博碩士論文 103522081 詳細資訊




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姓名 鍾文荃(Wen-Chiuan Chung)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 新聞語意特徵擷取流程設計與股價變化關聯性分析
(News semantic feature extraction process design and the correlation analysis between news and stock price)
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摘要(中) 近年來,有許多的研究希望透過市場上現有的消息,例如公司的財務報表以及新聞報導,來進行對股價的預測及走向判斷。根據Fama提出的效率市場假說[12],這些公開的訊息會反映在股價的變化上,因此,如何從這些訊息中擷取出有效的內容以判別股價的走向是此類研究的重點。然而在這一方面,過往的研究大多以詞袋(Bag of words)來進行模型的建立,再進一步則是使用複合詞(complex words)如N-gram、名詞短語(Noun phrase)等等。較少有研究進一步的在文中去搜尋與股價有確切關聯的內容。在本研究中,我們使用了不同的文本探勘工具去找尋與特定公司更具有關聯性的內容,並分析這些內容與其股價的關係。我們希望透過應用近年來不斷成熟的文本探勘技術,針對新聞中單詞的詞性、句子的結構以及情感分數進行更有效的特徵擷取,以增進預測模型的精準度。
摘要(英) In recent years, there are many studies try to predict the direction of stock price with available message on the market, such as financial statements and financial news. According to Fama′s efficient market hypothesis[12], these public information will be reflected in the change of stock price. Therefore, how to retrieve the effective message from news to determine the stock price trend is the significant point of such research. However, in this aspect, past studies mostly established prediction model with bag of words, still further was the use of complex word such as n-gram, noun phrase, etc, few studies have further to search the text content associated with the stock price in the news. In this study, we used some text mining tools to find the more relevant content of specific company and analyze the relationship between these content and the company’s stock price. We hope to get effective features through applied the more mature text mining technology for part of speech of words, sentence structure and sentiment analysis, we can enhance the accuracy of the prediction model.
關鍵字(中) ★ 文本探勘
★ 股票
★ 關聯性分析
關鍵字(英) ★ text mining
★ stock
★ correlation analysis
論文目次 摘要 i
ABSTRACT ii
Table of Contents iii
List of Tables iv
List of Figures v
Chapter 1 Introduction 1
Chapter 2 Related Works 2
Chapter 3 Materials and Methods 4
3.1 Data source 4
3.2 Work flow 5
3.3 Preprocessing 6
3.4 Multi-layer feature extraction 7
3.5 Semantic abstraction 11
3.6 Feature selection and representation 11
3.7 Training and classification 12
Chapter 4 Results 13
Chapter 5 Discussion and Conclusion 17
References 19
參考文獻 1. Ou, Jane A., and Stephen H. Penman. "Financial statement analysis and the prediction of stock returns." Journal of accounting and economics 11.4 (1989): 295-329.
2. Healy, Paul M., and Krishna G. Palepu. "The effect of firms′ financial disclosure strategies on stock prices." Accounting Horizons 7.1 (1993): 1.
3. Dechow, Patricia M., et al. "Short-sellers, fundamental analysis, and stock returns." Journal of Financial Economics 61.1 (2001): 77-106.
4. Lam, Monica. "Neural network techniques for financial performance prediction: integrating fundamental and technical analysis." Decision Support Systems 37.4 (2004): 567-581.
5. Mittermayer, Marc-André. "Forecasting intraday stock price trends with text mining techniques." System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on. IEEE, 2004.
6. Tetlock, Paul C., M. A. Y. T. A. L. SAAR‐TSECHANSKY, and Sofus Macskassy. "More than words: Quantifying language to measure firms′ fundamentals." The Journal of Finance 63.3 (2008): 1437-1467.
7. Li, Feng. "The information content of forward‐looking statements in corporate filings—A naïve Bayesian machine learning approach." Journal of Accounting Research 48.5 (2010): 1049-1102.
8. Schumaker, Robert P., and Hsinchun Chen. "Textual analysis of stock market prediction using breaking financial news: The AZFin text system." ACM Transactions on Information Systems (TOIS) 27.2 (2009): 12.
9. Schumaker, Robert P., et al. "Evaluating sentiment in financial news articles." Decision Support Systems 53.3 (2012): 458-464.
10. Butler, Matthew, and Vlado Kešelj. "Financial forecasting using character n-gram analysis and readability scores of annual reports." Advances in artificial intelligence. Springer Berlin Heidelberg, 2009. 39-51.
11. Hagenau, Michael, Michael Liebmann, and Dirk Neumann. "Automated news reading: Stock price prediction based on financial news using context-capturing features." Decision Support Systems 55.3 (2013): 685-697.

12. Fama, Eugene F. "The behavior of stock-market prices." The journal of Business 38.1 (1965): 34-105.
13. Bird, Steven. "NLTK: the natural language toolkit." Proceedings of the COLING/ACL on Interactive presentation sessions. Association for Computational Linguistics, 2006.
14. Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. "Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network." In Proceedings of HLT-NAACL 2003, pp. 252-259.
15. Finkel, Jenny Rose, Trond Grenager, and Christopher Manning. "Incorporating non-local information into information extraction systems by gibbs sampling." Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2005.
16. Richard Socher, John Bauer, Christopher D. Manning and Andrew Y. Ng. "Parsing With Compositional Vector Grammars." Proceedings of ACL 2013.
17. Jeong, Yoonjae, and Sung-Hyon Myaeng. "Using WordNet hypernyms and dependency features for phrasal-level event recognition and type classification." Advances in information retrieval. Springer Berlin Heidelberg, 2013. 267-278.
18. Nassirtoussi, Arman Khadjeh, et al. "Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment." Expert Systems with Applications 42.1 (2015): 306-324.
19. Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining." LREC. Vol. 10. 2010.
20. Coussement, Kristof, and Dirk Van den Poel. "Improving customer complaint management by automatic email classification using linguistic style features as predictors." Decision Support Systems 44.4 (2008): 870-882.
21. Chang, Chih-Chung, and Chih-Jen Lin. "LIBSVM: a library for support vector machines." ACM Transactions on Intelligent Systems and Technology (TIST) 2.3 (2011): 27.
22. Wuthrich, B., et al. "Daily stock market forecast from textual web data." Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on. Vol. 3. IEEE, 1998.
23. Das, Sanjiv R., and Mike Y. Chen. "Yahoo! for Amazon: Sentiment extraction from small talk on the web." Management Science 53.9 (2007): 1375-1388.

24. Groth, Sven S., and Jan Muntermann. "An intraday market risk management approach based on textual analysis." Decision Support Systems 50.4 (2011): 680-691.
25. Berezina, Katerina, et al. "Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews." Journal of Hospitality Marketing & Management 25.1 (2016): 1-24.
26. Barak, Sasan, and Mohammad Modarres. "Developing an approach to evaluate stocks by forecasting effective features with data mining methods." Expert Systems with Applications 42.3 (2015): 1325-1339.
27. Nguyen, Thien Hai, Kiyoaki Shirai, and Julien Velcin. "Sentiment analysis on social media for stock movement prediction." Expert Systems with Applications 42.24 (2015): 9603-9611.
28. Shynkevich, Yauheniya, et al. "Stock price prediction based on stock-specific and sub-industry-specific news articles." 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015.
29. Shynkevich, Yauheniya, et al. "Predicting Stock Price Movements Based on Different Categories of News Articles." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
指導教授 洪炯宗(Jorng-Tzong Horng) 審核日期 2016-7-14
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