Stock Market Prediction Using Heat of Related Keywords on Micro Blog
ABSTRACT
Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed, as Micro Blog is an ideal source for capturing public opinions towards certain topic. We choose Shanghai Composite index as the research object, through correlation analysis, Granger causality analysis, and support vector machine classification, the results have shown that the keywords heat on micro blog can make a short-time prediction of stock market, and the keyword which expresses negative emotion have more powerful prediction ability.
Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed, as Micro Blog is an ideal source for capturing public opinions towards certain topic. We choose Shanghai Composite index as the research object, through correlation analysis, Granger causality analysis, and support vector machine classification, the results have shown that the keywords heat on micro blog can make a short-time prediction of stock market, and the keyword which expresses negative emotion have more powerful prediction ability.
Cite this paper
S. Zhou, X. Shi, Y. Sun, W. Qu and Y. Shi, "Stock Market Prediction Using Heat of Related Keywords on Micro Blog," Journal of Software Engineering and Applications, Vol. 6 No. 3, 2013, pp. 37-41. doi: 10.4236/jsea.2013.63B009.
S. Zhou, X. Shi, Y. Sun, W. Qu and Y. Shi, "Stock Market Prediction Using Heat of Related Keywords on Micro Blog," Journal of Software Engineering and Applications, Vol. 6 No. 3, 2013, pp. 37-41. doi: 10.4236/jsea.2013.63B009.
References
[1] Zhu Yu. Reaserch on Stock Market Prediction. North-western Polytechnical University, 2006. [2] Peters, E., Chaos and Order in the Capital Markets: a New View of Cycles, Prices and Market Volatility, New York: John Wiley and Sons, 1991: 5~6. [3] Lin Xiaoming, Wang meijin. China’s Stock Market Chaos Phenomenon and Market Efficiency, The Journal of Quantitative & Tech-nical Economics, 1997, 4: 51~53. [4] Hu Binbin, Dang Jiarui, Lan Baixiong. Research on China’s Stock Market Predictablilty. Finanace & Economics, 2001 (3). [5] Fisher KL, Statman M. Investor sentiment and stock returns. Financial Analysts Journal, 2000, 3/4(2):13-23. [6] Chan S Y, Fong W M. Individual Investors’ sentiment and temporary stock price pressure. Journal of Business Finance and Accounting, 2004, 31(5-6): 823-836. [7] Verma R, Verma P. Noise trading and stock market volatility. Journal of Multinational Financial Management, 2007, 17: 231-243. [8] Yu Peikun, Zhong Ruijun, Can Individual Investor Senti-ment Predict Market Rate of Return, Nankai Business Review, 2009, 12(1): 96-101. [9] Asur, S. Predicting the Future with Social Media. In Proceedings of 2010 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pages 492-499, Tornoto, Canda, August 2010. IEEE. [10] John B, Huina M, Xiaojun Z. Twitter Mood Predicts the Stock Mraket, Journal of Computational Science, 2: 1-8, 2011. [11] Yang Miao, The multivariate generalization of the Granger causality and the Applied Research, Southwestern University of Finance and Economics, 2002. [12] Zhang Zuoquan. Application of SVM in Security Investment Analysis, Beijing Jiaotong University, 2007. [13] Vapnik V N. The nature of statistical learning theory. New York:Springer,1995. [14] 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
[1] Zhu Yu. Reaserch on Stock Market Prediction. North-western Polytechnical University, 2006. [2] Peters, E., Chaos and Order in the Capital Markets: a New View of Cycles, Prices and Market Volatility, New York: John Wiley and Sons, 1991: 5~6. [3] Lin Xiaoming, Wang meijin. China’s Stock Market Chaos Phenomenon and Market Efficiency, The Journal of Quantitative & Tech-nical Economics, 1997, 4: 51~53. [4] Hu Binbin, Dang Jiarui, Lan Baixiong. Research on China’s Stock Market Predictablilty. Finanace & Economics, 2001 (3). [5] Fisher KL, Statman M. Investor sentiment and stock returns. Financial Analysts Journal, 2000, 3/4(2):13-23. [6] Chan S Y, Fong W M. Individual Investors’ sentiment and temporary stock price pressure. Journal of Business Finance and Accounting, 2004, 31(5-6): 823-836. [7] Verma R, Verma P. Noise trading and stock market volatility. Journal of Multinational Financial Management, 2007, 17: 231-243. [8] Yu Peikun, Zhong Ruijun, Can Individual Investor Senti-ment Predict Market Rate of Return, Nankai Business Review, 2009, 12(1): 96-101. [9] Asur, S. Predicting the Future with Social Media. In Proceedings of 2010 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pages 492-499, Tornoto, Canda, August 2010. IEEE. [10] John B, Huina M, Xiaojun Z. Twitter Mood Predicts the Stock Mraket, Journal of Computational Science, 2: 1-8, 2011. [11] Yang Miao, The multivariate generalization of the Granger causality and the Applied Research, Southwestern University of Finance and Economics, 2002. [12] Zhang Zuoquan. Application of SVM in Security Investment Analysis, Beijing Jiaotong University, 2007. [13] Vapnik V N. The nature of statistical learning theory. New York:Springer,1995. [14] 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