Abstract
How the online social media, like Twitter or its variant Weibo, interacts with the stock market and whether it can be a convincing proxy to predict the stock market have been debated for years, especially for China. As the traditional theory in behavioral finance states, the individual emotions can influence decision-makings of investors, it is reasonable to further explore these controversial topics systematically from the perspective of online emotions, which are richly carried by massive tweets in social media. Through thorough studies on over 10 million stock-relevant tweets and 3 million investors from Weibo, it is revealed that inexperienced investors with high emotional volatility are more sensible to the market fluctuations than the experienced or institutional ones, and their dominant occupation also indicates that the Chinese market might be more emotional as compared to its western counterparts. Then both correlation analysis and causality test demonstrate that five attributes of the stock market in China can be competently predicted by various online emotions, like disgust, joy, sadness and fear. Specifically, the presented prediction model significantly outperforms the baseline model, including the one taking purely financial time series as input features, on predicting five attributes of the stock market under the K-means discretization. We also employ this prediction model in the scenario of realistic online application and its performance is further testified.
Similar content being viewed by others
Notes
In the present paper, index refers in particular to Shanghai Stock Exchange Composite Index and the trading volume refers in particular to the daily volume of the Shanghai Stock Exchange.
References
Antweiler, W., Frank, M.Z.: Is all that talk just noise? the information content of internet stock message boards. J. Financ. 59(3), 1259–1294 (2004)
Asur, S., Huberman, B.A.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM international conference on Web intelligence and intelligent agent technology (WI-IAT), vol. 1, pp. 492–499. IEEE (2010)
Bagozzi, R.P., Wong, N., Yi, Y.: The role of culture and gender in the relationship between positive and negative affect. Cognit. Emot. 13(6), 641–672 (1983)
Baker, M., Wurgler, J.: Investor sentiment in the stock market. Working Paper 13189, National Bureau of Economic Research (2007)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A.: Web search queries can predict stock market volumes. PLoS ONE 7(7), e40014 (2012)
Brown, G.W., Cliff, M.T.: Investor sentiment and the near-term stock market. J. Empir. Financ. 11(1), 1–27 (2004)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: The million follower fallacy. In: Proceedings of the 4th international AAAI conference on Weblogs and social media. ICWSM ’10, Washington DC, USA (2010)
Cohen-Charash, Y., Scherbaum, C.A., Kammeyer-Mueller, J.D., Staw, B.M.: Mood and the market: can press reports of investors’ mood predict stock prices PLoS ONE 8(8), e72031 (2013)
Deng, Y., Chang, L., Yang, M., Huo, M., Zhou, R.: Gender differences in emotional response: Inconsistency between experience and expressivity. PloS one 11 (6), e0158666 (2016)
Dolan, R.J.: Emotion, cognition, and behavior. Science 298(5596), 1191–1194 (2002)
Dunbar, R.: Grooming, Gossip, and the Evolution of Language. Harvard University Press, Cambridge (1998)
Ekman, P., Friesen, W.V., Ellsworth, P.: Emotion in the human face: Guidelines for research and an integration of findings. Elsevier, Amsterdam (2013)
Gayo-Avello, D.: “i wanted to predict elections with twitter and all i got was this lousy paper” –a balanced survey on election prediction using twitter data. arXiv preprint. arXiv:1204.6441 (2012)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report, Stanford Digital Library Technologies Project (2011)
Guo, Z., Li, Z., Tu, H.: Sina microblog: an information-driven online social network. In: 2011 international conference on cyberworlds (CW), pp. 160–167. IEEE (2011)
