請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74631
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林嬋娟 | |
dc.contributor.author | Yi-Hsin Liao | en |
dc.contributor.author | 廖宜心 | zh_TW |
dc.date.accessioned | 2021-06-17T08:46:47Z | - |
dc.date.available | 2024-08-07 | |
dc.date.copyright | 2019-08-07 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-06 | |
dc.identifier.citation | 吳清在、邱正仁與鄭莉,2011,財務危機公司內部治理對會計師簽發繼續經營疑慮意見的影響:台灣上市公司之實證研究,臺大管理論叢,第21卷第2期:187-217頁。
李建然、陳政芳與李啟華,2003,董監事持股集中度與會計師獨立性-對會計師出具繼續經營疑慮查核意見之影響,當代會計,第4卷:213-231頁。 李建然與陳政芳,2004,審計客戶重要性與盈餘管理:以五大事務所組別為觀察標的,會計評論,第38期:59-80頁。 李德冠、戴怡蕙與林嬋娟,2016,將繼續經營假設存有重大疑慮公司列為全額交割股的影響,證券市場發展季刊,第28卷第3期:49-92頁。 官月緞與郭子建,2011,客戶重要性、非審計服務與會計師任期對審計品質之影響,當代會計,第12卷:1-30頁。 許永聲、王志成與劉政淮,2011,上市櫃公司首次出現繼續經營疑慮之後動態分析,證券市場發展季刊,第23卷第4期:27-62頁。 黃娟娟,2012,公司年報文字探勘與財務預警資訊內涵,逢甲大學商學博士論 文,未出版。 楊炎杰與官月緞,2006,客戶重要性與非審計服務是否影響審計品質?Enron後的觀察,會計評論,第43期:27-61頁。 劉嘉雯與王泰昌,2005,繼續經營有重大疑慮審計意見:第33號審計準則公報之影響,管理學報,第22卷第4期:525-549頁。 劉嘉雯與王泰昌,2008,會計師任期與審計品質之關連性研究,管理評論,第27卷第4期,1-28頁。 盧鈺欣、林昱成與林育伶,2016,資料探勘技術在繼續經營疑慮意見診斷模型之應用,會計評論,第63期:77-108頁。 Alles, M. G. 2015. Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession. Accounting Horizons 29 (2): 439-449. Archer, K. J., and R. V. Kimes. 2008. Empirical characterization of random forest variable importance measures. Computational Statistics & Data Analysis 52: 2249-2260. Behn, B. K., S. E. Kaplan, and K. R. Krumwiede. 2001. Further evidence on the auditor’s going-concern report: The influence of management plans. Auditing: A Journal of Practice & Theory 20 (1): 13-28. Bell, T. B., and R. H. Tabor. 1991. Empirical analysis of audit uncertainty qualifications. Journal of Accounting Research 29 (2): 350-370. Breiman, L. 1996. Bagging Predictors. Machine Learning 24: 123-140. Breiman, L. 2001. Random Forests. Machine Learning 45: 5-32. Cao, M., R. Chychyla, and T. Stewart. 2015. Big data analytics in financial statement audits. Accounting Horizons 29 (2): 423-429. Carcello, J. V., and Z. V. Palmrose. 1994. Auditor ltigation and modified reporting on bankrupt clients. Journal of Accounting Research 32: 1-30. Carcello, J. V., and T. L. Neal. 2000. Audit committee composition and auditor reporting. The Accounting Review 75 (4): 453-467. Carcello, J. V., and T. L. Neal. 2003. Audit committee characteristics and auditor dismissals following 'new' going-concern reports. The Accounting Review 78 (1): 95-117. Carey, P. J., M. A. Geiger, and B. T. O'connell. 2008. Costs associated with going-concern-modified audit opinions: An analysis of the Australian audit market. A Journal of Accounting Finance and Business Studies 44 (1): 61-81. Carey, P. J., and R. Simnett. 2006. Audit partner tenure and audit quality. The Accounting Review 81 (3): 653-676. Chang, C. C., and C. J. Lin. 2011. LIBSVM: A library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2 (3): 1-27. Chen, K. C. W., and B. K. Church. 1992. Default on debt obligations and the issuance of going concern opinions. Auditing: A Journal of Practice & Theory 11 (2): 30-49. Chen, H., P. De, Y. J. Hu, and B. H. Hwang. 2014. Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies 27 (5): 1367-1403. Citron, D. B., and R. J. Taffler. 1992. The audit report under going concern uncertainties: An empirical analysis. Accounting and Business Research 22 (88): 337-345. Davis, A. K., and I. Tama-Sweet. 2012. Managers’ use of language across alternative disclosure outlets: Earnings press releases versus MD&A. Contemporary Accounting Research 29: 804-837. Davis, A. K., J. M. Piger, and L. M. Sedor. 2012. Beyond the numbers: Measuring the information content of earnings press release language. Contemporary Accounting Research 29: 845-868. DeFond, M. L., K. Raghunandan,and R. K. Subramanyam. 2002. Do non-audit service fees impair auditor independence? Evidence from going-concern audit opinions. Journal of Accounting Research 40 (4): 1247-1274. Dopuch, N., R. W. Holthausen, and R. W. Leftwich. 