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

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Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/35922

Title:	Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques
Authors:	Goo, YJJ (Goo, Yeung-Ja James) Chi, DJ (Chi, Der-Jang) Shen, ZD (Shen, Zong-De)
Contributors:	會計系
Keywords:	Going concern prediction Least absolute shrinkage and selection operator (LASSO);Data mining Neural network (NN) Classification and regression tree (CART) Support vector machine (SVM)
Date:	2016-05
Issue Date:	2017-04-14 13:06:16 (UTC+8)
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 %).
Relation:	SPRINGERPLUS 卷: 5 文獻號碼: 539
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] periodical articles

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