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研究生:邱彥鈞
研究生(外文):Yen-Jiun Chiou
論文名稱:預測盈餘管理之可行性研究─以電子業為例
論文名稱(外文):Earnings Management Prediction Using Neural Networks and Decision Trees: a Case Study of Electronics Industry
指導教授:蔡志豐蔡志豐引用關係
指導教授(外文):Chih-Fong Tsai
學位類別:碩士
校院名稱:國立中正大學
系所名稱:會計所
學門:商業及管理學門
學類:會計學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:95
中文關鍵詞:決策樹類神經網路資料探勘盈餘管理
外文關鍵詞:Earnings ManagementData MiningNeural NetworksDecision Trees
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近年來,多數上市公司紛紛無預警地爆發財務危機。對於大多數的投資人以及資金授信者而言,這些財務危機的發生幾乎是不可預知的,尤其當牽涉盈餘管理時更是如此。因為盈餘管理係管理階層透過特定方法或程序用以操縱會計盈餘進而達成一己之私利的過程,因此隱藏的財務危機更得以窗飾。
在盈餘管理的相關文獻中,已有多數研究指出何種因素對於盈餘管理有顯著影響。然而這些因素雖然與盈餘管理具有相關性,但是卻未被直接用來預測盈餘管理的程度。
為了減少盈餘管理所帶來的財務危機風險以及幫助投資人避免巨額的投資虧損,本研究開發出一套類神經網路模型用以預測盈餘管理。類神經網路技術已在釵h領域中被證實其在模型建立以及預測上具有極佳的效果。本研究主要目的在於探討類神經網路是否對於向上及向下盈餘操縱具備預測之能力。
本研究採用台灣經濟新報資料庫系統作為資料來源,並且根據文獻選取了11個與盈餘管理相關的輸入變數。本研究經過交叉驗證之後發現,類神經網路對於向上盈餘操縱的預測準確度最高可達81.08%。此外,本研究亦利用決策樹塑模方法建立CART以及C5.0二種決策樹模型,發現了有趣的預測向上盈餘管理的規則。也就是同時具備低公司績效、高盈餘持續性以及已發行股數變動量高者,其向上盈餘操縱的可能性極高。
Recently, one of the most attractive business news is a series of financial crisis cases related to the public companies. However, many investors and creditors cannot foresee the financial crisis, especially in the cases with earnings management. Earnings management is manipulating earnings in accounting 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. That is, 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.
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. Neural networks have been proven to possess the most practical effect in modeling and forecasting in many areas. The aim of this paper is to investigate the applicability of neural networks for predicting upwards and downwards of earnings management.
We used the TEJ database and 11 input variables which are selected based on the related factors identified in literature. After running 5-fold cross-validation, the accuracy of the prediction model can reach up to 81.08% in the cases of manipulating earnings upwards. Besides, we also developed the decision trees models trained and tested by CART and C5.0. In the built decision trees, we found several rules to predict the upward earnings management cases. That is, upward earnings management will most likely occur in the firms which simultaneously have low firm performance, high earnings persistence and the outstanding shares increased or decreased by 10%.
LIST OF TABLES v
LIST OF FIGURES vi
Chapter 1 Introduction 1
1.1. Motivation 1
1.2. Aim and Objectives 4
1.2.1. Aim 4
1.2.2. Objectives 4
1.3. Contribution 5
1.4. Structure of the Thesis 7
Chapter 2 Literature Review 8
2.1. Earnings Management 8
2.1.1. Definition of Earnings Management 8
2.1.2. Related Work of Earnings Management 9
2.2. Measurement of Earnings Management 21
2.2.1. The Proxy of Earnings Management 21
2.2.2. Choice of Measuring Models 21
2.2.3. The Cross-Sectional Jones Model 23
2.3. Data Mining 25
2.3.1. Definition of Data Mining 25
2.3.2. Data Mining Strategies 26
2.4. Neural Networks 29
2.4.1. Definition and Architecture of Neural Networks 29
2.4.2. Backpropagation Algorithm 32
2.5. Discussion 35
Chapter 3 Experimental Methodology 37
3.1. Sample Selection and Data Collection 39
3.2. Variables Selection 40
3.2.1. Related Variables 40
3.2.2. An Example For Variables Selection 46
3.3. Models Construction/ Parameters Settings 48
3.4. Training and Testing Using 5-fold Cross-validation 53
3.5. Rules Identification 55
3.5.1. Rules of Decision Trees 55
3.5.2. Comparison of Decision Trees and MLP Model 58
Chapter 4 Results 61
4.1. The Results of the MLP Models 61
4.2. The Results of the Rules of Decision Trees 64
4.2.1. The Best Classifier Selection Phase 64
4.2.2. Decision Trees Training Phase 64
4.2.3. Decision Trees Testing Phase 73
4.3. The Results of the Comparison of Decision Trees and MLP 80
4.3.1. The Best Classifier Selection Phase 80
4.3.2. MLP model Training Phase 80
4.3.3. MLP model Testing Phase 81
Chapter 5 Conclusions 83
5.1. Research Summary & Discussion 83
5.2. Research Constraints 85
5.3. Future Works 86
Appendix 87
Appendix A: Rate of Accuracy of 100 Classifiers in Predicting Upward Cases 87
References 88
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