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題名 財報文本分析與財務預警之關連性研究
The Association between Financial Statements Text Analytics and Financial Warning作者 林鍇銘
Lin, Kai-Ming貢獻者 諶家蘭
Seng, Jia-Lang
林鍇銘
Lin, Kai-Ming關鍵詞 財務危機
舞弊偵測
預警模型
管理階層討論與分析
financial distress
fraud detection
financial warning model
management discussion and analysis日期 2018 上傳時間 7-Aug-2019 15:54:14 (UTC+8) 摘要 分析財務報表進行投資時，投資人最擔心挑到潛在危險的公司，於持有其股份後發生財務危機，或該公司發生舞弊，損害自己的權益。故對投資人而言，即時的財務預警十分重要。本研究欲探討問題公司與一般公司股東會年報管理階層討論與分析的文本是否具重大差異。\n\n本研究以台灣證券交易所公告之2017年1月到2019年3月變更交易方法及此期間發生弊案的企業，採人工判讀股東會年度報告中管理階層討論與分析的文本，計算相關情緒分析字詞，並發展出預測模型，預測潛在風險之公司。實證結果顯示問題公司存在較低的正面字詞佔比、較高的負面字詞佔比、情緒字詞強度呈現負面、標點符號佔比較低、較少於年報中對經濟情況進行舉例。
When analyzing financial statements for investment, investors are most worried about invest potentially dangerous companies. Company financial crisis or the financial fraud occurred after investors holding shares to damage the price. Therefore, for investors, immediate financial warning is very important. The purpose of this study is to investigate whether there is a significant different for company in distress and non- distress in management discussion and analysis in annual reports.\n\nThis study chooses company which have being change transaction method by Taiwan Stock Exchange or have committed frauds from January 2016 to March 2019. Manually interpret will be used to analyze Management Discussion and Analysis in annual report and calculate relevant sentiment tone words. Finally, developing models to predict potential risks for companies. The empirical results show that company in distress has lower proportion of positive words, higher proportion of negative words, lower proportion of emotional words, lower proportion of punctuation, and less examples for economic conditions in the annual report.參考文獻 中文文獻\n吳昱萱，2018，股價波動與財務預警-數據分析觀點，國立政治大學會計研究所碩士論文。\n\n英文文獻\nAltman, E. I. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.\n\nBeaver, W. H. (1966), Financial ratios as predictors of failure. Journal of Accounting Research, 71-111.\n\nBrazel, J. F., Jones, K. L., & Zimbelman, M. F. (2009), Using Nonfinancial Measures to Assess Fraud Risk. Journal of Accounting Research, 47(5), 1135-1166.\n\nBhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2010), Data mining for credit card fraud: A comparative study. Decision Support Systems, 50, 602-613.\n\nCarlos, S. C., & Begoña, G. N. (2016), The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decision Support Systems, 89, 113-122.\n\nCarneiro, N., Figueira, G., & Costa, M. (2017), A data mining based system for credit-card fraud detection in e-tail. Decision Support Systems, 95, 91-101.\n\nChen, Y. J., Wu, C.H., Chen, Y.M., Li, H.Y., & Chen, H.K. (2017), Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32-45.\n\nChuryk, N. T., Lee, C. C., & Clinton, B. G. (2009), Early detection of fraud: Evidence from restatements. Advances in Accounting Behavioral Research, 12, 25-40.\n\nDalnial, H., Kamaluddin, A., Sanusi, Z. M., & Khairuddin, K. S. (2014), Detecting fraudulent financial reporting through financial statement analysis. Journal of Advanced Management Science, 2, 17-22.\n\nDebreceny, R. S., & Gray, G. L. (2010), Data mining journal entries for fraud detection: An exploratory study. International Journal of Accounting Information Systems, 11, 157-181.\n\nDechow, P. M., Ge, W. L., Larson, C. R., & Sloan, R. G. (2011), Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28, 17-82.\n\nDilla, W. N., & Raschke, R. L. (2015), Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems, 16, 1-22.\n\nDong, W., Liao, S., & Zhang, Z. (2018), Leveraging Financial Social Media Data for Corporate Fraud Detection. Journal of Management Information Systems, 35, 461-487.\n\nFrazier, K. B., Ingram, R. W., & Tennyson, B. M. (1984), A methodology for the analysis of narrative accounting disclosures. Journal of Accounting Research, 22(1), 318-331.