Earnings management prediction: A pilot study of combining neural networks and decision trees

NCU Institutional Repository > 管理學院 > 資訊管理研究所 > 期刊論文 > Item 987654321/29658

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/29658

题名:	Earnings management prediction: A pilot study of combining neural networks and decision trees
作者:	Tsai,CF;Chiou,YJ
贡献者:	資訊管理研究所
关键词:	DISCRETIONARY-ACCRUALS;BANKRUPTCY PREDICTION;INCENTIVES;BUSINESS;SERVICES
日期:	2009
上传时间:	2010-06-29 20:37:32 (UTC+8)
出版者:	中央大學
摘要:	Many financial crisis cases related to the public companies have increased recently, but many investors and creditors are difficult to foresee the financial crisis, especially in the cases with earnings management. Earnings management is Manipulating earnings to fulfill managers' purposes using certain methods or processes. In literature. many studies related to earnings management only focus on identifying some related factors which can significantly affect earnings management. Therefore, we can only figure out the correlation between these factors and earnings management. However, these factors have not been used directly to forecast the level of earnings management in advance (i.e. upward and downward earnings management). In order to decrease the financial crisis risks derived from earnings management and help the investors avoid suffering a great loss in the stock market, we developed a neural network model to predict the level of earnings management. By using the Taiwan Economic Journal (TEJ) dataset and 11 factors which affect earnings management studied in literature, the model provides the highest prediction rate of 81% in the cases of manipulating earnings upwards. In addition, the cases which are correctly predicted by the neural network model are used to construct a decision tree model to generate useful decision rules. Two important rules are identified to allow investors and creditors for effective earnings management prediction. Combining the neural network and decision tree models provide not only higher rate of prediction accuracy but also important decision rules compared with using the neural network model alone. (c) 2008 Elsevier Ltd. All rights reserved.
關聯:	EXPERT SYSTEMS WITH APPLICATIONS
显示于类别:	[資訊管理研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	1328	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....