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2. Social Computing, Behavioral-Cultural Modeling and Prediction
3. Conference paper
WINNING BY FOLLOWING THE WINNERS: MINING THE BEHAVIOUR OF STOCK MARKET EXPERTS
IN SOCIAL MEDIA
* Conference paper
* pp 103–110
* Cite this conference paper
Social Computing, Behavioral-Cultural Modeling and Prediction (SBP 2014)
* Wenhui Liao19,
* Sameena Shah19 &
* Masoud Makrehchi20
Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8393))
Included in the following conference series:
* International Conference on Social Computing, Behavioral-Cultural Modeling,
and Prediction
* 4492 Accesses
* 6 Citations
* 2 Altmetric
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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PREVIEW
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SIMILAR CONTENT BEING VIEWED BY OTHERS
AIS - A METRIC FOR ASSESSING THE IMPACT OF AN INFLUENCER’S TWITTER ACTIVITY
ON THE PRICE OF A CRYPTOCURRENCY
Chapter © 2024
TWITTER SENTIMENT ANALYSIS: HOW TO HEDGE YOUR BETS IN THE STOCK MARKETS
Chapter © 2014
TRACKING MULTIPLE SOCIAL MEDIA FOR STOCK MARKET EVENT PREDICTION
Chapter © 2017
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
Download references
AUTHOR INFORMATION
AUTHORS AND AFFILIATIONS
1. Thomson Reuters, USA
Wenhui Liao & Sameena Shah
2. University of Ontario Institute of Technology, Canada
Masoud Makrehchi
Authors
1. Wenhui Liao
View author publications
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2. Sameena Shah
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You can also search for this author in PubMed Google Scholar
3. Masoud Makrehchi
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You can also search for this author in PubMed Google Scholar
EDITOR INFORMATION
EDITORS AND AFFILIATIONS
1. Center for Social Complexity and Department of Computational Social Science,
George Mason University, 4400 University Drive, MS 6B2, 22030-4400, Fairfax,
VA, USA
William G. Kennedy
2. University of Arkansas at Little Rock, 2801 South University Avenue, EIT
Building, Room 553, 72204, Little Rock, AR, USA
Nitin Agarwal
3. Department of Computer Engineering, Rochester Institute of Technology, 83
Lomb Memorial Drive, Bldg 09, 14623-5603, Rochester, NY, USA
Shanchieh Jay Yang
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CITE THIS PAPER
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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* Online ISBN: 978-3-319-05579-4
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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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