1996. Abnormal stock returns associated with media disclosures of 'subject to' qualified audit. Journal of Accounting and Economics 8: 93-117. Dopuch, N., R. W. Holthausen, and R. W. Leftwich. 1987. Predicting audit qualifications with financial and market variables. The Accounting Review 62 (3): 431-454. Feldmann, D., and W. J. Read. 2013. Going-concern audit opinions for bankrupt companies - impact of credit rating. Managerial Auditing Journal 28 (4): 345-363. Fernández-Delgado, M., E. Cernadas, and S. Barro. 2014. Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research 15: 3133-3181. Firth, M. 1980. A note on the impact of audit qualifications on lending and credit decisions. Journal of Banking and Finance 4: 257-267. Geiger, M. A., and K. Raghunandan. 2002. Auditor tenure and audit reporting failures. Auditing: A Journal of Practice & Theory 21 (1): 67-78. Goo, Y. J. J., D. J. Chi, Z. D. Shen. 2016. Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques. SpringerPlus 5 (539). Grzymala-Busse, J. W. and M. Hu. 2001. A comparison of several approaches to missing attribute values in data mining. Rough Sets and Current Trends in Computing: 378-385. Hansen, V. J., J. B. McDonald, and J. D. Stice. 1992. Artificial intelligence and generalized qualitative-response models: An empirical test on two audit decision-making domains. Decision Sciences 23 (3): 708-723. Holton, C. 2009. Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem. Decision Support Systems 46 (4): 853-864. Hotho, A., A. Nurnberger,and G. Paaß. 2005. A brief survey of text mining. J Lang Technol Comput Linguist 20 (1): 19-62. Huang, X., S. H. Teoh, and Y. Zhang. 2014. Tone Management. The Accounting Review 89 (3): 1083-1113. Jones, F. L. 1996. The information content of the auditor's going concern evaluation. Journal of Accounting and Public Policy 15 (1): 1-27. Kida, T. 1980. An investigation into auditors’ continuity and related qualification judgments. Journal of Accounting Research 18 (2): 506-523. Kirkos, E., C. Spathis, and Y. Manolopoulos. 2008. Support vector machines, Decision Trees and Neural Networks for auditor selection. Journal of Computational Methods in Sciences and Engineering 8: 213-224. Kleinman, G., and A. Anandarajan. 1999. The usefulness of off-balance sheet variables as predictors of auditors’ going concern opinions: An empirical analysis. Managerial Auditing Journal 14 (6): 273-285. Knechel, R. W., and A.Vanstraelen. 2007. The relationship between auditor tenure and audit quality implied by going concern opinions. Auditing: A Journal of Practice & Theory 26 (1): 113-131. Koh, C. H., and C. K. Low. 2004. Going concern prediction using data mining techniques. Managerial Auditing Journal 19 (3): 462-476. Koh, C. H., and R. M. Brown. 1991. Probit prediction of going and non-going concerns. Managerial Auditing Journal 6 (3): 18-23. Koh, C. H., and S. S. Tan. 1999. A neural network approach to the prediction of going concern status. Accounting and Business Research 29 (3): 211-216. Koskivaara, E. and B. Back. 2007. Artificial neural network assistant (ANNA) for continuous auditing and monitoring of financial data. Journal of Emerging Technologies in Accounting 4 (1): 29-45. Lacher, R. C., P. K. Coats, S. C. Sharma, and L. F. Fant. 1995. A neural network for classifying the financial health of a firm. European Journal of Operational Research 85: 53-65. Lai, S. C., C. Lin, H. C. Li, and F. H. Wu. 2009. The information contents of modified unqualified audit opinions under the control of concurrent information: The case of Taiwan. Journal of Accounting and Corporate Governance 6 (1): 31-56. LaSalle, E. R., and A. Anandarajan. 