Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree

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题名:	Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree
作者:	Chen, Fu-Hsiang Chi, Der-Jang Wang, Yi-Cheng
贡献者:	Dept Accounting
关键词:	Data mining Bayesian network (EN) Back propagation neural network (BPN) Principal component analysis (PCA) C5.0 decision tree Accrual earnings management
日期:	2015-04
上传时间:	2015-10-30 13:31:19 (UTC+8)
摘要:	The characteristic of long value chain, high-risk, high cost of research and development are belong to high knowledge based content in the biotech medical industry, and the reliability of biotechnology industry's financial statements and the earnings management behavior conducted by the management in their accrual manipulation have been a critical issue. In recent years, some studies have used the data mining technique to detect earnings management, with which the accuracy has therefore risen. As such, this study attempts to diagnose the detecting biotechnology industry earnings management by integrating suitable computing models, we first screened the earnings management variables with the principal component analysis (PCA) and Bayesian network (BN), followed by further constructing the integrated model with the back propagation neural network (BPN) and C5.0 (decision tree) to detect if a company's earnings were seriously manipulated. The empirical results show that combining the EN screening method with C5.0 decision tree has the best performance with an accuracy rate of 98.51%. From the rules set in the final additional testing of the study, it is also found that an enterprise's prior period discretionary accruals play an important role in affecting the serious degree of accrual earnings management. (C) 2014 Elsevier B.V. All rights reserved.
關聯:	ECONOMIC MODELLING 卷: 46 頁碼: 1-10
显示于类别:	[會計學系暨研究所 ] 期刊論文

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