\n\nGeva, T., & Zahavi, J. (2013), Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decision Support Systems, 57, 212-223.\n\nGray, 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, 357-380.\n\nHájek, P. (2018), Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Computing and Applications, 29(7), 343-358.\n\nHeidinger, D., & Gatzert, N. (2018), Awareness, determinants and value of reputation risk management: Empirical evidence from the banking and insurance industry. Journal of Banking and Finance, 91, 106-118.\n\nHumpherys, S. L., Moffitt, K. C., Burns, M. B., Burgoon, J. K., & Felix, W. F. (2011), Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50, 585-594.\n\nJiang, P., Liua, X., Zhang, J., & Yuan, X. (2016), A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and early-warning. Decision Support Systems, 84, 89-103.\n\nKanapickienė, R., & Grundienė, Ž. (2015), The Model of Fraud Detection in Financial Statements by Means of Financial Ratios. Procedia - Social and Behavioral Sciences, 213, 321-327.\n\nKhandani, A.E., Kim, A. J., & Lo, A. W. (2010), Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34, 2767-2787.\n\nKirkos, E., Spathis, C., & Manolopoulos, Y. (2007), Datamining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.\n\nKwak, W., Shi, Y., Cheh, J. J., Lee, H. (2004), Multiple criteria linear programming data mining approach: an application for bankruptcy prediction. Data Mining and Knowledge Management, 164-173.\n\nLi, W. F., Chen, H.C., & Nunamaker, J. F. Jr. (2016), Identifying and Profiling Key Sellers in Cyber Carding Community: AZSecure Text Mining System. Journal of Management Information Systems, 33, 1059-1086.\n\nLin, F., Liang, D., & Chen, E. (2011), Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094-15102.\n\nLopatta, K., Gloger, M. A., Jaeschke, R. (2017), Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10K Filings. Accounting Perspectives, 16(4), 315-343.\n\nLoughran, 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.\n\nMayew, W. J., Sethuraman, M., & Venkatachalam, M. (2015), MD&A disclosure and the firm`s ability to continue as a going concern. The Accounting Review, 90, 1621-1651.\n\nNgai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y. J., & Sun, X. (2011), The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50, 559-569.\n\nNia, S. H. (2015), Financial ratio between fraudulent and non-fraudulent firms: evidence from Tehran Stock Exchange. Journal of Accounting and Taxation, 7(3), 38-44.\n\nOhlson, J. A. (1980), Financial ratio and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 1980(1), 109-131.\n\nOlson, D. L., Delen, D., & Meng, Y. Y. (2011), Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52, 464-473.\n\nPersons, O. S. (1995), Using financial statement data to identify factor associated with fraudulent financial reporting. Journal of Applied Business Research, 11, 38-46.\n\nRavisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2010), Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50, 491-500.\n\nRich, K. T., Roberts, B. L., & Zhang, J. X. (2016), Linguistic Tone of Municipal Management Discussion and Analysis Disclosures and Future Financial Reporting Delays. Journal of Emerging Technologies in Accounting, 13(2), 93-107.\n\nSaha, P., Bose, I., & Mahanti, A. (2016), A knowledge based scheme for risk assessment in loan processing by banks. Decision Support Systems, 84, 78-88.\n\nSiering, M., Koch, J. A., & Deokar A. V. (2016), Detecting Fraudulent Behavior on Crowdfunding Platforms: The Role of Linguistic and Content-Based Cues in Static and Dynamic Contexts. Journal of Management Information Systems, 33, 421-455.\n\nSmith, M., & Taffler, R. J. (2000). The chairman’s statement: A content analysis of discretionary narrative disclosures. Accounting, Auditing, and Accountability, 13(5), 624-646.