Going-concern prediction using hybrid random forests and rough set approach

CCUR > College of Business > Department of Accounting & Graduate Institute of Accounting > periodical articles > Item 987654321/29297

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/29297

Title:	Going-concern prediction using hybrid random forests and rough set approach
Authors:	Yeh, Ching-Chiang Chi, Der-Jang Lin, Yi-Rong
Contributors:	會計學系暨研究所
Keywords:	Going-concern prediction Intellectual capital Random forest Rough set theory
Date:	2014-01-01
Issue Date:	2015-01-28 10:49:34 (UTC+8)
Abstract:	Corporate going-concern opinions are not only useful in predicting bankruptcy but also provide some explanatory power in predicting bankruptcy resolution. The prediction of a firm's ability to remain a going concern is an important and challenging issue that has served as the impetus for many academic studies over the last few decades. Although intellectual capital (IC) is generally acknowledged as the key factor contributing to a corporation's ability to remain a going concern, it has not been considered in early prediction models. The objective of this study is to increase the accuracy of going-concern prediction by using a hybrid random forest (RF) and rough set theory (RST) approach, while adopting IC as a predictive variable. The results show that this proposed hybrid approach has the best classification rate and the lowest occurrence of Types I and II errors, and that IC is indeed valuable for going-concern prediction. (C) 2013 Elsevier Inc. All rights reserved.
Relation:	INFORMATION SCIENCES 卷: 254 頁碼: 98-110
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] periodical articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	418	View/Open

View Licence