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題名 探索美國財務報表的主觀性詞彙與盈餘的關聯性:意見分析之應用
Exploring the relationships between annual earnings and subjective expressions in US financial statements: opinion analysis applications作者 陳建良
Chen, Chien Liang貢獻者 劉昭麟<br>張元晨
Liu, Chao Lin<br>Chang, Yuan Chen
陳建良
Chen, Chien Liang關鍵詞 意見探勘
自然語言處理
語意分析
財務報表文字探勘
資訊擷取
opinion mining
natural language processing
sentiment analysis
financial text mining
information extraction日期 2010 上傳時間 4-Sep-2013 17:10:48 (UTC+8) 摘要 財務報表中的主觀性詞彙往往影響市場中的參與者對於報導公司價值和獲利能力衡量的決策判斷。因此，公司的管理階層往往有高度的動機小心謹慎的選擇用詞以隱藏負面的消息而宣揚正面的消息。然而使用人工方式從文字量極大的財務報表挖掘有用的資訊往往不可行，因此本研究採用人工智慧方法驗證美國財務報表中的主觀性多字詞 (subjective MWEs) 和公司的財務狀況是否具有關聯性。多字詞模型往往比傳統的單字詞模型更能掌握句子中的語意情境，因此本研究應用條件隨機域模型 (conditional random field) 辨識多字詞形式的意見樣式。另外，本研究的實證結果發現一些跡象可以印證一般人對於財務報表的文字揭露往往與真實的財務數字存在有落差的印象；更發現在負向的盈餘變化情況下，公司管理階層通常輕描淡寫當下的短拙卻堅定地承諾璀璨的未來。
Subjective assertions in financial statements influence the judgments of market participants when they assess the value and profitability of the reporting corporations. Hence, the managements of corporations may attempt to conceal the negative and to accentuate the positive with "prudent" wording. To excavate this accounting phenomenon hidden behind financial statements, we designed an artificial intelligence based strategy to investigate the linkage between financial status measured by annual earnings and subjective multi-word expressions (MWEs). We applied the conditional random field (CRF) models to identify opinion patterns in the form of MWEs, and our approach outperformed previous work employing unigram models. Moreover, our novel algorithms take the lead to discover the evidences that support the common belief that there are inconsistencies between the implications of the written statements and the reality indicated by the figures in the financial statements. Unexpected negative earnings are often accompanied by ambiguous and mild statements and sometimes by promises of glorious future.參考文獻 [1] W. Antweiler and M. Z. Frank, “Is all that Talk just Noise? The Information Content of Internet Stock Message Boards,” Journal of Finance, 59(3), pp. 1259-1294, 2004.[2] Apache Lucene 3.0.0, http://lucene.apache.org/java/docs/index.html.[3] Automatic Statistical SEmantic Role Tagger-v0.14b (ASSERT), http://cemantix.org/assert.html.[4] Charniak Parser, http://www.cs.brown.edu/~ec/#software.[5] Y. Choi, C. Cardie, E. Riloff and S. Patwardhan, “Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns,” Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 355-362, 2005[6] M. J. Collins, Head-Driven Statistical Models for Natural Language Parsing, Ph.D. thesis, University of Pennsylvania, 1999.[7] Electronic Data Gathering, Analysis and Retrieval system (EDGAR), http://www.sec.gov/edgar.shtml.[8] FrameNet, http://framenet.icsi.berkeley.edu.[9] D. Gildea and D. Jurafsky, “Automatic Labeling of Semantic Role,” Computational Linguistics, 28(3), pp. 245-288, 2002.[10] W. H. Greene, Econometric Analysis, Pearson Prentice Hall, 2008.[11] Illinois Chunker, http://cogcomp.cs.illinois.edu/page/software.[12] S.-M. Kim and E. Hovy, “Identifying Opinion Holders for Question Answering in Opinion Texts,” Proceedings of AAAI Workshop on Question Answering in Restricted Domains, pp. 20-26, 2005.[13] J. D. Lafferty, A. McCallum and F. C. N. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proceedings of the International Conference on Machine Learning, pp. 282-289, 2001.[14] F. Li, “Do Stock Market Investors Understand The Risk Sentiment Of Corporate Annual Reports?” University of Michigan Working Paper, 2006.[15] D. Lin, “Automatic Retrieval and Clustering of Similar Words.” Proceedings of the International Conference on Computational Linguistics (COLING)), pp. 768-774, 1998.[16] LingPipe 3.9 sentence model, http://alias-i.com/lingpipe.[17] B. Liu, “Sentiment Analysis and Subjectivity,” Handbook of Natural Language Processing, N. Indurkhya and F. J. Damerau (editors), CRC press , Second Edition, 2010.