臺灣博碩士論文加值系統

Financial reporting fraud leads to not only significant investment risks for external stockholders, but also financial crises for the capital market. Although the issue of fraudulent financial reporting has drawn much attention, relevant research is much less than issues of predicting financial distress or bankruptcy. Furthermore, one purpose of exploring the financial reporting fraud with various forms is to obtain a better understand of the corporate through investigating its financial and corporate governance indicators. This study addresses the challenge with proposing an approach with the following four phases: (1) to identify a set of financial and corporate governance indicators that are significantly correlated with the financial reporting fraud; (2) to use the Growing Hierarchical Self-Organizing Map (GHSOM) to cluster the normal and fraud listed corporate data; (3) to extract knowledge about the financial reporting fraud through observing the hierarchical relationship displayed in the trained GHSOM; and (4) to make the justification of the extracted knowledge. The proposed approach is feasible because researchers claim that the GHSOM can discover the hidden hierarchical relationship from data with high dimensionality.

CHAPTER 1 INTRODUCTION 1
1.1 General Background 1
1.2 Motivation of Research 3
1.3 Purpose of Research 4
1.4 Overview 6
CHAPTER 2 LITERATURE REVIEW 7
2.1 Definition of Fraud 7
2.2 Schemes of Financial Reporting Fraud 10
2.3 Corporate Governance and Financial Reporting Fraud 14
2.4 Detection Techniques of Financial Reporting Fraud 17
2.5 Self-Organizing Map 21
2.5.1 Self-Organizing Map and Financial Application 23
2.5.2 Growing Hierarchical Self-Organizing Map (GHSOM) 25
CHAPTER 3 RESEARCH METHODOLOGY 28
3.1 Sample 28
3.2 Variable 34
3.2.1 Dependent Variable 34
3.2.2 Independent Variable 34
3.3 Research Method 44
3.3.1 Descriptive Statistic 44
3.3.2 Multi-collinearity 44
3.3.3 Significance Test－Discriminant Analysis 44
3.3.4 Growing Hierarchical Self-Organizing Map (GHSOM) 46
CHAPTER 4 EXPERIMENTAL RESULTS 48
4.1 Descriptive Statistics 48
4.2 Canonical Discriminant Analysis (CANDISC) 51
4.3 GHSOM Experiment 54
4.3.1 GHSOM Model Selection 54
4.3.2 Labeling Significant Leaves 56
CHAPTER 5 DISCUSSION AND CONCLUSION 85
5.1 Conclusion 85
5.2 Strategy Implications 88
5.3 Limitations of the Study 89
5.4 Recommendations for Future Research 89

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