis (2018)
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