[18] T. Loughran and B. McDonald, “When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks,” Journal of Finance, 66(1), pp. 67-97, 2011.[19] MAchine Learning for LanguagE Toolkit-2.0.6 (MALLET), http://mallet.cs.umass.edu.[20] C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2009.[21] Multi-Perspective Question Answering 2.0 (MPQA), http://www.cs.pitt.edu/mpqa.[22] B. Pang, L. Lee and S. Vaithyanathan, “Thumbs up? Sentiment Classification Using Machine Learning Techniques,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79-86, 2002.[23] F. Peng, F. Feng and A. McCallum, “Chinese Segmentation and New Word Detection using Conditional Random Fields,” Proceedings of the conference on Computational Linguistics, 2004.[24] R.W. Picard, E. Vyzas and J. Healey, “Toward Machine Emotional Intelligence: Analysis of Affective Physiological State,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), pp. 1175-1191, 2001.[25] S. Pradhan, W. Ward, K. Hacioglu, J. Martin and D. Jurafsky, “Shallow Semantic Parsing Using Support Vector Machines,” Proceedings of the Human Language Technology Conference/North American Chapter of the ACL, 2004.[26] L. A. Ramshaw and M. P. Marcus, “Text Chunking Using Transformation-based Learning,” Proceedings of the ACL Workshop on Very Large Corpora, pp 82–94, 1995.[27] E. Riloff and J. Wiebe, “Learning Extraction Patterns for Subjective Expressions,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 25-32, 2003.[28] J. Ronen and V. Yaari, Earnings Management: Emerging Insights in Theory, Practice, and Research, Springer-Verlag, 2008.[29] Standard & Poor’s Compustat Research Insight 8.4.1, http://www.compustat.com.[30] Stanford Dependencies manual, http://nlp.stanford.edu/software/dependencies_manual.pdf.[31] Stanford NLP Toolkits, http://nlp.stanford.edu/software.[32] Stata dataset of Compustat Quarterly Match to SEC Filings, http://faculty.chicagobooth.edu/amir.sufi/data.htm.[33] Stata/MP 11.2, http://www.stata.com.[34] P. C. Tetlock, “Giving Content to Investor Sentiment: The Role of Media in the Stock Market,” Journal of Finance, 62(3), pp.1139-1168, 2007.[35] P. C. Tetlock, M. Saar-Tsechansky and S. Macskassy, “More than Words: Quantifying Language to Measure Firms` Fundamentals,” Journal of Finance, 63(3), pp. 1437-1467, 2008.[36] J. Wiebe, R. Bruce and T. O’Hara, “Development and Use of a Gold Standard Data Set for Subjectivity Classifications,” Proceedings of the Annual Meeting of the ACL, pp. 246-253, 1999.[37] T. Wilson, J. Wiebe and P. Hoffmann, “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis,” Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354, 2005. 描述 碩士
國立政治大學
資訊科學學系
98753013
99資料來源 http://thesis.lib.nccu.edu.tw/record/#G0987530132 資料類型 thesis dc.contributor.advisor 劉昭麟<br>張元晨 zh_TW dc.contributor.advisor Liu, Chao Lin<br>Chang, Yuan Chen en_US dc.contributor.author (Authors) 陳建良 zh_TW dc.contributor.author (Authors) Chen, Chien Liang en_US dc.creator (作者) 陳建良 zh_TW dc.creator (作者) Chen, Chien Liang en_US dc.date (日期) 2010 en_US dc.date.accessioned 4-Sep-2013 17:10:48 (UTC+8) - dc.date.available 4-Sep-2013 17:10:48 (UTC+8) - dc.date.issued (上傳時間) 4-Sep-2013 17:10:48 (UTC+8) - dc.identifier (Other Identifiers) G0987530132 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60264 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 98753013 zh_TW dc.description (描述) 99 zh_TW dc.description.abstract (摘要) 財務報表中的主觀性詞彙往往影響市場中的參與者對於報導公司價值和獲利能力衡量的決策判斷。因此，公司的管理階層往往有高度的動機小心謹慎的選擇用詞以隱藏負面的消息而宣揚正面的消息。然而使用人工方式從文字量極大的財務報表挖掘有用的資訊往往不可行，因此本研究採用人工智慧方法驗證美國財務報表中的主觀性多字詞 (subjective MWEs) 和公司的財務狀況是否具有關聯性。多字詞模型往往比傳統的單字詞模型更能掌握句子中的語意情境，因此本研究應用條件隨機域模型 (conditional random field) 辨識多字詞形式的意見樣式。另外，本研究的實證結果發現一些跡象可以印證一般人對於財務報表的文字揭露往往與真實的財務數字存在有落差的印象；更發現在負向的盈餘變化情況下，公司管理階層通常輕描淡寫當下的短拙卻堅定地承諾璀璨的未來。 