Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques

Yeung-Ja James Goo; Der-Jang Chi; Zong-De Shen

doi:10.1186/s40064-016-2186-5

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques

Springerplus. 2016 Apr 27:5:539. doi: 10.1186/s40064-016-2186-5. eCollection 2016.

Authors

Yeung-Ja James Goo¹, Der-Jang Chi², Zong-De Shen¹

Affiliations

¹ Department of Business Administration, National Taipei University, No. 67, Section 3, Ming-Shen East Road, Taipei City, 10478 Taiwan.
² Department of Accounting, Chinese Culture University, No. 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei City, 11114 Taiwan.

Abstract

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

Keywords: Classification and regression tree (CART); Data mining; Going concern prediction; Least absolute shrinkage and selection operator (LASSO); Neural network (NN); Support vector machine (SVM).