dc.contributor.advisor | 蔡瑞煌<br>馬秀如 | zh_TW |
dc.contributor.author (Authors) | 徐國英 | zh_TW |
dc.creator (作者) | 徐國英 | zh_TW |
dc.date (日期) | 2007 | en_US |
dc.date.accessioned | 18-Sep-2009 20:14:38 (UTC+8) | - |
dc.date.available | 18-Sep-2009 20:14:38 (UTC+8) | - |
dc.date.issued (上傳時間) | 18-Sep-2009 20:14:38 (UTC+8) | - |
dc.identifier (Other Identifiers) | G0095356022 | en_US |
dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/36946 | - |
dc.description (描述) | 碩士 | zh_TW |
dc.description (描述) | 國立政治大學 | zh_TW |
dc.description (描述) | 資訊管理研究所 | zh_TW |
dc.description (描述) | 95356022 | zh_TW |
dc.description (描述) | 96 | zh_TW |
dc.description.abstract (摘要) | 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. | en_US |
dc.description.tableofcontents | CHAPTER 1 INTRODUCTION 11.1 General Background 11.2 Motivation of Research 31.3 Purpose of Research 41.4 Overview 6CHAPTER 2 LITERATURE REVIEW 72.1 Definition of Fraud 72.2 Schemes of Financial Reporting Fraud 102.3 Corporate Governance and Financial Reporting Fraud 142.4 Detection Techniques of Financial Reporting Fraud 172.5 Self-Organizing Map 212.5.1 Self-Organizing Map and Financial Application 232.5.2 Growing Hierarchical Self-Organizing Map (GHSOM) 25CHAPTER 3 RESEARCH METHODOLOGY 283.1 Sample 283.2 Variable 343.2.1 Dependent Variable 343.2.2 Independent Variable 343.3 Research Method 443.3.1 Descriptive Statistic 443.3.2 Multi-collinearity 443.3.3 Significance Test-Discriminant Analysis 443.3.4 Growing Hierarchical Self-Organizing Map (GHSOM) 46CHAPTER 4 EXPERIMENTAL RESULTS 484.1 Descriptive Statistics 484.2 Canonical Discriminant Analysis (CANDISC) 514.3 GHSOM Experiment 544.3.1 GHSOM Model Selection 544.3.2 Labeling Significant Leaves 56CHAPTER 5 DISCUSSION AND CONCLUSION 855.1 Conclusion 855.2 Strategy Implications 885.3 Limitations of the Study 895.4 Recommendations for Future Research 89 | zh_TW |
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dc.format.mimetype | application/pdf | - |
dc.language.iso | en_US | - |
dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0095356022 | en_US |
dc.subject (關鍵詞) | 財務報表舞弊 | zh_TW |
dc.subject (關鍵詞) | 成長階層式自我組織圖 | zh_TW |
dc.subject (關鍵詞) | 知識擷取 | zh_TW |
dc.subject (關鍵詞) | Financial Reporting Fraud | en_US |
dc.subject (關鍵詞) | Growing Hierarchical Self-Organizing Map (GHSOM) | en_US |
dc.subject (關鍵詞) | Knowledge Extraction | en_US |
dc.title (題名) | 財務報表舞弊之探索研究 | zh_TW |
dc.title (題名) | Exploring financial reporting fraud | en_US |
dc.type (資料類型) | thesis | en |
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