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題名 探討新聞文本情緒分析與企業舞弊偵測之關聯性研究
Exploring the relationship between the news sentiment analysis and the corporate fraud detection作者 麥嘉蕙 貢獻者 諶家蘭
麥嘉蕙關鍵詞 舞弊偵測
新聞情緒分析
情緒詞典
文本分析
羅吉斯迴歸
Fraud detection
News sentiment analysis
Textual analysis
Logistic regression日期 2021 上傳時間 3-May-2021 10:23:35 (UTC+8) 摘要 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。
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.參考文獻 江玟瑜(2019)。以資料探勘技術偵測財務報表舞弊。國立臺灣大學會計學研究所碩士論文。岑紹基(2010)。語言功能與中文教學：系統功能語言學在中文教學上應用。香港：香港大學出版社。李承諺(2013)。應用舞弊三角理論偵測及預測財務報表舞弊-以台灣上市(櫃)公司為例。國立成功大學會計研究所學位論文。林宜萱(2013)。財經領域情緒辭典之建置與其有效性之驗證-以財經新聞為元件。臺灣大學會計學研究所學位論文。黃娟娟 (2012)。公司年報文字探勘與財務預警資訊內涵。逢甲大學商學博士學位學程博士論文。廖宜心(2019)。資料探勘技術於繼續經營能力評估模型之應用－媒體情緒分析。國立臺灣大學會計學研究所碩士論文。賴士詮(2018)。結合文字探勘與財務指標建置財務預警模型之研究。國立政治大學資訊管理系研究所學位論文。財團法人中華民國會計研究發展基金會，審計準則公報第四十三號-查核財務報表對舞弊之考量。Abrahams, A.S., Fan, W., Wang, G.A., Zhang, Z., and Jiao, J. (2015) An integrated text analytic framework for product defect discovery. Production and Operations Management, 24,6, 975–990.Amiram, D., Bozanic, Z., Cox, J. D., Dupont, Q., Karpoff, J. M., & Sloan, R. (2018). Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature. Review of Accounting Studies, 23(2), 732–783.An, Z., Chen, C., Naiker, V., & Wang, J. (2020). Does media coverage deter firms from withholding bad news? Evidence from stock price crash risk. Journal of Corporate Finance, 64, 101664.Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58(1), 199-235.Beasley, M. S., Carcello, J. V., Hermanson, D. R., & Neal, T. L. (2010) “Fraudulent Financial Reporting: 1998–2007: An Analysis of U.S. Public Companies.” Sponsored by the Committee of Sponsoring Organizations of the Treadway Commission (COSO), 2010.Bian, S., Jia, D., Li, F., & Yan, Z. (2019). A New Chinese Financial Sentiment Dictionary for Textual Analysis in Accounting and Finance. SSRN Electronic Journal. doi:10.2139/ssrn.3446388Brazel, J. F., Jones, K. L., and Zimbelman, M. F. (2009). Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, 47,5, 1135–1166.Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What Are You Saying? Using topic to Detect Financial Misreporting. Journal of Accounting Research, 58(1), 237–291.Call, A. C., Kedia, S., & Rajgopal, S. (2016). Rank and file employees and the discovery of misreporting: The role of stock options. Journal of Accounting and Economics, 62(2-3), 277-300.Cao, J., Luo, X., & Zhang, W. (2020). Corporate employment, red flags, and audit effort. Journal of Accounting and Public Policy, 39(1).Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Detecting Management Fraud in Public Companies. Management Science, 56(7), 1146–1160.Chen, Y., Liou, W., Chen, Y., & Wu, J. (2019). Fraud detection for financial statements of business groups. International Journal of Accounting Information Systems, 32, 1-23.Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82.Dong, W., Liao, S., & Zhang, Z. (2018). Leveraging Financial Social Media Data for Corporate Fraud Detection. Journal of Management Information Systems, 35(2), 461–487.Gray, G. L., & Debreceny, R. S. (2014). A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), 357-380.Hasnan, S., Razali, M. H., & Hussain, A. R. (2020). The effect of corporate governance and firm-specific characteristics on the incidence of financial restatement. Journal of Financial Crime, 27(2).Hobson, J. L., Mayew, W. J., & Venkatachalam, M. (2011). Analyzing Speech to Detect Financial Misreporting. Journal of Accounting Research, 50(2), 349–392.Jacobs, H. (2020). Hype or help? Journalists’ perceptions of mispriced stocks. Journal of Economic Behavior & Organization, 178, 550–565.Kim, C. S., Wang, K., Zhang, L. D.