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

an article by Yu-Pei Huang (National Cheng Kung University, Taiwan; National Quemoy University, Taiwan) and Meng-Feng Yen (National Cheng Kung University, Taiwan) published in Applied Soft Computing Volume 83 (October 2019)

Highlights

• This paper reviewed the pros and cons of recent literature on various MLmodels for FDP.
• This paper compared the performance of six ML-based approaches using real-life data.
• Among the four supervised models, the XGBoost algorithm provided the most accurate FD prediction.
• The hybrid DBN-SVM model gave betterforecasts than both the SVM and the classifier DBN models.

Abstract

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.

our 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.

JEL classification: G17, G32, O16, O31

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