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

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 ne...

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Bibliographic Details
Published in:SpringerPlus 2016-04, Vol.5 (1), p.539-539, Article 539
Main Authors: Goo, Yeung-Ja James, Chi, Der-Jang, Shen, Zong-De
Format: Article
Language:English
Subjects:
Business and Economics
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
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Summary: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 %).
ISSN:2193-1801
2193-1801
DOI:10.1186/s40064-016-2186-5