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題名 運用資料及文字探勘探討不同市場營運概況文字敘述及財務表現之一致性
Using data and text mining to explore for consistencies between narrative disclosures and financial performance in different markets
作者 江韋達
Chiang, Danny Wei Ta
貢獻者 周濟群
Chou, Chi Chun
江韋達
Chiang, Danny Wei Ta
關鍵詞 文字探勘
K-Means分群
文字敘述
營運概況
Text Mining
K-Means
Narrative Disclosures
MD&A
日期 2011
上傳時間 30-Oct-2012 11:43:25 (UTC+8)
摘要 本研究使用TFIDF文字探勘技術分析樣本公司年度財務報告裡面的重要非量化資訊，與三項量化財務比率比較，欲探討公司年報在不同市場裡文字敘述與財務表現之一致性。研究結果顯示，根據從2003年至2010年上市半導體公司之年度報告，美國公司的年報較會對財務表現做出誇大的文字敘述，本研究亦發現在文字敘述上，市場較不成熟的中國公司所發布之年報較偏向低估他們的財務表現。
This study presented a way to extract useful information out of unstructured qualitative textual data with the use of the TFIDF text mining technique, which was used to help us explore for consistencies between financial performance in the form of quantitative financial ratios and qualitative narrative disclosures in the annual report between countries with different levels of market development. The results show that, based on listed semiconductor companies` annual reports between 2003 to 2010, companies in the United States have a high tendency to exaggerate and overstate about their performance in the MD&A, while less developed markets such as China turned out to have the lowest tendency to exaggerate and was more likely to understate about its performance in their Director`s Report.
參考文獻 Amir, E. and B. Lev. 1996. Value relevance of nonfinancial information: The wireless telecommunications industry. Journal of Accounting and Economics 22(1-3): 3-30.
Belsky, G. 2012. Why text mining may be the next big thing. TIME Business. Retrieved on May 4, 2012 from http://business.time.com/2012/03/20/why-text-mining-may-be-the-next-big-thing.
Bhattacharyya, D., P. Das, D. Ganguly, K. Mitra, P. Das, S. K. Bandyopadhyay and T. Kim. 2008. Unstructured document categorization: A study. International Journal of Signal Processing, Image Processing and Pattern Recognition (IJSIP) 1(1): 55-62.
David, C. 2001. Mythmaking in annual reports. Journal Of Business And Technical Communication 15(2): 195-222.
Deng, K. and A. W. Moore. 1998. On the greediness of feature selection algorithms. International Conference of Machine Learning (ICML `98). Retrieved on April 29, 2012 from http://www.cs.cmu.edu/~kdeng/thesis/feature.pdf
Dias, W., and R. Matias-Fonseca. 2010. The language of annual reports as an indicator of the organizations’ financial situation. International Review of Business Research Papers 6(5): 206-215.
Glassman, C. A., U.S. Securities and Exchange Commission (SEC). 1987. MD&A Report Card. Journal of Accountancy. Retrieved on April 30, 2012 from http://www.journalofaccountancy.com/Issues/2006/Aug/MdAReportCard.htm.
Hearst, M. 1999. Untangling text data mining. Proceedings of the 37th Annual Meeting of the ACL, University of Maryland (invited paper). Retrieved on May 1, 2012 from http://www.ai.mit.edu/people/jimmylin/papers/Hearst99a.pdf.
Herreman, I. M. and J. Ryans. 1995. The case for better measurement and reporting of marketing performance. Business Horizons 38(5): 51-60.
Hildebrandt, H. W., and Snyder, R. D. 1981. The pollyanna hypothesis in business writing: Initial results, suggestions for research. Journal of Business Communication 18(1): 5-15.
International Organization of Securities Commissions (IOSCO). February 2003. General Principles Regarding Disclosure of Management’s Discussion and Analysis of Financial Condition and Results of Operations. Report of the Technical Committee of the International Organization of Securities Commissions. Retrieved on April 25, 2012 from http://www.sec.gov/about/offices/oia/oia_corpfin/genprinc.pdf.
Karlsson, J., B. Back, H. Vanharanta, and A. Visa. 2001. Financial benchmarking of telecommunications companies. Turku Centre for Computer Science.
Kloptchenko, A. 2003. Text mining based on the prototype matching method. TUCS Dissertations 47. Turku Centre for Computer Science.
Kloptchenko, A., T. Eklund, J. Karlsson, B. Back, H. Vanharanta, and A. Visa. 2004. Combining data and text mining techniques for analysing financial reports. Intelligent Systems in Accounting, Finance & Management 12(1): 20-28.
