Journal
APPLIED SOFT COMPUTING
Volume 83, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105663
Keywords
HACT; GA-fuzzy clustering; XGBoost; Hybrid DBN-SVM; Financial distress prediction
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Funding
- Center for Innovative FinTech Business Models, National Cheng Kung University, Taiwan (ROC)
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We set out in this study to review a vast amount of recent literature on machine learning (ML) approaches to predicting financial distress (FD), including supervised, unsupervised and hybrid supervised-unsupervised learning algorithms. Four supervised ML models including the traditional support vector machine (SVM), recently developed hybrid associative memory with translation (HACT), hybrid GA-fuzzy clustering and extreme gradient boosting (XGBoost) were compared in prediction performance to the unsupervised classifier deep belief network (DBN) and the hybrid DBN-SVM model, whereby a total of sixteen financial variables were selected from the financial statements of the publicly-listed Taiwanese firms as inputs to the six approaches. Our empirical findings, covering the 2010-2016 sample period, demonstrated that among the four supervised algorithms, the XGBoost provided the most accurate FD prediction. Moreover, the hybrid DBN-SVM model was able to generate more accurate forecasts than the use of either the SVM or the classifier DBN in isolation. (C) 2019 Elsevier B.V. All rights reserved.
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