Hardle, W., Marron, J.: Bootstrap simultaneous error bars for nonparametric regression. Ann. Stat. 19, 778–796 (1991)
Hirshleifer, D., Shumway, T.: Good day sunshine: Stock returns and the weather. J. Financ. 58(3), 1009–1032 (2003)
Howarth, E., Hoffman, M.S.: A multidimensional approach to the relationship between mood and weather. Br. J. Psychol. 75(Pt 1, 1), 15–23 (1984)
Hu, Y., Zhao, J., Wu, J.: Emoticon-based ambivalent expression: A hidden indicator for unusual behaviors in weibo. PLoS ONE 11(1), e0147079 (2016)
Kara, Y., Boyacioglu, M.A., Baykan, O.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the istanbul stock exchange. Expert Syst. Appl. 38(5), 5311–5319 (2011)
Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55(1), 307–319 (2003)
Krauss, C., Do, X.A., Huck, N.: Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the s&p 500. Eur. J. Oper. Res. 259(2), 689–702 (2017)
Leinweber, D., Sisk, J.: Event-driven trading and the ”new news”. J. Portf. Manag. 38(1), 110–124 (2011)
Luarn, P., Yang, J.C., Chiu, Y.P.: The network effect on information dissemination on social network sites. Comput. Hum. Behav. 37, 1–8 (2014)
Mao, H., Counts, S., Bollen, J.: Quantifying the effects of online bullishness on international financial markets. In: ECB Workshop on Using Big Data for Forecasting and Statistics, Frankfurt, Germany (2014)
Nofsinger, J.R.: Social mood and financial economics. J. Behav. Finance 6(3), 144–160 (2005)
Nossman, M., Wilhelmsson, A.: Is the vix futures market able to predict the vix index? a test of the expectation hypothesis (digest summary). J. Altern. Invest. 12(2), 54–67 (2009)
Oh, C., Sheng, O.: Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In: ICIS. Association for Information Systems (2011)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC. vol. 10, pp. 1320–1326 (2010)
Parikh, R., Movassate, M.: Sentiment analysis of user-generated twitter updates using various classification techniques. Technical report (2009)
Parkins, R.: Gender and emotional expressiveness: An analysis of prosodic features in emotional expression. Pragmatics and Intercultural Communication 5(1), 46–54 (2012)
Preis, T., Reith, D., Stanley, H.E.: Complex dynamics of our economic life on different scales: insights from search engine query data. Philos. Trans. R. Soc. London A Math. Phys. Eng. Sci. 368(1933), 5707–5719 (2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web. pp. 851–860. ACM (2010)
Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: The AZFin Text System. ACM Trans. Inf. Syst. 27(2), 1–19 (2009)
Schwert, G.W.: Why does stock market volatility change over time J. Financ. 44(5), 1115–1153 (1989)
Wanyun, C., Jie, L.: Investors’ bullish sentiment of social media and stock market indices. J. Manag. 5, 012 (2013)
Westerhoff, F.H.: Greed, fear and stock market dynamics. Physica A 343, 635–642 (2004)
Xiao Ding, Y.Z.T.L.J.D.: Deep Learning for Event-Driven Stock Prediction. In: IJCAI. pp. 1–7 (2015)
Young, P.: Jackknife and bootstrap resampling methods in statistical analysis to correct for bias. Stat. Sci. 11, 189–228 (1996)
Zhang, L., Pentina, I.: Motivations and usage patterns of Weibo. Cyberpsychol. Behav. Soc. Netw. 15(6), 312–317 (2012)
Zhao, J., Dong, L., Wu, J., Xu, K.: Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 1528–1531. ACM (2012)
Zhou, Z., Zhao, J., Xu, K.: Can online emotions predict the stock market in china?. In: International Conference on Web Information Systems Engineering. pp. 328–342. Springer (2016)
Author information
Authors and Affiliations
Corresponding author
Additional information
This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering
Guest Editors: Wojciech Cellary, Hua Wang, and Yanchun Zhang
Rights and permissions
About this article
Cite this article
Zhou, Z., Xu, K. & Zhao, J. Tales of emotion and stock in China: volatility, causality and prediction. World Wide Web 21, 1093–1116 (2018). https://doi.org/10.1007/s11280-017-0495-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-017-0495-4