zh_TW dc.description.abstract (摘要) Subjective assertions in financial statements influence the judgments of market participants when they assess the value and profitability of the reporting corporations. Hence, the managements of corporations may attempt to conceal the negative and to accentuate the positive with "prudent" wording. To excavate this accounting phenomenon hidden behind financial statements, we designed an artificial intelligence based strategy to investigate the linkage between financial status measured by annual earnings and subjective multi-word expressions (MWEs). We applied the conditional random field (CRF) models to identify opinion patterns in the form of MWEs, and our approach outperformed previous work employing unigram models. Moreover, our novel algorithms take the lead to discover the evidences that support the common belief that there are inconsistencies between the implications of the written statements and the reality indicated by the figures in the financial statements. Unexpected negative earnings are often accompanied by ambiguous and mild statements and sometimes by promises of glorious future. en_US dc.description.tableofcontents CHAPTER 1 Introduction 11.1 Background 11.2 Methodology overview 21.3 Contributions 41.4 Organization 5CHAPTER 2 Literature Review 72.1 Finance literature review 72.2 Computer science literature review 9CHAPTER 3 Financial Data and Corpora 163.1 Annotated corpus: MPQA 163.2 Financial statements preprocessing 193.3 Quantitative financial data and data merging 20CHAPTER 4 Models for Opinion Patterns Identification 234.1 Conditional random fields 244.2 Feature sets and linear chain CRF data view 284.2.1 Morphological and orthographical features 294.2.2 Predicate-argument structure features 324.2.3 Syntactic features 334.2.4 Simple semantic features 38CHAPTER 5 Linkages between Earnings and Subjective MWEs 415.1 Dependent variable: standardized unexpected earnings 415.2 Explanatory variables: MWEf-idf and control variables 425.3 Multinomial logistic regression 445.4 Strategies of discriminative MWE identification 45CHAPTER 6 Experimental evaluation of CRF models 486.1 Design of the experiments 486.2 Experimental results 51CHAPTER 7 Empirical study of earnings and subjective MWEs 577.1 Opinion patterns extraction from financial statements 577.2 Empirical results of small dataset 617.3 Robustness tests of large dataset 637.4 Analysis of the economic meanings of subjective MWEs 66CHAPTER 8 Conclusions 718.1 Discussions 728.2 Future work 73References 75Appendix 79 zh_TW dc.format.extent 1293510 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0987530132 en_US dc.subject (關鍵詞) 意見探勘 zh_TW dc.subject (關鍵詞) 自然語言處理 zh_TW dc.subject (關鍵詞) 語意分析 zh_TW dc.subject (關鍵詞) 財務報表文字探勘 zh_TW dc.subject (關鍵詞) 資訊擷取 zh_TW dc.subject (關鍵詞) opinion mining en_US dc.subject (關鍵詞) natural language processing en_US dc.subject (關鍵詞) sentiment analysis en_US dc.subject (關鍵詞) financial text mining en_US dc.subject (關鍵詞) information extraction en_US dc.title (題名) 探索美國財務報表的主觀性詞彙與盈餘的關聯性:意見分析之應用 zh_TW dc.title (題名) Exploring the relationships between annual earnings and subjective expressions in US financial statements: opinion analysis applications en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] W. Antweiler and M. Z. Frank, “Is all that Talk just Noise? The Information Content of Internet Stock Message Boards,” Journal of Finance, 59(3), pp. 1259-1294, 2004.[2] Apache Lucene 3.0.0, http://lucene.apache.org/java/docs/index.html.[3] Automatic Statistical SEmantic Role Tagger-v0.14b (ASSERT), http://cemantix.org/assert.html.[4] Charniak Parser, http://www.cs.brown.edu/~ec/#software.[5] Y. Choi, C. Cardie, E. Riloff and S. Patwardhan, “Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns,” Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 355-362, 2005[6] M. J. Collins, Head-Driven Statistical Models for Natural Language Parsing, Ph.D. thesis, University of Pennsylvania, 1999.