\n\nSpathis, C. T. (2002), Detecting false financial statements using published data: Some evidence from Greece. Managerial Auditing Journal, 17(4), 179-191.\n\nTennyson, B. M., Ingram, R. W., & Dugan, M. T. (1990), Assessing the information content of narrative disclosures in explaining bankruptcy. Journal of Business Finance & Accounting, 17, 391-410.\n\nTinoco, M. H., & Wilson, N. (2013), Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394-419.\n\nWang, Y.B., & Xu, W. (2018), Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud, Decision Support Systems, 105, 87-95.\n\nYekini, L. S., Wisniewski, T. P., & Millo, Y. (2016), Market reaction to the positiveness of annual report narratives. The British Accounting Review, 48(4), 415-430.\n\nZainudin, E. F., & Hashim, H.A. (2016), Detecting fraudulent financial reporting using financial ratio. Contemporary Accounting Research, 28, 17-82.\n\nZhou, L., Burgoon, J. K., Twitchell, D. P., Nunamaker, J. F., & Twitchell, D. P. (2004), Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communication. Group Decision and Negotiation, 13, 81-106.\n\nZmijewsk, M. (1984), Method Logistical issue related to the estimation of financial distress prediction model. Journal of Accounting Research, 22, 59-82. 描述 碩士
國立政治大學
會計學系
106353041資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106353041 資料類型 thesis dc.contributor.advisor 諶家蘭 zh_TW dc.contributor.advisor Seng, Jia-Lang en_US dc.contributor.author (Authors) 林鍇銘 zh_TW dc.contributor.author (Authors) Lin, Kai-Ming en_US dc.creator (作者) 林鍇銘 zh_TW dc.creator (作者) Lin, Kai-Ming en_US dc.date (日期) 2018 en_US dc.date.accessioned 7-Aug-2019 15:54:14 (UTC+8) - dc.date.available 7-Aug-2019 15:54:14 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 15:54:14 (UTC+8) - dc.identifier (Other Identifiers) G0106353041 en_US dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=143127 - 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) 106353041 zh_TW dc.description.abstract (摘要) 分析財務報表進行投資時，投資人最擔心挑到潛在危險的公司，於持有其股份後發生財務危機，或該公司發生舞弊，損害自己的權益。故對投資人而言，即時的財務預警十分重要。本研究欲探討問題公司與一般公司股東會年報管理階層討論與分析的文本是否具重大差異。\n\n本研究以台灣證券交易所公告之2017年1月到2019年3月變更交易方法及此期間發生弊案的企業，採人工判讀股東會年度報告中管理階層討論與分析的文本，計算相關情緒分析字詞，並發展出預測模型，預測潛在風險之公司。實證結果顯示問題公司存在較低的正面字詞佔比、較高的負面字詞佔比、情緒字詞強度呈現負面、標點符號佔比較低、較少於年報中對經濟情況進行舉例。 zh_TW dc.description.abstract (摘要) When analyzing financial statements for investment, investors are most worried about invest potentially dangerous companies. Company financial crisis or the financial fraud occurred after investors holding shares to damage the price. Therefore, for investors, immediate financial warning is very important. The purpose of this study is to investigate whether there is a significant different for company in distress and non- distress in management discussion and analysis in annual reports.\n\nThis study chooses company which have being change transaction method by Taiwan Stock Exchange or have committed frauds from January 2016 to March 2019. Manually interpret will be used to analyze Management Discussion and Analysis in annual report and calculate relevant sentiment tone words. Finally, developing models to predict potential risks for companies. The empirical results show that company in distress has lower proportion of positive words, higher proportion of negative words, lower proportion of emotional words, lower proportion of punctuation, and less examples for economic conditions in the annual report. en_US dc.description.tableofcontents 第一章 緒論 3\n第一節 研究動機與目的 3\n第二節 研究問題 6\n第三節 研究流程 8\n第二章 文獻探討 9\n第一節 文本分析 9\n第二節 財務危機 13\n第三節 財報舞弊 15\n第三章 研究方法 19\n第一節 研究假設 19\n第二節 資料來源及搜集 22\n第三節 研究設計 23\n第四節 研究模型 25\n第五節 變數解釋 29\n第四章 研究結果及分析 33\n第一節 敘述性統計 33\n第二節 相關係數分析 37\n第三節 實證結果 42\n第五章 結論與建議 49\n第一節 結論 49\n第二節 研究限制及未來研究建議 51\n參考文獻 52\n附錄 57 zh_TW dc.format.extent 1487902 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106353041 en_US dc.subject (關鍵詞) 財務危機 zh_TW dc.subject (關鍵詞) 舞弊偵測 zh_TW dc.subject (關鍵詞) 預警模型 zh_TW dc.subject (關鍵詞) 管理階層討論與分析 zh_TW dc.subject (關鍵詞) financial distress en_US dc.subject (關鍵詞) fraud detection en_US dc.subject (關鍵詞) financial warning model en_US dc.subject (關鍵詞) management discussion and analysis en_US dc.title (題名) 財報文本分析與財務預警之關連性研究 zh_TW dc.title (題名) The Association between Financial Statements Text Analytics and Financial Warning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文文獻\n吳昱萱，2018，股價波動與財務預警-數據分析觀點，國立政治大學會計研究所碩士論文。