Ensemble Learning or Deep Learning? Application to Default Risk Analysis

Shigeyuki Hamori, Minami Kawai (), Takahiro Kume (), Yuji Murakami () and Chikara Watanabe ()
Additional contact information
Minami Kawai: Department of Economics, Kobe University
Takahiro Kume: Department of Economics, Kobe University
Yuji Murakami: Department of Economics, Kobe University
Chikara Watanabe: Department of Economics, Kobe University

No 1802, Discussion Papers from Graduate School of Economics, Kobe University

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 increasing important. This study analyzed default payment data from Taiwan and compared 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 indicate that Boosting has a high prediction accuracy, whereas that of Bagging and Random Forest is relatively low. They also indicate that the prediction accuracy and classification performance of Boosting is better than that of deep neural networks, Bagging, and Random Forest.

Keywords: credit risk; ensemble learning; deep learning; bagging; random forest; boosting; deep neural network. (search for similar items in EconPapers)
Pages: 22 pages
Date: 2018-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
References: View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Journal Article: Ensemble Learning or Deep Learning? Application to Default Risk Analysis (2018)
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