Abstract
The stock price forecasting has always been considered as a difficult problem in time series prediction. Mass of financial Internet information play an important role in the financial markets,information sentiment is an important indicator reflecting the ideas and emotions of investors and traders. Most of the existing research use the stock’s historical price and technical indicators to predict future price trends of the stock without taking the impact of financial information into account. In this paper, we further explore the relationship between the Internet financial information and financial markets,including the relations between the Internet financial information content, Internet financial information sentimental value and stock price. We collect the news of three stocks in the Chinese stock market in the GEMin a few large portals, use the text sentiment analysis algorithm to calculate the sentimental value of the corresponding Internet financial information, combined with the stock price data, implantSupport Vector Machines to analyzes and forecasts on the stock price, the accuracy of the prediction of stock priceshas been improved.
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References
Yaser, S.A.M., Atiya, A.F.: Introduction to Financial Forecasting. J. Appl. Intell. 6, 205–213 (1996)
Malkiel, B.G.: A Random Walk Down Wall Street. W.W. Norton and Company Ltd., New York (1973)
Werner, A., Murray, Z.F.: Is All That talk Just Noise? The Information Content of Internet Stock Message Boards. J. Fin. 59, 1259–1294 (2004)
Liang, X.: Impacts of Internet Stock News on Stock Markets Based on Neural Networks. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 897–903. Springer, Heidelberg (2005)
Liang, X.: Mining Associations betweenWeb Stock News Volumes and Stock Prices. J.Int. J. Syst. Sci. 37, 919–930 (2006)
Sanjiv, R.D., Mike, Y.C.: Yahoo! For Amazon Sentiment Extraction from Small Talk on the Web. J. Mgt. Sci. 53, 1375–1388 (2007)
Wang, Y.F.: Predicting Stock Price Using Fuzzy Grey Prediction System. J.Expert Syst. Appl. 22, 33–39 (2002)
Kim, K.J., Han, I.: Genetic Algorithms Approach to Feature Discretization in Artificial Neural Networks for the Prediction of Stock Price Index. J. Expert Syst. Appl. 19, 125–132 (2000)
Cao, L.J., Tay, F.E.H.: Financial Forecasting Using Support Vector Machines. J. Neural Comput. Appl. 10, 184–192 (2001)
Hassan, M.R., Nath, B.: StockMarket Forecasting Using Hidden Markov Model: A New Approach. In: 5th International Conference on Intelligent Systems Design and Applications, pp. 192–196. IEEE Press, Australia (2005)
Chang, P.C., Liu, C.H.: A TSK Type Fuzzy Rule based System for Stock Price Prediction. J. Expert Syst. Appl. 34, 135–144 (2008)
Liao, Z., Wang, J.: Forecasting Model of Global Stock Index by Stochastic Time Effective Neural Network. J. Expert Syst. Appl. 37, 834–841 (2010)
Liang, X., Zhang, H.S., Xiao, J.G., Chen, Y.: Improving Option Price Forecasts with Neural Networks and SupportVector Regressions. J. Neural Comput. Appl. 72, 3055–3065 (2009)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall International Inc., Englewood Cliffs (1999)
Cao, L.J., Tay, F.E.H.: Support Vector Machine with Adaptive Parameters inFinancial Time Series Forecasting. J. IEEE Tran. on Neural Network 14, 1506–1518 (2003)
Lee, M.C.: Using Support Vector Machine with a Hybrid Feature Selection Method to the Stock Trend Prediction. Expert Syst. Appl. 36, 10896–10904 (2009)
Yeh, C.Y., Huang, C.W., Lee, S.J.: A Multiple-Kernel Support Vector Regression Approach for Stock MarketPrice Forecasting. J. Expert Syst. Appl. 38, 2177–2186 (2011)
Hassan, M.R., Nath, B., Kirley, M.: AFusion Model of HMM, ANN and GA for Stock Market Forecasting. J. Expert Syst. Appl. 33, 171–180 (2007)
Bildirici, M., Erisn, O.O.: Improving Forecasts of GARCH Family Models with the Artificial Neural Networks: An Application to the Daily Returns in Istanbul Stock Exchange. J. Expert Syst. Appl. 36, 7355–7362 (2009)
Wen, Q.H., Yang, Z.H., Song, Y.X., Jia, P.F.: Automatic Stock Decision Support System Based on Box Theory and SVM Algorithm. J. Expert Syst. Appl. 37, 1015–1022 (2010)
Zhao, Y.Y., Qin, B., Liu, T.: Sentiment Analysis. J. Softw. 21, 1834–1848 (2010)
Chen, P.Y., Wu, S.Y., Yoon, J.S.: The Impact of Online Recommendations and Consumer Feedback on Sales. In: 25th International Conference on Information Systems, pp. 711–724. AIS, Washington (2004)
Liu, Y.: Word of Mouth for Movie: Its Dynamics and Impact on Box Office Revenue. J. Marketing 70, 74–89 (2006)
Ghose, A., Panagiotis, G.I.: Designing Novel Review Ranking Systems: Predicting the Usefulness and Impact of Reviews. In: 9th International Conference on Electronic Commerce, pp. 303–310. ACM, New York (2007)
Devitt, A., Ahmad, K.: Sentiment Polarity Identification InFinancial News: A CohesionbasedApproach. In: 45th Annual Meeting of the Association of Computational Linguistics, pp. 984–991. Association for Computational Linguistics, Prague (2007)
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Cao, R., Liang, X., Ni, Z. (2012). Stock Price Forecasting with Support Vector Machines Based on Web Financial Information Sentiment Analysis. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_44
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DOI: https://doi.org/10.1007/978-3-642-35527-1_44
Publisher Name: Springer, Berlin, Heidelberg
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