\n\n英文文獻\nAltman, E. I. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.\n\nBeaver, W. H. (1966), Financial ratios as predictors of failure. Journal of Accounting Research, 71-111.\n\nBrazel, J. F., Jones, K. L., & Zimbelman, M. F. (2009), Using Nonfinancial Measures to Assess Fraud Risk. Journal of Accounting Research, 47(5), 1135-1166.\n\nBhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2010), Data mining for credit card fraud: A comparative study. Decision Support Systems, 50, 602-613.\n\nCarlos, S. C., & Begoña, G. N. (2016), The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decision Support Systems, 89, 113-122.\n\nCarneiro, N., Figueira, G., & Costa, M. (2017), A data mining based system for credit-card fraud detection in e-tail. Decision Support Systems, 95, 91-101.\n\nChen, Y. J., Wu, C.H., Chen, Y.M., Li, H.Y., & Chen, H.K. (2017), Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32-45.\n\nChuryk, N. T., Lee, C. C., & Clinton, B. G. (2009), Early detection of fraud: Evidence from restatements. Advances in Accounting Behavioral Research, 12, 25-40.\n\nDalnial, H., Kamaluddin, A., Sanusi, Z. M., & Khairuddin, K. S. (2014), Detecting fraudulent financial reporting through financial statement analysis. Journal of Advanced Management Science, 2, 17-22.\n\nDebreceny, R. S., & Gray, G. L. (2010), Data mining journal entries for fraud detection: An exploratory study. International Journal of Accounting Information Systems, 11, 157-181.\n\nDechow, P. M., Ge, W. L., Larson, C. R., & Sloan, R. G. (2011), Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28, 17-82.\n\nDilla, W. N., & Raschke, R. L. (2015), Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems, 16, 1-22.\n\nDong, W., Liao, S., & Zhang, Z. (2018), Leveraging Financial Social Media Data for Corporate Fraud Detection. Journal of Management Information Systems, 35, 461-487.\n\nFrazier, K. B., Ingram, R. W., & Tennyson, B. M. (1984), A methodology for the analysis of narrative accounting disclosures. Journal of Accounting Research, 22(1), 318-331.\n\nGeva, T., & Zahavi, J. (2013), Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decision Support Systems, 57, 212-223.\n\nGray, 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, 357-380.\n\nHájek, P. (2018), Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Computing and Applications, 29(7), 343-358.\n\nHeidinger, D., & Gatzert, N. (2018), Awareness, determinants and value of reputation risk management: Empirical evidence from the banking and insurance industry. Journal of Banking and Finance, 91, 106-118.\n\nHumpherys, S. L., Moffitt, K. C., Burns, M. B., Burgoon, J. K., & Felix, W. F. (2011), Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50, 585-594.\n\nJiang, P., Liua, X., Zhang, J., & Yuan, X. (2016), A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and early-warning. Decision Support Systems, 84, 89-103.\n\nKanapickienė, R., & Grundienė, Ž. (2015), The Model of Fraud Detection in Financial Statements by Means of Financial Ratios. Procedia - Social and Behavioral Sciences, 213, 321-327.\n\nKhandani, A.E., Kim, A. J., & Lo, A. W. (2010), Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34, 2767-2787.\n\nKirkos, E., Spathis, C., & Manolopoulos, Y. (2007), Datamining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.