Abstract
This paper provides an improved method by introducing Sentiment Analysis into the Event Study and Principal Component Analysis. The model is constructed by using the heuristic mean-end analysis. This method enables us to take into investors’ feelings towards related stocks when we study the stock market’s reaction to a given event. This paper investigates the Chinese A-shared market over 2013–2019 to study the influence of rumors and the offsetting impact of rumor clarifications on the stock price. The results indicate that no matter investor sentiment is bullish or bearish, stock price reacts significantly to rumors before as well as when the rumor goes public. Furthermore, clarification offsets the positive abnormal returns caused by rumors with bullish sentiment substantially at a limited level. Still, after five days, it creates a positive effect like the positive rumor does on the stock price. Under the bearish sentiment, clarification brings an insignificant impact on the stock price. The results indicate that the source of rumor may not come from the media and investment decisions established on rumors would be beneficial to investors before as well as after they are published. Moreover, official clarification causes an offset effect, but it is very limited.
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Aggarwal, R. K., & Wu, G. (2003). Stock market manipulation—Theory and evidence. SSRN Electronic Journal https://doi.org/10.2139/ssrn.474582.
Ahern, K. R., & Sosyura, D. (2015). Rumor has it: Sensationalism in financial media. The Review of Financial Studies, 28(7), 2050–2093.
Beigi, G., Hu, X., Maciejewski, R., & Liu, H. (2016). An overview of sentiment analysis in social media and its applications in disaster relief. In W. Pedrycz & S.-M. Chen (Eds.), sentiment analysis and ontology engineering (Vol. 639, pp. 313–340). https://doi.org/10.1007/978-3-319-30319-2_13.
Black, F. (1986). Noise. The Journal of Finance, 41(3), 528–543. https://doi.org/10.1111/j.1540-6261.1986.tb04513.x.
Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010 (pp. 177–186). Physica-Verlag HD.
Chen, L. (2016). Based on Multivariate Linear Regression Analysis - Forecasting Stock Prices in China Citic Bank. Economic Research Guide, 2016(19), pp.75–76.
Chen, Y. (2017). The impact of the spread of rumors and clarification on stock returns. (Doctoral dissertation).
CNKI. (2019). Retrieved Nov 7th, 2019 from https://kns.cnki.net/kns/brief/default_result.aspx
Davies, P. L., & Canes, M. (1978). Stock prices and the publication of second-hand information. Journal of Business, 51, 43–56.
Diefenbach, R. E. (1972). How good is institutional brokerage research? Financial Analysts Journal, 28(1), 54–60.
Fama, E. (1991). Efficient capital markets: II. Journal of Finance, 46, 1575–1617.
Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R., & Lin, C. J. (2008). LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9(Aug), 1871–1874.
Hastie, T., Rosset, S., Zhu, J., & Zou, H. (2009). Multi-class adaboost. Statistics and its Interface, 2(3), 349–360.
Huberman, G., & Regev, T. (2001). Contagious speculation and a cure for cancer: A nonevent that made stock prices soar. The Journal of Finance, 56(1), 387–396.
Kiymaz, H. (2001). The effects of stock market rumors on stock prices: Evidence from an emerging market. Journal of Multinational Financial Management, 11(1), 105–115.
Kleinbaum, D. G., Dietz, K., Gail, M., Klein, M., & Klein, M. (2002). Logistic regression. New York: Springer-Verlag.
Kothari, S. P., & Warner, J. B. (2007). Econometrics of event studies. In B. Eckbo Espen (Ed.), Handbook of corporate finance: Empirical corporate finance (pp. 3–36). North-Holland: Handbooks in Finance Series, Elsevier.
Lafi, S. Q., & Kaneene, J. B. (1992). An explanation of the use of principal-components analysis to detect and correct for multicollinearity. Preventive Veterinary Medicine, 13(4), 261–275.
Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69, 14–23.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18–22.
Logue, D. E., & Tuttle, D. L. (1973). Brokerage house investment advice. Financial Review, 8(1), 38–54.
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
Mansfield, E. R., & Helms, B. P. (1982). Detecting multicollinearity. The American Statistician, 36(3a), 158–160.
Mittal, A., & Goel, A. (2012). Stock prediction using twitter sentiment analysis (p. CS229). Standford University.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821). Wiley.
Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. In proceedings of the 2nd international conference on knowledge capture (pp. 70-77). ACM.
Oh, C., & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In Icis (pp. 1-19).
Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883.
Quinlan, J. R. (1996). Learning decision tree classifiers. ACM Computing Surveys (CSUR), 28(1), 71–72.
SSE. (2008). Retrieved Nov 7th, 2019, from http://www.sse.com.cn/disclosure/listedinfo/announcement/c/2008-02-26/600050_20080226_1.pdf
Su, J., Shirab, J. S., & Matwin, S. (2011). Large scale text classification using semi-supervised multinomial naive bayes. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 97–104).
Teng, L., & Zheng, W. (2019). Deep Multiple Regression Model for Stock Price Trend Forecasting. Economic Research Guide, 2019(21), pp. 71–74.
Yang, X., & Luo, Y. (2014). Rumor clarification and stock returns: Do bull markets behave differently from bear markets? Emerging Markets Finance and Trade, 50(1), 197–209. https://doi.org/10.2753/REE1540-496X500111.
Zhao, J. M., He, X., & Wu, F. Y. (2010). Research on Chinese stock market rumors: Rumors, rumors and their impact on stock prices. Management World, 11, 48–61.
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This work is partially supported by the National Natural Science Foundation of China (Grant No. 61872084) and also VC Research (VCR 0000017).
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Xu, Q., Chang, V. & Hsu, CH. Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study. Inf Syst Front 22, 1021–1037 (2020). https://doi.org/10.1007/s10796-020-10024-5
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DOI: https://doi.org/10.1007/s10796-020-10024-5