A New Perspective of Performance Comparison among Machine Learning Algorithms for Financial Distress Prediction

33 Pages Posted: 19 Aug 2019

See all articles by Yu-Pei Huang

Yu-Pei Huang

Department of Electronic Engineering, National Quemoy University

Meng-Feng (Stephane) Yen

Department of Accountancy and Graduate Institute of Finance, National Cheng Kung University

Date Written: July 13, 2019

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. 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.

Keywords: HACT, GA-fuzzy clustering, XGBoost, hybrid DBN-SVM, financial distress prediction

JEL Classification: G17, G32, O16, O31

Suggested Citation

Huang, Yu-Pei and Yen, Meng-Feng (Stephane), A New Perspective of Performance Comparison among Machine Learning Algorithms for Financial Distress Prediction (July 13, 2019). Available at SSRN: https://ssrn.com/abstract=3437863 or http://dx.doi.org/10.2139/ssrn.3437863

Yu-Pei Huang

Department of Electronic Engineering, National Quemoy University ( email )

Kinmen
Taiwan

Meng-Feng (Stephane) Yen (Contact Author)

Department of Accountancy and Graduate Institute of Finance, National Cheng Kung University ( email )

1 Univeristy Road
East District
Tainan City, Taiwan 70101
Taiwan
(06) 275-7575 ext 53400 (Phone)
(06) 2744104 (Fax)

HOME PAGE: http://myweb.ncku.edu.tw/~yenmf/

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