博碩士論文 103522075 詳細資訊




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姓名 詹皓宇(Hao-Yu Chan)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 建立財務報告自動分析系統進行股價預測
(Build an automatic financial report analysis system to predict stock price movement)
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摘要(中) 近年來,有很多的文獻研究顯示,新聞、財報、社群媒體所提供的消息和股票市場是習習相關的。美國的公司必需將他們的季報交給美國證券交易委員會,該季報稱為10-Q報告。我們使用美國證券交易委員會所提供的公司季報作為消息來源,該資料來源提供了諸如公司經營策略、風險、財務報表等資訊,能夠影響投資人對公司的看法並因此造成股價的波動。隨著時間的推移,美國證券交易委員會收集了許多的公司的財務資訊在他們的網頁中。如何有效率的使用這些資訊是很重要的事情。我們設計了一個混合系統,使用財報中的管理階層討論與分析(Management discussion and analysis, MD&A)部分來分析管理階層對於公司未來的期望和討論,並結合傳統的基本面分析對未來三個月內的股票價格作出預測。
摘要(英) Recently, many research shows that news, reports, and social media is correlated to the stock market. 10-Q report is the quarterly report which must be submitted to the United States federal Securities and Exchange Commission. We use the 10-Q report offered in SEC’s website as our resource, these data provide such as management strategy, risk, and financial statements which can impact investors’ decision and stock price movement. As time goes on, it is clear that the SEC will gather many financial information about companies. How to use these information efficiently is becoming a significant concern. In this work, we design a hybrid system combine the information in Management discussion and analysis and fundamental analysis to predict stock price movement within 3 months.
關鍵字(中) ★ 財報 關鍵字(英) ★ 財報
論文目次 1. Introduction 1
2. Related works 2
3. Materials and Methods 3
3.1 System overview 3
3.2 Data source 4
3.3 Preprocessing 4
3.3.1 Extract Management discussion and analysis section 5
3.3.2 Extract forward-looking statement 6
3.3.3 Tokenize and Stop word removal 6
3.3.4 Hypernyms 6
3.4 Feature extraction 7
3.4.1 Two-words combination feature 7
3.4.2 Sentiment feature 8
3.4.3 Fundamental analysis feature 9
3.5 Feature selection 9
3.6 Report-market mapping 9
3.7 Model training and Model evaluation 10
4. Result 12
5. Discussion and Conclusions 16
6. Reference 17
參考文獻 1.Junqué de Fortuny, E., De Smedt, T., Martens, D. & Daelemans, W. Evaluating and understanding text-based stock price prediction models. Information Processing & Management 50, 426–441 (2014).
2.Chan, S. W. K. & Franklin, J. A text-based decision support system for financial sequence prediction. Decision Support Systems 52, 189–198 (2011).
3.Junqué de Fortuny, E., De Smedt, T., Martens, D. & Daelemans, W. Evaluating and understanding text-based stock price prediction models. Information Processing & Management 50, 426–441 (2014).
4.Schumaker, R. P. & Chen, H. A quantitative stock prediction system based on financial news. Information Processing & Management 45, 571–583 (2009).
5.Schumaker, R. P. & Chen, H. Textual analysis of stock market prediction using breaking financial news. ACM Transactions on Information Systems 27, 1–19 (2009).
6.Schumaker, R. P., Zhang, Y., Huang, C.-N. & Chen, H. Evaluating sentiment in financial news articles. Decision Support Systems 53, 458–464 (2012).
7.Huang, C.-J., Liao, J.-J., Yang, D.-X., Chang, T.-Y. & Luo, Y.-C. Realization of a news dissemination agent based on weighted association rules and text mining techniques. Expert Systems with Applications 37, 6409–6413 (2010).
8.LI, F. The information content of forward-looking statements in corporate filings-a Naïve Bayesian machine learning approach. Journal of Accounting Research 48, 1049–1102 (2010).
9.Bollen, J., Mao, H. & Zeng, X. Twitter mood predicts the stock market. Journal of Computational Science 2, 1–8 (2011).
10.Harris, Z. S. Distributional structure. WORD 10, 146–162 (1954).
11.About the SEC. (2016). Retrieved from https://www.sec.gov/about.shtml
12.Bird, Steven. NLTK: the natural language toolkit. Proceedings of the COLING/ACL on Interactive presentation sessions. Association for Computational Linguistics, 2006.
13.Bill McDonald’s home page. (2015). at
14.Jeong, Y., & Myaeng, S.-H. (2013). Using WordNet hypernyms and dependency features for phrasal-level event recognition and type classification. In P. Serdyukov, P. Braslavski, S. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, I. Segalovich, & E. Yilmaz (Eds.). Advances in information retrieval (Vol. 7814, pp. 267–278). Berlin Heidelberg: Springer.
15.Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T. & Ngo, D. C. L. Text mining of news-headlines for FOREX market prediction: A multi-layer dimension reduction algorithm with semantics and sentiment. Expert Systems with Applications 42, 306–324 (2015).
16.Hagenau, M., Liebmann, M. & Neumann, D. Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems 55, 685–697 (2013).
17.Li, Q. et al. The effect of news and public mood on stock movements. Information Sciences 278, 826–840 (2014).
18.Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. LREC. Vol. 10. 2010.
19.Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
20.Heo, J. & Yang, J. Y. Stock price prediction based on financial statements using SVM. International Journal of Hybrid Information Technology 9, 57–66 (2016).
21.Richard Kyle MacKinnon and Carson K. Leung. Stock Price Prediction in Undirected Graphs Using a Structural Support Vector Machine. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (2015).
22.Kaltwasser, P. R. (2010). Uncertainty about fundamentals and herding behavior in the FOREX market. Physica A: Statistical Mechanics and its Applications, 389, 1215–1222.
23.Fama, E. F. Random walks in stock market prices. Financial Analysts Journal 21, 55–59 (1965).
指導教授 洪炯宗(Jorng-Tzong Horng) 審核日期 2016-7-22
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