(2018). Readability of 10-k reports and stock price crash risk. Contemporary Accounting Research, 36(2),1184-1216.Loughran, T., & Mcdonald, B. (2011). When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.Miller, G. S. (2006). The Press as a Watchdog for Accounting Fraud. Journal of Accounting Research, 44(5), 1001–1033.Purda, L.; and Skillicorn, D. (2014). Accounting variables, deception, and a bag of words: Assessing the tools of fraud detection. Contemporary Accounting Research, 32,3, 1193–1223.Richardson, S. A., Sloan, R. G., Soliman, M. T., & Tuna, I. (2005). Accrual reliability, earnings persistence and stock prices. Journal of Accounting and Economics, 39(3), 437-485.Singh, N., Lai, K., Vejvar, M., & Cheng, T. C. (2019). Data‐driven auditing: A predictive modeling approach to fraud detection and classification. Journal of Corporate Accounting & Finance, 30(3), 64-82.Skousen, C. and Wright, C. (2008). Contemporaneous risk factors and the prediction of financial statement fraud. Journal of Forensic Accounting, IX,37-62.Summers, S. L. and Sweeney, J. T. (1998) Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73,1, 131–146.Sun, Y., Sun, X., & Wu, W. (2020). Who detects corporate fraud under the thriving of the new media? Evidence from Chinese‐listed firms. Accounting & Finance.ACFE Report to the Nations: 2020 Global Fraud Study. (2020). Retrieved from https://www.acfe.com/report-to-the-nations/2020/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= 描述 碩士
國立政治大學
會計學系
107353044資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107353044 資料類型 thesis dc.contributor.advisor 諶家蘭 zh_TW dc.contributor.author (Authors) 麥嘉蕙 zh_TW dc.creator (作者) 麥嘉蕙 zh_TW dc.date (日期) 2021 en_US dc.date.accessioned 3-May-2021 10:23:35 (UTC+8) - dc.date.available 3-May-2021 10:23:35 (UTC+8) - dc.date.issued (上傳時間) 3-May-2021 10:23:35 (UTC+8) - dc.identifier (Other Identifiers) G0107353044 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/134863 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 會計學系 zh_TW dc.description (描述) 107353044 zh_TW dc.description.abstract (摘要) 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 (摘要) 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第一節 研究動機與目的 5第二節 研究問題 7第三節 研究流程 8第四節 論文組織 9第二章 文獻探討 10第一節 舞弊 10第二節 舞弊偵測相關研究 12第三節 媒體與舞弊偵測的關聯 13第四節 應用於舞弊偵測的技術 14第五節 系統功能語言學 15第六節 小結 18第三章 研究方法 22第一節 研究方法流程 22第二節 研究假說 23第三節 研究模型 26第四節 樣本選取 33第五節 資料預處理 49第六節 小結 52第四章 研究結果與分析 53第一節 敍述性統計 53第二節 相關係數及共線性分析 56第三節 實證分析與結果 60第四節 小結 67第五章 結論 69第一節 討論和心得 69第二節 研究限制及建議 70參考文獻 72附錄一、斷詞程式碼 77附錄二、正面情緒字詞 80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 (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107353044 en_US dc.subject (關鍵詞) 舞弊偵測 zh_TW dc.subject (關鍵詞) 新聞情緒分析 zh_TW dc.subject (關鍵詞) 情緒詞典 zh_TW dc.subject (關鍵詞) 文本分析 zh_TW dc.subject (關鍵詞) 羅吉斯迴歸 zh_TW dc.subject (關鍵詞) Fraud detection en_US dc.subject (關鍵詞) News sentiment analysis en_US dc.subject (關鍵詞) Textual analysis en_US dc.subject (關鍵詞) Logistic regression en_US dc.title (題名) 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 (題名) Exploring the relationship between the news sentiment analysis and the corporate fraud detection en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 江玟瑜(2019)。以資料探勘技術偵測財務報表舞弊。國立臺灣大學會計學研究所碩士論文。岑紹基(2010)。語言功能與中文教學：系統功能語言學在中文教學上應用。香港：香港大學出版社。李承諺(2013)。應用舞弊三角理論偵測及預測財務報表舞弊-以台灣上市(櫃)公司為例。國立成功大學會計研究所學位論文。林宜萱(2013)。財經領域情緒辭典之建置與其有效性之驗證-以財經新聞為元件。臺灣大學會計學研究所學位論文。黃娟娟 (2012)。公司年報文字探勘與財務預警資訊內涵。逢甲大學商學博士學位學程博士論文。廖宜心(2019)。資料探勘技術於繼續經營能力評估模型之應用－媒體情緒分析。國立臺灣大學會計學研究所碩士論文。賴士詮(2018)。結合文字探勘與財務指標建置財務預警模型之研究。國立政治大學資訊管理系研究所學位論文。財團法人中華民國會計研究發展基金會，審計準則公報第四十三號-查核財務報表對舞弊之考量。Abrahams, A.S., Fan, W., Wang, G.A., Zhang, Z., and Jiao, J. (2015) An integrated text analytic framework for product defect discovery. Production and Operations Management, 24,6, 975–990.Amiram, D., Bozanic, Z., Cox, J. D., Dupont, Q., Karpoff, J. M., & Sloan, R. (2018). Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature. Review of Accounting Studies, 23(2), 732–783.An, Z., Chen, C., Naiker, V., & Wang, J. (2020). Does media coverage deter firms from withholding bad news? Evidence from stock price crash risk. Journal of Corporate Finance, 64, 101664.Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58(1), 199-235.Beasley, M. S., Carcello, J. V., Hermanson, D. R., & Neal, T. L. (2010) “Fraudulent Financial Reporting: 1998–2007: An Analysis of U.S. Public Companies.” Sponsored by the Committee of Sponsoring Organizations of the Treadway Commission (COSO), 2010.Bian, S., Jia, D., Li, F., & Yan, Z. (2019). A New Chinese Financial Sentiment Dictionary for Textual Analysis in Accounting and Finance. SSRN Electronic Journal. doi:10.2139/ssrn.3446388Brazel, J. F., Jones, K. L., and Zimbelman, M. F. (2009). Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, 47,5, 1135–1166.Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What Are You Saying? Using topic to Detect Financial Misreporting. Journal of Accounting Research, 58(1), 237–291.Call, A. C., Kedia, S., & Rajgopal, S. (2016). Rank and file employees and the discovery of misreporting: The role of stock options. Journal of Accounting and Economics, 62(2-3), 277-300.Cao, J., Luo, X., & Zhang, W. (2020). Corporate employment, red flags, and audit effort. Journal of Accounting and Public Policy, 39(1).Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Detecting Management Fraud in Public Companies. Management Science, 56(7), 1146–1160.Chen, Y., Liou, W., Chen, Y., & Wu, J. (2019). Fraud detection for financial statements of business groups. International Journal of Accounting Information Systems, 32, 1-23.Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82.Dong, W., Liao, S., & Zhang, Z. (2018). Leveraging Financial Social Media Data for Corporate Fraud Detection. Journal of Management Information Systems, 35(2), 461–487.Gray, G. L., & Debreceny, R. S. (2014). A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), 357-380.Hasnan, S., Razali, M. H., & Hussain, A. R. (2020). The effect of corporate governance and firm-specific characteristics on the incidence of financial restatement. Journal of Financial Crime, 27(2).Hobson, J. L., Mayew, W. J., & Venkatachalam, M. (2011). Analyzing Speech to Detect Financial Misreporting. Journal of Accounting Research, 50(2), 349–392.Jacobs, H. (2020). Hype or help? Journalists’ perceptions of mispriced stocks. Journal of Economic Behavior & Organization, 178, 550–565.Kim, C. S., Wang, K., Zhang, L. D.(2018). Readability of 10-k reports and stock price crash risk. Contemporary Accounting Research, 36(2),1184-1216.Loughran, T., & Mcdonald, B. (2011). When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.Miller, G. S. (2006). The Press as a Watchdog for Accounting Fraud. Journal of Accounting Research, 44(5), 1001–1033.Purda, L.; and Skillicorn, D. (2014). Accounting variables, deception, and a bag of words: Assessing the tools of fraud detection. Contemporary Accounting Research, 32,3, 1193–1223.Richardson, S. A., Sloan, R. G., Soliman, M. T., & Tuna, I. (2005). Accrual reliability, earnings persistence and stock prices. Journal of Accounting and Economics, 39(3), 437-485.Singh, N., Lai, K., Vejvar, M., & Cheng, T. C. (2019). Data‐driven auditing: A predictive modeling approach to fraud detection and classification. Journal of Corporate Accounting & Finance, 30(3), 64-82.Skousen, C. and Wright, C. (2008). Contemporaneous risk factors and the prediction of financial statement fraud. Journal of Forensic Accounting, IX,37-62.Summers, S. L. and Sweeney, J. T. (1998) Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73,1, 131–146.Sun, Y., Sun, X., & Wu, W. (2020). Who detects corporate fraud under the thriving of the new media? Evidence from Chinese‐listed firms. Accounting & Finance.ACFE Report to the Nations: 2020 Global Fraud Study. (2020). Retrieved from https://www.acfe.com/report-to-the-nations/2020/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 (DOI) 10.6814/NCCU202100422 en_US