Kohut, G. F., and A. H. Segars. 1992. The president`s letter to stockholders: An examination of corporate communication strategy. Journal of Business Communication 29(1): 7-21.
Lehtinen, J. 1996. Financial ratios in an international comparison. Acta Wasaensia, Vaasa.
Li, F. 2008. Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics 45(2-3): 221-247.
McDonald, D. 2012. The value and benefits of text mining. Journal Information Systems Committee. Retrieved on May 4, 2012 from http://www.jisc.ac.uk/media/documents/publications/reports/2012/value-text-mining.pdf.
Pava, M. L., and M. Epstein. 1993. MD&A as an investment tool: User beware! Journal of Accountancy 75(3): 51-53.
Petersen, M. A. 2004. Information : hard and soft. Northwestern University. Unpublished.
Qiu, X. Y., P. Srinivasan and N. Street. 2006. Exploring the forecasting potential of company annual reports. Proceedings of the American Society for Information Science and Technology 43(1): 1-15.
Qu, W. and P. Leung. 2006, Cultural impact on Chinese corporate disclosure - a corporate governance perspective. Managerial auditing journal 21(3): 241-264.
Rogers, R. & Grant, J. 1997. An empirical investigation of the relevance of the financial reporting process to financial analysts. Unpulished.
Roiger, R. J. and M.W. Geatz. 2003. Data mining: a tutorial-based primer (international edition). Pearson Education, USA.
Scott, S. and S. Matwin. 1999. Feature engineering for text classification. Proceedings of ICML-99, 16th. International Conference on Machine Learning: 379–388.
Sporleder, C. 2007. Text mining for cultural heritage data from natural history domain. Computational Linguistics Saarland University Retrieved on May 2, 2012 from http://www.coli.uni-saarland.de/~csporled/papers/ed_07.pdf.
Thiprungsri S. and M. Vasarhelyi. 2011. Cluster analysis for anomaly detection in accounting data: an audit approach. The International Journal of Digital Accounting Research 11.
Tseng, H., P. Chang, G. Andrew, D. Jurafsky and Christopher Manning. 2005. A conditional random field word segmenter. Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing.
U.S. Securities and Exchange Commission (SEC). 1987. Concept release on management`s discussion and analysis of financial condition and results of operations. Securities Act Release No. 6711.Retrieved on April 11, 2012 from http://www.sec.gov/rules/other/33-8056.htm.
U.S. Securities and Exchange Commission (SEC). 2003. Disclosure in management`s discussion and analysis about off-balance sheet arrangements and aggregate contractual obligations. Securities Act Release No. 33-8182. Retrieved on April 11, 2012 from http://www.sec.gov/rules/final/33-8182.htm.
Van der Laan Smith, J., A. Adhikari, R. H. Tondkar and R. L. Andrews. 2010. The impact of corporate social disclosure on investment behavior: A cross-national study. Journal of Accounting and Public Policy 29:177-192.
Vanneschi1, L., A. Farinaccio1, G. Mauri1, M. Antoniotti, P. Provero and M. Giacobini. 2011. A comparison of machine learning techniques for survival prediction in breast cancer. BioData Mining 4: 1-12
Ville, B. D. 2006. Text mining with “holographic” decision tree ensembles. Proceedings of the Thirty-first Annual SAS Users Group International Conference.
Visa, A., J. Toivonen, P. Ruokonen, H. Vanharanta, and B.Back, 2000. Knowledge discovery from text documents based on paragraph maps. Proceedings from the 33rd Hawaii International Conference on System Sciences 2: 1-9.
