When can social media lead financial markets?

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Zheludev, Ilya ^a   Smith, Robert ^a   Aste, Tomaso ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84922286895     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep04213     Document Type: Article

References (26)

1. Kietzmann, J. H., Hermkens, K., McCarthy, I. P. & Silvestre, B. S. Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horizons 54, 241-251 (2011).
2. Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P. & Rosenquist, J. N. Understanding the Demographics of Twitter Users. Paper presented at the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain. Menlo Park, CA, USA: The AAAI Press. (July 2011).
3. de Vries, L., Gensler, S. & Leeflang, P. S. H. Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. J. Interact. Mark. 26, 83-91 (2012).
4. Asur, S. & Huberman, B. A. Predicting the Future with Social Media. Paper presented at the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Theory, Toronto, Canada. DOI: 10.1109/WIIAT. 2010.63. (August 2010).
5. O'Connor, B., Balasubramanyan, R., Routledge, B. R. & Smith, N. A. From tweets to polls: Linking text sentiment to public opinion time series. Paper presented at the Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, USA. Menlo Park, CA, USA: The AAAI Press. (May 2010).
6. Bollen, J., Mao, H. & Zeng, X. Twitter mood predicts the stock market. J. Comp. Sci. 2, 1-8 (2011).
7. Zhang, X., Fuehres, H.&Gloor, P. A. Predicting Stock Market Indicators Through Twitter ''I hope it is not as bad as I fear'' Procedia Soc. Behav. Sci. 26, 55-62 (2011).
8. Mao, Y.,Wei,W., Wang, B.&Liu, B. Correlating S&P500 stocks with Twitter data. Paper presented at the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, Beijing, China. New York, NY, USA: ACM. (August 2012).
9. Ruiz, E. J., Hristidis, V., Castillo, C., Gionis, A. & Jaimes, A. Correlating Financial Time Series with Micro Blogging Activity. Paper presented at the Fifth ACM International Conference on Web search and Data Mining, Seattle, USA. New York, NY, USA: ACM. (February 2012).
10. Preis, T., Moat, H. S. & Stanley, H. E. Quantifying Trading Behavior in Financial Markets Using Google Trends. Sci. Rep. 3, 1684 (2013).
11. Challet, D. & Bel Hadj Ayed, A. Predicting financial markets with Google Trends and not so random keywords. arXiv preprint arXiv:1307.4643 (2013).
12. Preis, T., Reith, D. & Stanley, H. E. Complex dynamics of our economic life on different scales: insights from search engine query data. Philos. T. R. Soc. A. 368, 5707-5719 (2010).
13. Bordino, I., Battiston, S., Caldarelli, G. & Cristelli, M. Web search queries can predict stock market volumes. PloS one 7, e40014 (2012).
14. Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E. & Preis, T. Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Sci. Rep. 3, 1801 (2013).
15. Grossman, S. J. & Stiglitz, J. E. On the Impossibility of Informationally Efficient Markets. Am. Econ. Rev. 70, 393-408 (1980).
16. Brody, D.,Meister, B. & Parry, M. Informational inefficiency in financial markets. Math. Fin. Econ. 6, 249-259 (2012).
17. Saavedra, S., Duch, J. & Uzzi, B. Tracking Traders' Understanding of the Market Using e-Communication Data. PLoS ONE 6, e26705 (2011).
18. Oliveira, N., Cortez, P. & Areal, N. Some experiments on modeling stock market behavior using investor sentiment analysis and posting volume from Twitter. Paper presented at the 3rd International Conference on Web Intelligence, Mining and Semantics, Madrid, Spain. New York, NY, USA: ACM. (June 2013).
19. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D. & Kappas, A. Sentiment strength detection in short informal text. J. Am. Soc. Imf. Sci. Technol. 61, 2544-2558 (2010).
20. Thelwall, M., Buckley, K. & Paltoglou, G. Sentiment strength detection for the social web. J. Am. Soc. Imf. Sci. Technol. 63, 163-173 (2012).
21. Dionisio, A., Menezes, R. & Mendes, D. A. Mutual information: a measure of dependency for nonlinear time series. Phys. A. 344, 326-329 (2004).
22. Sturges, H. A. The Choice of a Class Interval. JASA 21, 65-66 (1926).
23. MacQueen, J. B. Some Methods for Classification and Analysis of MultiVariate Observations. Paper presented at the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA. Berkeley, CA, USA: University of California Press. (June 1965).
24. D'Souza, S. Brandirectory Global 500 2013 Top Brands. Brandirectory (2013). Available at: http://brandirectory.com/league-tables/table/global-500-2013. Accessed: 29/Oct/2013.
25. Zaremba, A. & Aste, T. Measures of Causality in Complex Datasets with application to financial data. arXiv preprint arXiv:1401.1457 (2014).
26. Wood, R., Zheludev, I. & Treleaven, P. Mining Social Data with UCL's Social Media Platform. Paper presented at the 2012 International Conference on Data Mining, Las Vegas, NV, USA. Las Vegas, NV, USA: CSREA Press. (July 2012).