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Winning by Following the Winners: Mining the Behaviour of Stock Market Experts in Social Media

  • Conference paper
Social Computing, Behavioral-Cultural Modeling and Prediction (SBP 2014)

Abstract

We propose a novel yet simple method for creating a stock market trading strategy by following successful stock market expert in social media. The problem of “how and where to invest” is translated into “who to follow in my investment”. In other words, looking for stock market investment strategy is converted into stock market expert search. Fortunately, many stock market experts are active in social media and openly express their opinions about market. By analyzing their behavior, and mining their opinions and suggested actions in Twitter, and simulating their recommendations, we are able to score each expert based on his/her performance. Using this scoring system, experts with most successful trading are recommended. The main objective in this research is to identify traders that outperform market historically, and aggregate the opinions from such traders to recommend trades.

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References

  1. Bandari, R., Asur, S., Huberman, B.A.: The pulse of news in social media: Forecasting popularity. CoRR (2012)

    Google Scholar 

  2. Bollena, J., Maoa, H., Zengb, X.: Twitter mood predicts the stock market. Journal of Computational Science (2011)

    Google Scholar 

  3. Gilbert, E., Karahalios, K.: Widespread worry and the stock market. In: Int. AAAI Conf. on Weblogs and Social Media (2010)

    Google Scholar 

  4. Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in twitter. In: Int. Conf. Companion on WWW (2011)

    Google Scholar 

  5. Hsieh, C.-C., Moghbel, C., Fang, J., Cho, J.: Experts vs the crowd: Examining popular news prediction performance on twitter. In: Int. Conf. on WWW (2013)

    Google Scholar 

  6. Kumar, S., Morstatter, F., Zafarani, R., Liu, H.: Whom should I follow? Identifying relevant users during crisis. In: ACM Conf. on Hypertext and Social Media (2013)

    Google Scholar 

  7. Lehmann, J., Castillo, C., Lalmas, M., Zuckerman, E.: Finding news curators in twitter. In: Int. Conf. on WWW Companion, pp. 863–870 (2013)

    Google Scholar 

  8. Makrehchi, M., Shah, S., Liao, W.: Stock prediction using event-based sentiment analysis. In: IEEE/WIC/ACM Int. Conf. on Web Intelligence (2013)

    Google Scholar 

  9. McNair, D., Heuchert, J.P., Shilony, E.: Profile of mood states. Bibliography, 1964–2002 (2003)

    Google Scholar 

  10. Oh, C., Sheng, O.: Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In: ICIS, pp. 57–58 (2011)

    Google Scholar 

  11. Ruiz, E.J., Hristidis, V., Castillo, C., Gionis, A., Jaimes, A.: Correlating financial time series with micro-blogging activity. In: Int. Conf. on Web Search and Data Mining, WSDM 2012, pp. 513–522 (2012)

    Google Scholar 

  12. Sharma, N.K., Ghosh, S., Benevenuto, F., Ganguly, N., Gummadi, K.: Inferring who-is-who in the twitter social network. SIGCOMM Comput. Commun. Rev. 42(4), 533–538 (2012)

    Article  Google Scholar 

  13. Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Opinionfinder: a system for subjectivity analysis. In: HLT/EMNLP on Interactive Demonstrations, HLT-Demo 2005, pp. 34–35 (2005)

    Google Scholar 

  14. Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. In: Innovation Networks Conference- COINs 2010 (2010)

    Google Scholar 

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© 2014 Springer International Publishing Switzerland

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Liao, W., Shah, S., Makrehchi, M. (2014). Winning by Following the Winners: Mining the Behaviour of Stock Market Experts in Social Media. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2014. Lecture Notes in Computer Science, vol 8393. Springer, Cham. https://doi.org/10.1007/978-3-319-05579-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-05579-4_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05578-7

  • Online ISBN: 978-3-319-05579-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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