描述 碩士
國立政治大學
會計研究所
99353057
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099353057
資料類型 thesis
dc.contributor.advisor 周濟群zh_TW
dc.contributor.advisor Chou, Chi Chunen_US
dc.contributor.author (Authors) 江韋達zh_TW
dc.contributor.author (Authors) Chiang, Danny Wei Taen_US
dc.creator (作者) 江韋達zh_TW
dc.creator (作者) Chiang, Danny Wei Taen_US
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 11:43:25 (UTC+8)-
dc.date.available 30-Oct-2012 11:43:25 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 11:43:25 (UTC+8)-
dc.identifier (Other Identifiers) G0099353057en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54760-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 會計研究所zh_TW
dc.description (描述) 99353057zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 本研究使用TFIDF文字探勘技術分析樣本公司年度財務報告裡面的重要非量化資訊，與三項量化財務比率比較，欲探討公司年報在不同市場裡文字敘述與財務表現之一致性。研究結果顯示，根據從2003年至2010年上市半導體公司之年度報告，美國公司的年報較會對財務表現做出誇大的文字敘述，本研究亦發現在文字敘述上，市場較不成熟的中國公司所發布之年報較偏向低估他們的財務表現。zh_TW
dc.description.abstract (摘要) This study presented a way to extract useful information out of unstructured qualitative textual data with the use of the TFIDF text mining technique, which was used to help us explore for consistencies between financial performance in the form of quantitative financial ratios and qualitative narrative disclosures in the annual report between countries with different levels of market development. The results show that, based on listed semiconductor companies` annual reports between 2003 to 2010, companies in the United States have a high tendency to exaggerate and overstate about their performance in the MD&A, while less developed markets such as China turned out to have the lowest tendency to exaggerate and was more likely to understate about its performance in their Director`s Report.en_US
dc.description.tableofcontents CHAPTER 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Methodology Overview 4
1.4 Contributions 5
CHAPTER 2 Literature Review 6
2.1 Soft and Hard Information 6
2.2 Narrative Disclosures 6
2.3 Data Mining 9
2.4 Text Mining 10
2.4.1 Related Literature 10
2.4.2 Distinction Between Data Mining and Text Mining 14
2.4.3 Text Segmentation 14
2.4.4 Vector Space Model 16
CHAPTER 3 Methodology 19
3.1 Data Selection and Collection 20
3.1.1 Quantitative Data 20
3.1.2 Qualitative Data 21
3.2 Data Treatment 25
3.2.1 Quantitative Data 25
3.2.2 Qualitative Data 26
3.3 Data Analysis 28
CHAPTER 4 Results and Findings 30
4.1 Data Selection and Collection 30
4.1.1 Quantitative Data 30
4.1.2 Qualitative Data 30
4.2 Data Treatment 30
4.2.1 Quantitative Data 30
4.2.2 Qualitative Data 35
4.3 Data Analysis 46
CHAPTER 5 Summary and Conclusion 51
5.1 Summary 51
5.2 Limitations 52
5.3 Future Work 52
CHAPTER 6 References 54		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099353057en_US
dc.subject (關鍵詞) 文字探勘zh_TW
dc.subject (關鍵詞) K-Means分群zh_TW
dc.subject (關鍵詞) 文字敘述zh_TW
dc.subject (關鍵詞) 營運概況zh_TW
dc.subject (關鍵詞) Text Miningen_US
dc.subject (關鍵詞) K-Meansen_US
dc.subject (關鍵詞) Narrative Disclosuresen_US
dc.subject (關鍵詞) MD&Aen_US
dc.title (題名) 運用資料及文字探勘探討不同市場營運概況文字敘述及財務表現之一致性zh_TW
dc.title (題名) Using data and text mining to explore for consistencies between narrative disclosures and financial performance in different marketsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Amir, E. and B. Lev. 1996. Value relevance of nonfinancial information: The wireless telecommunications industry. Journal of Accounting and Economics 22(1-3): 3-30.
Belsky, G. 2012. Why text mining may be the next big thing. TIME Business. Retrieved on May 4, 2012 from http://business.time.com/2012/03/20/why-text-mining-may-be-the-next-big-thing.
Bhattacharyya, D., P. Das, D. Ganguly, K. Mitra, P. Das, S. K. Bandyopadhyay and T. Kim. 2008. Unstructured document categorization: A study. International Journal of Signal Processing, Image Processing and Pattern Recognition (IJSIP) 1(1): 55-62.
David, C. 2001. Mythmaking in annual reports. Journal Of Business And Technical Communication 15(2): 195-222.
Deng, K. and A. W. Moore. 1998. On the greediness of feature selection algorithms. International Conference of Machine Learning (ICML `98). Retrieved on April 29, 2012 from http://www.cs.cmu.edu/~kdeng/thesis/feature.pdf
Dias, W., and R. Matias-Fonseca. 2010. The language of annual reports as an indicator of the organizations’ financial situation. International Review of Business Research Papers 6(5): 206-215.
Glassman, C. A., U.S. Securities and Exchange Commission (SEC). 1987. MD&A Report Card. Journal of Accountancy. Retrieved on April 30, 2012 from http://www.journalofaccountancy.com/Issues/2006/Aug/MdAReportCard.htm.
Hearst, M. 1999. Untangling text data mining. Proceedings of the 37th Annual Meeting of the ACL, University of Maryland (invited paper). Retrieved on May 1, 2012 from http://www.ai.mit.edu/people/jimmylin/papers/Hearst99a.pdf.
Herreman, I. M. and J. Ryans. 1995. The case for better measurement and reporting of marketing performance. Business Horizons 38(5): 51-60.
