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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林嬋娟 | |
dc.contributor.author | Wen-Yu Chiang | en |
dc.contributor.author | 江玟諭 | zh_TW |
dc.date.accessioned | 2021-06-17T08:32:05Z | - |
dc.date.available | 2021-03-27 | |
dc.date.copyright | 2019-08-19 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-10 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74368 | - |
dc.description.abstract | 本文以資料探勘技術應用於我國財務報表舞弊之偵測,並探討文字資訊對於預測財務報表舞弊是否具資訊增益量(Information Gain)。在考量舞弊之特性後,本文採隨機森林(Random Forest)此資料探勘技術,並參考舞弊三角架構以及從年報中致股東報告書及營運概況之文字資訊選取與舞弊相關之變數以建構偵測模型。研究結果顯示,隨機森林較傳統迴歸模型更能準確區分舞弊及非舞弊公司,而年報中之文字對於辨別舞弊與非舞弊公司較無資訊增益量。然而,值得注意的是,年報中之不確定性字詞對於區分舞弊及非舞弊公司之重要性於83個變數中居第13名,顯示年報中之不確定性字詞為偵測財務報表舞弊之重要指標之一。 | zh_TW |
dc.description.abstract | This study attempts to apply data mining techniques on detection of fraudulent financial statements, and investigate whether textual information has information gain for fraudulent financial statements detection. Considering the characteristics of fraud, this study uses Random Forest as data mining techniques to build fraud detection model. Structured variables are selected based on fraud triangle, and textual information is extracted from letter to shareholders and operation review in annual report. The result shows that Random Forest achieved higher classification accuracy than traditional regression model, and the text in annual report has no explicit information gain for distinguishing fraudulent and non-fraudulent companies. However, it is worth noting that the importance of uncertain words in annual report ranks 13 among 83 variables. This implies that tentative words in annual report may be regarded as an important indicator to fraudulent financial statement occurrence. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:32:05Z (GMT). No. of bitstreams: 1 ntu-108-R06722019-1.pdf: 1121747 bytes, checksum: 5449a19c8eb3890e88d7bdbedb00e808 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 謝辭 I
摘要 II ABSTRACT III I. Introduction 1 A. Research Motivations and Purposes 1 B. Research Structure 5 II. Literature Review 7 A. Definition of Fraudulent Financial Statement 7 B. Risk Factors Related to Misstatements Arising from Fraudulent Financial Statement 9 C. Text Mining Techniques Applied on Detection of Fraudulent Financial Statement 12 D. Data Mining Techniques and Detection of Fraudulent Financial Statement 16 III. Research Design 18 A. Sample Selection 18 B. Variable Selection and Data Source 19 C. Research Methodology - Random Forest 23 D. Research Process 25 IV. Results and Analysis 28 A. Descriptive Statistics of Fraudulent Observations 28 B. Independent Variables Univariant Analysis 29 C. Results of Features Evaluation 33 D. Performance of Detection Model 35 V. Conclusions and Suggestions 38 A. Research Conclusions 38 B. Research Constraints and Suggestions 39 References 41 Appendix 45 | |
dc.language.iso | en | |
dc.title | 以資料探勘技術偵測財務報表舞弊 | zh_TW |
dc.title | Fraudulent Financial Statement Detection Using Data Mining Techniques | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林孝倫,謝喻婷 | |
dc.subject.keyword | 資料探勘,隨機森林,文字探勘,財務報表舞弊,舞弊三角, | zh_TW |
dc.subject.keyword | Data Mining,Random Forest,Text Mining,Fraudulent Financial Statement,Fraud Triangle, | en |
dc.relation.page | 49 | |
dc.identifier.doi | 10.6342/NTU201903029 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-12 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 會計學研究所 | zh_TW |
顯示於系所單位: | 會計學系 |
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