A new perspective of performance comparison among machine learning algorithms for financial distress prediction

• Published in: Applied soft computing Vol. 83; p. 105663
• Main Authors: Huang, Yu-Pei, Yen, Meng-Feng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2019
• Subjects: Financial distress prediction; GA-fuzzy clustering; HACT; Hybrid DBN-SVM; XGBoost; GA-fuzzy clustering; Financial distress prediction; O16; XGBoost; G32; Hybrid DBN-SVM; HACT; G17; O31
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105663

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 vect...