Hildebrandt, H. W., and Snyder, R. D. 1981. The pollyanna hypothesis in business writing: Initial results, suggestions for research. Journal of Business Communication 18(1): 5-15.
International Organization of Securities Commissions (IOSCO). February 2003. General Principles Regarding Disclosure of Management’s Discussion and Analysis of Financial Condition and Results of Operations. Report of the Technical Committee of the International Organization of Securities Commissions. Retrieved on April 25, 2012 from http://www.sec.gov/about/offices/oia/oia_corpfin/genprinc.pdf.
Karlsson, J., B. Back, H. Vanharanta, and A. Visa. 2001. Financial benchmarking of telecommunications companies. Turku Centre for Computer Science.
Kloptchenko, A. 2003. Text mining based on the prototype matching method. TUCS Dissertations 47. Turku Centre for Computer Science.
Kloptchenko, A., T. Eklund, J. Karlsson, B. Back, H. Vanharanta, and A. Visa. 2004. Combining data and text mining techniques for analysing financial reports. Intelligent Systems in Accounting, Finance & Management 12(1): 20-28.
Kohut, G. F., and A. H. Segars. 1992. The president`s letter to stockholders: An examination of corporate communication strategy. Journal of Business Communication 29(1): 7-21.
Lehtinen, J. 1996. Financial ratios in an international comparison. Acta Wasaensia, Vaasa.
Li, F. 2008. Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics 45(2-3): 221-247.
McDonald, D. 2012. The value and benefits of text mining. Journal Information Systems Committee. Retrieved on May 4, 2012 from http://www.jisc.ac.uk/media/documents/publications/reports/2012/value-text-mining.pdf.
Pava, M. L., and M. Epstein. 1993. MD&A as an investment tool: User beware! Journal of Accountancy 75(3): 51-53.
Petersen, M. A. 2004. Information : hard and soft. Northwestern University. Unpublished.
Qiu, X. Y., P. Srinivasan and N. Street. 2006. Exploring the forecasting potential of company annual reports. Proceedings of the American Society for Information Science and Technology 43(1): 1-15.
Qu, W. and P. Leung. 2006, Cultural impact on Chinese corporate disclosure - a corporate governance perspective. Managerial auditing journal 21(3): 241-264.
Rogers, R. & Grant, J. 1997. An empirical investigation of the relevance of the financial reporting process to financial analysts. Unpulished.
Roiger, R. J. and M.W. Geatz. 2003. Data mining: a tutorial-based primer (international edition). Pearson Education, USA.
Scott, S. and S. Matwin. 1999. Feature engineering for text classification. Proceedings of ICML-99, 16th. International Conference on Machine Learning: 379–388.
Sporleder, C. 2007. Text mining for cultural heritage data from natural history domain. Computational Linguistics Saarland University Retrieved on May 2, 2012 from http://www.coli.uni-saarland.de/~csporled/papers/ed_07.pdf.
Thiprungsri S. and M. Vasarhelyi. 2011. Cluster analysis for anomaly detection in accounting data: an audit approach. The International Journal of Digital Accounting Research 11.
Tseng, H., P. Chang, G. Andrew, D. Jurafsky and Christopher Manning. 2005. A conditional random field word segmenter. Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing.
U.S. Securities and Exchange Commission (SEC). 1987. Concept release on management`s discussion and analysis of financial condition and results of operations. Securities Act Release No. 6711.Retrieved on April 11, 2012 from http://www.sec.gov/rules/other/33-8056.htm.
U.S. Securities and Exchange Commission (SEC). 2003. Disclosure in management`s discussion and analysis about off-balance sheet arrangements and aggregate contractual obligations. Securities Act Release No. 33-8182. Retrieved on April 11, 2012 from http://www.sec.gov/rules/final/33-8182.htm.
Van der Laan Smith, J., A. Adhikari, R. H. Tondkar and R. L. Andrews. 2010. The impact of corporate social disclosure on investment behavior: A cross-national study. Journal of Accounting and Public Policy 29:177-192.
Vanneschi1, L., A. Farinaccio1, G. Mauri1, M. Antoniotti, P. Provero and M. Giacobini. 2011. A comparison of machine learning techniques for survival prediction in breast cancer. BioData Mining 4: 1-12
Ville, B. D. 2006. Text mining with “holographic” decision tree ensembles. Proceedings of the Thirty-first Annual SAS Users Group International Conference.
Visa, A., J. Toivonen, P. Ruokonen, H. Vanharanta, and B.Back, 2000. Knowledge discovery from text documents based on paragraph maps. Proceedings from the 33rd Hawaii International Conference on System Sciences 2: 1-9.		zh_TW