1996. Auditors' views on the type of audit report issued to entities with going concern uncertainties. Accounting Horizons 10 (2): 51-72. Lee, T. S., and Y. H. Yeh. 2004. Corporate governance and financial distress: Evidence from Taiwan. Corporate Governance: An International Review 12 (3): 378-388. Lenard, J. M., P. Alam, and G. R. Madey. 1995. The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision. Decision Sciences 26 (2): 209-227. Loudder, M. L., I. K. Khurana, R. B. Sawyers, C. Cordery, J. Carol, L. Jordan, and W. Robert. 1992. The information content of audit qualifications. A Journal of Practice and Theory: 69-80. Loughran, T., and B. McDonald. 2016. Textual analysis in accounting and finance: A survey. Journal of Accounting Research 54 (4): 1187-1230. Loughran, T., and B. McDonald. 2011. When is a liability not a liability? Textual analysis, Dictionaries, and 10-Ks. The Journal of Finance LXVI (1): 35-65. Lu, Y. C., C. H. Shen, and Y. C. Wei. 2013. Revisiting early warning signals of corporate credit default using linguistic analysis. Pacific-Basin Finance Journal 24: 1-21. Lu, Y. C., Y. C. Wei., and T. Y. Chang. 2015. The effects and applicability of financial media reports on corporate default ratings. International Review of Economics and Finance 36: 69-87. Martens, D., L. Bruynseels, B. Baesens, M. Willekens, and J. Vanthienen. 2008. Predicting going concern opinion with data mining. Decision Support Systems 45: 765-777. McAfee, A., and E. Brynjolfsson. 2012. Big data: the management revolution. Harvard Business Review 90: 60-66. McKeown, J. C., J. F. Mutchler, and W. Hopwood. 1991. Towards an explanation of auditor failure to modify the audit opinions of bankrupt companies. Auditing: A Journal of Practice & Theory 10: 1-13. Mutchler, J. F. 1984. Auditors’ perceptions of the going-concern opinion decision. Auditing: A Journal of Practice & Theory 3 (2): 17-29. Mutchler, J. F. 1985. A multivariate analysis of the auditor's going-concern opinion decision. Journal of Accounting Research 23 (2): 668-682. Mutchler, J. F., W. Hopwood, and J. McKeown., 1997. The influence of contrary information and mitigating factors in audit opinion decisions on bankrupt companies. Journal of Accounting Research 35 (2): 295-310. Reynolds, K. J., and J. R. Francis, 2001. Does size matter? The influence of clients on office-level auditor reporting decisions. Journal of Accounting and Economics 30 (3): 375-400. Rogers, J. L., A. V. Buskirk, and S. L. C. Zechman. 2011. Disclosure tone and shareholder litigation. The Accounting Review 86 (6): 2155-2183. Safavian, S. R., and D. Landgrebe. 1991. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics 21 (3): 660-674. Salehi, M., and F. Z. Fard. 2013. Data mining approach to prediction of going concern using Classification and Regression Tree. Global Journal of Management and Business Research Accounting and Auditing 13 (3): 25-29. Shirata, C. Y., and M. Sakagami. 2008. An analysis of the 'Going Concern Assumption' : Text mining from Japanese financial reports. Journal of Emerging Technologies in Accounting 5: 1-16. St.Pierre, K., and J. A. Anderson. 1984. An analysis of factors associated with lawsuits against public accountants. The Accounting Review 59 (2): 242-263. Sung, T. K., N. Chang, and G. Lee. 1999. Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems 16 (1): 63-85. Suykens, J. A. K. and J. Vandewalle. 1999. Least squares support vector machine classifiers. Neural Processing Letters 9: 293–300. Tetlock, P. C. 2007. Giving content to investor sentiment: The role of media in the stock market. Journal of Finance: 1139-1168. Tetlock, P. C., M. Saar-Tsechansky, and S. Macskassy. 2008. More than words: Quantifying language to measure firms' fundamentals. Journal of Finance 63 (3): 1437-1467. Udo, G. 1993. Neural network performance on the bankruptcy classification problem. Computers and Industrial Engineering 25 (1-4): 377-380. Wu, C. H., and C. J. Lin. 2017. The impact of media coverage on investor trading behavior and stock returns. Pacific-Basin Finance Journal 43: 151-172. Yeh, C. C., D. J. Chi, and Y. R. Lin. 2014. Going-concern prediction using hybrid random forests and rough set approach. Information Sciences 254: 98-110. Yoon, K., L. Hoogduin, and L. Zhang. 2015. Big data as complementary audit evidence. Accounting Horizons 29 (2): 431-438. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74631 | - |
