政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/60264

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/60264

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题名:	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
贡献者:	劉昭麟張元晨 Liu, Chao Lin Chang, Yuan Chen 陳建良 Chen, Chien Liang
关键词:	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
上传时间:	2013-09-04 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.
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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
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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