應用貝氏網路、決策樹、約略集合及倒傳遞類神經網路偵測應計項目之盈餘管理

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题名:	應用貝氏網路、決策樹、約略集合及倒傳遞類神經網路偵測應計項目之盈餘管理 Applying Bayesian Network, Decision Tree, Rough Set Theory, and Back Propagation Network Analysis for Detecting Accrual Earnings Management
作者:	汪奕丞 Wang, Yi-Cheng
贡献者:	會計學系
关键词:	資料探勘貝氏網路倒傳遞類神經約略集合決策樹應計項目盈餘管理 data mining bayesian network back-propagation neural network rough sets theory decision trees accrual earnings management
日期:	2014-06
上传时间:	2014-09-30 15:55:51 (UTC+8)
摘要:	企業於財務報表上的可靠性及管理階層操縱應計項目形成的盈餘管理行為一直是會計的重大議題。以往於應計項目盈餘管理上皆是使用傳統的迴歸模式，近年來已有多位學者使用資料探勘方法針對應計項目盈餘管理做研究，準確度有所提高，但整體文獻上還不夠完整，因此本研究以資料探勘法中的決策樹及倒傳遞類神經網路來進行預測，希望能得出一個更準確地偵測模式。本研究嘗試以CHAID決策樹、約略集合及貝氏網路(Bayesian Networks, BN)先將變數進行第一階段的篩選，再進一步使用倒傳遞類神經網路及C5.0決策樹來建立模型檢測企業是否具有嚴重操縱盈餘的情況。而實證結果顯示，約略集合篩選方法搭配倒傳遞類神經網路的表現最佳，準確率為96.82%。 Enterprise reliability in financial statements and management manipulated ac-cruals formed earnings management behavior has been a major topic of accounting. Previous studies in accrual earnings management are using traditional regression model. In recent years, a number of scholars have been doing research using data mining methods for accruals earnings management and accuracy was improved, but it is not enough to complete the whole literature. Therefore, this study use data mining method in the decision tree and back-propagation neural network to make predictions, hoping to achieve a more accurate detection mode. This study attempts to use CHAID decision trees, rough sets and Bayesian Net-work in the first stage of the variable filter. Further use of back-propagation neural network and C5.0 decision tree to modeling detect whether a company has a serious manipulation of earnings. The empirical results show that rough sets theory screening method with back-propagation neural network has the best performing, and it’s accuracy rate is 96.82%.
显示于类别:	[會計學系暨研究所 ] 博碩士論文

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