dc.description.abstract | 本研究以資料探勘技術中的Random Forest、Support Vector Machine和傳統統計模型的Logistic Regression建立繼續經營能力評估模型,並以情緒詞彙分析(sentiment word analysis)剖析媒體內容,探討新聞媒體對於會計師出具查核意見決策的影響。
實證結果發現,前述三種方法皆是在選用關鍵特徵變數後預測表現達到最佳。Random Forest不論在何種變數組合下,預測績效皆較其他方法準確,其中Recall平均值最高約為0.92,代表在所有確實被出具繼續經營有重大疑慮意見之公司中,Random Forest之預測準確度超過九成。 然而從Random Forest的特徵變數重要性評估結果可以發現,會計師在衡量受查公司繼續經營能力時,主要還是以財務和營運資訊為判斷依據,公司治理、審計品質和媒體情緒變數的重要性都相對較低。從模型的預測結果亦顯示,不論是否將樣本限縮在第一年被出具繼續經營疑慮意見之公司,媒體情緒對於會計師查核意見的決策皆無顯著影響。 推論原因可能為國內會計師在出具查核意見時,僅以受查公司內部提供的財務和營運資訊為主要考量,抑或是媒體新聞所內含的消息已經可以從受查公司給予的資訊中取得,故外界新聞報導的增額資訊價值較低。 | zh_TW |
dc.description.abstract | This study applies both data mining techniques(Random Forest and Support Vector Machine)and the traditional statistical method(i.e., Logistic Regression)to construct a going concern diagnostic model. This study also tries to assess the impact of media coverage on auditors’ going concern opinion by using sentiment word analysis.
The empirical results show that all methods above achieve the best predictive performance after selecting key features from 72 variables. Among the three methods, Random Forest has the highest Recall value, about 0.92, indicating among all companies receiving going concern opinion, the prediction accuracy of Random Forest is over 90%. However, the empirical results from Random Forest show that financial and operational variables remain the most important factors considered by auditors while assessing the likelihood of going concern. The importance of variables related to corporate governance, audit quality, and media sentiment, however, is relatively low. Specifically, this study finds that the media information has no significant effect on the issuance of going concern audit opinion, whether samples are confined to the companies receiving going concern opinion in the first year or not. In sum, these results may suggest that media coverage adds little incremental value beyond the operating and financial information already considered by auditors in making going concern opinion decision. One possible reason might be that most of media content has been incorporated in the operating and financial information disclosed by clients. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:46:47Z (GMT). No. of bitstreams: 1 ntu-108-R06722033-1.pdf: 1864216 bytes, checksum: af006a137cb8ed9dd6eb28819f70313c (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 致謝 i
中文摘要 ii ABSTRACT iii 目錄 iv 表目錄 v 圖目錄 vi 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機與目的 5 第二章 文獻探討 8 第一節 影響會計師出具繼續經營有重大疑慮意見之因素 8 第二節 資料探勘技術在繼續經營能力評估模型上之應用 16 第三節 文字探勘技術在會計財金領域上之應用 24 第三章 研究設計 32 第一節 研究流程 32 第二節 文字探勘技術 34 第三節 樣本選取 37 第四章 實證結果 42 第一節 特徵變數評估結果 42 第二節 敘述性統計 45 第三節 分類預測模型績效評估 52 第四節 額外測試 56 第五章 研究結論與限制 61 參考文獻 64 附錄 70 附錄一 財務危機與非財務危機特徵字詞庫(部分) 70 附錄二 變數定義表 71 附錄三 變數重要度彙整表 75 | |
dc.language.iso | zh-TW | |
dc.title | 資料探勘技術於繼續經營能力評估模型之應用-媒體情緒之分析 | zh_TW |
dc.title | Using Data Mining Techniques for Going Concern Prediction-Sentiment Analysis of Media | 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 | 繼續經營疑慮意見,隨機森林,支持向量機,情緒詞彙分析, | zh_TW |
dc.subject.keyword | going concern opinion,Random Forest,Support Vector Machine,sentiment word analysis, | en |
dc.relation.page | 76 | |
dc.identifier.doi | 10.6342/NTU201902599 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-06 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 1.82 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。