Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/134863| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 諶家蘭 | zh_TW |
| dc.contributor.author | 麥嘉蕙 | zh_TW |
| dc.creator | 麥嘉蕙 | zh_TW |
| dc.date | 2021 | en_US |
| dc.date.accessioned | 2021-05-03T02:23:35Z | - |
| dc.date.available | 2021-05-03T02:23:35Z | - |
| dc.date.issued | 2021-05-03T02:23:35Z | - |
| dc.identifier | G0107353044 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/134863 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 會計學系 | zh_TW |
| dc.description | 107353044 | zh_TW |
| dc.description.abstract | 本研究最主要之目的，係探討新聞文本資訊是否能反映出公司的財務狀況，並有效分辨舞弊公司，提早向投資人作出警示。本研究收集2010到2020年間遭投資者保護中心起訴及TEJ資料庫中所記載發生舞弊事件之公司，選擇共58家發生舞弊事件的企業，以資產規模相近為準則選取116家一般公司為參照，收集舞弊公司舞弊曝光前兩年的新聞並計算相關新聞文本情緒字詞，得出情緒變數。最後以羅吉斯回歸來檢驗新聞文本情緒與舞弊偵測之關聯性。實證結果發現，「負面詞佔比」、「情緒強度」、「負面新聞數量」能顯著分辨舞弊公司及一般公司，亦發現加入情緒分數的迴歸式比起單使用財務變數之迴歸式解釋力更強。 | zh_TW |
| dc.description.abstract | The main purpose of this study is to examine whether the press release information can reflect the company`s financial situation and effectively identify fraudulent companies so that investors can be warned in advance. In this study, we collected companies that were prosecuted by the Securities and Futures Investors Protection Center(SFIPC) and flagged as fraudulent by TEJ database from 2010 to 2020. Finally selected a total of 58 companies that had fraudulent events, and 116 companies that had no fraudulent events based on similar asset size. Logistic regression was used to examine the correlation between news sentiment and fraud detection. The results show that "negative words", "sentiment intensity", and "number of negative news" can significantly distinguish fraudulent companies from ordinary companies. The regression which combined sentiment variables with financial variables have stronger explanatory power than regressions with only financial variables. | en_US |
| dc.description.tableofcontents | 第一章 緒論 5\n第一節 研究動機與目的 5\n第二節 研究問題 7\n第三節 研究流程 8\n第四節 論文組織 9\n第二章 文獻探討 10\n第一節 舞弊 10\n第二節 舞弊偵測相關研究 12\n第三節 媒體與舞弊偵測的關聯 13\n第四節 應用於舞弊偵測的技術 14\n第五節 系統功能語言學 15\n第六節 小結 18\n第三章 研究方法 22\n第一節 研究方法流程 22\n第二節 研究假說 23\n第三節 研究模型 26\n第四節 樣本選取 33\n第五節 資料預處理 49\n第六節 小結 52\n第四章 研究結果與分析 53\n第一節 敍述性統計 53\n第二節 相關係數及共線性分析 56\n第三節 實證分析與結果 60\n第四節 小結 67\n第五章 結論 69\n第一節 討論和心得 69\n第二節 研究限制及建議 70\n參考文獻 72\n附錄一、斷詞程式碼 77\n附錄二、正面情緒字詞 80\n附錄三、負面字詞列表 88 | zh_TW |
| dc.format.extent | 2619396 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0107353044 | en_US |
| dc.subject | 舞弊偵測 | zh_TW |
| dc.subject | 新聞情緒分析 | zh_TW |
| dc.subject | 情緒詞典 | zh_TW |
| dc.subject | 文本分析 | zh_TW |
| dc.subject | 羅吉斯迴歸 | zh_TW |
| dc.subject | Fraud detection | en_US |
| dc.subject | News sentiment analysis | en_US |
| dc.subject | Textual analysis | en_US |
| dc.subject | Logistic regression | en_US |
| dc.title | 探討新聞文本情緒分析與企業舞弊偵測之關聯性研究 | zh_TW |
| dc.title | Exploring the relationship between the news sentiment analysis and the corporate fraud detection | en_US |
| dc.type | thesis | en_US |
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(1998) Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73,1, 131–146.\nSun, Y., Sun, X., & Wu, W. (2020). Who detects corporate fraud under the thriving of the new media? Evidence from Chinese‐listed firms. Accounting & Finance.\nACFE Report to the Nations: 2020 Global Fraud Study. (2020). Retrieved from https://www.acfe.com/report-to-the-nations/2020/\n投保中心依投保法第10條之1第1項第1款代表公司提起公司法第214條、227條訴訟時，起訴對象可否及於「卸任董監事」？—最高法院一○六年度台上字第二四二○號民事判決。取自：https://www.angle.com.tw/news/post27.aspx?ip= | zh_TW |
| dc.identifier.doi | 10.6814/NCCU202100422 | en_US |
| item.cerifentitytype | Publications | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.openairetype | thesis | - |
| item.fulltext | With Fulltext | - |
| item.grantfulltext | open | - |
| Appears in Collections: | 學位論文 | |
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| 304401.pdf | 2.56 MB | Adobe PDF2 | View/Open |
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