[7] Electronic Data Gathering, Analysis and Retrieval system (EDGAR), http://www.sec.gov/edgar.shtml.[8] FrameNet, http://framenet.icsi.berkeley.edu.[9] D. Gildea and D. Jurafsky, “Automatic Labeling of Semantic Role,” Computational Linguistics, 28(3), pp. 245-288, 2002.[10] W. H. Greene, Econometric Analysis, Pearson Prentice Hall, 2008.[11] Illinois Chunker, http://cogcomp.cs.illinois.edu/page/software.[12] S.-M. Kim and E. Hovy, “Identifying Opinion Holders for Question Answering in Opinion Texts,” Proceedings of AAAI Workshop on Question Answering in Restricted Domains, pp. 20-26, 2005.[13] J. D. Lafferty, A. McCallum and F. C. N. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proceedings of the International Conference on Machine Learning, pp. 282-289, 2001.[14] F. Li, “Do Stock Market Investors Understand The Risk Sentiment Of Corporate Annual Reports?” University of Michigan Working Paper, 2006.[15] D. Lin, “Automatic Retrieval and Clustering of Similar Words.” Proceedings of the International Conference on Computational Linguistics (COLING)), pp. 768-774, 1998.[16] LingPipe 3.9 sentence model, http://alias-i.com/lingpipe.[17] B. Liu, “Sentiment Analysis and Subjectivity,” Handbook of Natural Language Processing, N. Indurkhya and F. J. Damerau (editors), CRC press , Second Edition, 2010.[18] T. Loughran and B. McDonald, “When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks,” Journal of Finance, 66(1), pp. 67-97, 2011.[19] MAchine Learning for LanguagE Toolkit-2.0.6 (MALLET), http://mallet.cs.umass.edu.[20] C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2009.[21] Multi-Perspective Question Answering 2.0 (MPQA), http://www.cs.pitt.edu/mpqa.[22] B. Pang, L. Lee and S. Vaithyanathan, “Thumbs up? Sentiment Classification Using Machine Learning Techniques,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79-86, 2002.[23] F. Peng, F. Feng and A. McCallum, “Chinese Segmentation and New Word Detection using Conditional Random Fields,” Proceedings of the conference on Computational Linguistics, 2004.[24] R.W. Picard, E. Vyzas and J. Healey, “Toward Machine Emotional Intelligence: Analysis of Affective Physiological State,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), pp. 1175-1191, 2001.[25] S. Pradhan, W. Ward, K. Hacioglu, J. Martin and D. Jurafsky, “Shallow Semantic Parsing Using Support Vector Machines,” Proceedings of the Human Language Technology Conference/North American Chapter of the ACL, 2004.[26] L. A. Ramshaw and M. P. Marcus, “Text Chunking Using Transformation-based Learning,” Proceedings of the ACL Workshop on Very Large Corpora, pp 82–94, 1995.[27] E. Riloff and J. Wiebe, “Learning Extraction Patterns for Subjective Expressions,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 25-32, 2003.[28] J. Ronen and V. Yaari, Earnings Management: Emerging Insights in Theory, Practice, and Research, Springer-Verlag, 2008.[29] Standard & Poor’s Compustat Research Insight 8.4.1, http://www.compustat.com.[30] Stanford Dependencies manual, http://nlp.stanford.edu/software/dependencies_manual.pdf.[31] Stanford NLP Toolkits, http://nlp.stanford.edu/software.[32] Stata dataset of Compustat Quarterly Match to SEC Filings, http://faculty.chicagobooth.edu/amir.sufi/data.htm.[33] Stata/MP 11.2, http://www.stata.com.[34] P. C. Tetlock, “Giving Content to Investor Sentiment: The Role of Media in the Stock Market,” Journal of Finance, 62(3), pp.1139-1168, 2007.[35] P. C. Tetlock, M. Saar-Tsechansky and S. Macskassy, “More than Words: Quantifying Language to Measure Firms` Fundamentals,” Journal of Finance, 63(3), pp. 1437-1467, 2008.[36] J. Wiebe, R. Bruce and T. O’Hara, “Development and Use of a Gold Standard Data Set for Subjectivity Classifications,” Proceedings of the Annual Meeting of the ACL, pp. 246-253, 1999.[37] T. Wilson, J. Wiebe and P. Hoffmann, “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis,” Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354, 2005. zh_TW