\n\nKwak, W., Shi, Y., Cheh, J. J., Lee, H. (2004), Multiple criteria linear programming data mining approach: an application for bankruptcy prediction. Data Mining and Knowledge Management, 164-173.\n\nLi, W. F., Chen, H.C., & Nunamaker, J. F. Jr. (2016), Identifying and Profiling Key Sellers in Cyber Carding Community: AZSecure Text Mining System. Journal of Management Information Systems, 33, 1059-1086.\n\nLin, F., Liang, D., & Chen, E. (2011), Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094-15102.\n\nLopatta, K., Gloger, M. A., Jaeschke, R. (2017), Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10K Filings. Accounting Perspectives, 16(4), 315-343.\n\nLoughran, 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.\n\nMayew, W. J., Sethuraman, M., & Venkatachalam, M. (2015), MD&A disclosure and the firm`s ability to continue as a going concern. The Accounting Review, 90, 1621-1651.\n\nNgai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y. J., & Sun, X. (2011), The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50, 559-569.\n\nNia, S. H. (2015), Financial ratio between fraudulent and non-fraudulent firms: evidence from Tehran Stock Exchange. Journal of Accounting and Taxation, 7(3), 38-44.\n\nOhlson, J. A. (1980), Financial ratio and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 1980(1), 109-131.\n\nOlson, D. L., Delen, D., & Meng, Y. Y. (2011), Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52, 464-473.\n\nPersons, O. S. (1995), Using financial statement data to identify factor associated with fraudulent financial reporting. Journal of Applied Business Research, 11, 38-46.\n\nRavisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2010), Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50, 491-500.\n\nRich, K. T., Roberts, B. L., & Zhang, J. X. (2016), Linguistic Tone of Municipal Management Discussion and Analysis Disclosures and Future Financial Reporting Delays. Journal of Emerging Technologies in Accounting, 13(2), 93-107.\n\nSaha, P., Bose, I., & Mahanti, A. (2016), A knowledge based scheme for risk assessment in loan processing by banks. Decision Support Systems, 84, 78-88.\n\nSiering, M., Koch, J. A., & Deokar A. V. (2016), Detecting Fraudulent Behavior on Crowdfunding Platforms: The Role of Linguistic and Content-Based Cues in Static and Dynamic Contexts. Journal of Management Information Systems, 33, 421-455.\n\nSmith, M., & Taffler, R. J. (2000). The chairman’s statement: A content analysis of discretionary narrative disclosures. Accounting, Auditing, and Accountability, 13(5), 624-646.\n\nSpathis, C. T. (2002), Detecting false financial statements using published data: Some evidence from Greece. Managerial Auditing Journal, 17(4), 179-191.\n\nTennyson, B. M., Ingram, R. W., & Dugan, M. T. (1990), Assessing the information content of narrative disclosures in explaining bankruptcy. Journal of Business Finance & Accounting, 17, 391-410.\n\nTinoco, M. H., & Wilson, N. (2013), Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394-419.\n\nWang, Y.B., & Xu, W. (2018), Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud, Decision Support Systems, 105, 87-95.\n\nYekini, L. S., Wisniewski, T. P., & Millo, Y. (2016), Market reaction to the positiveness of annual report narratives. The British Accounting Review, 48(4), 415-430.\n\nZainudin, E. F., & Hashim, H.A. (2016), Detecting fraudulent financial reporting using financial ratio. Contemporary Accounting Research, 28, 17-82.\n\nZhou, L., Burgoon, J. K., Twitchell, D. P., Nunamaker, J. F., & Twitchell, D. P. (2004), Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communication. Group Decision and Negotiation, 13, 81-106.\n\nZmijewsk, M. (1984), Method Logistical issue related to the estimation of financial distress prediction model. Journal of Accounting Research, 22, 59-82. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900324 en_US
