JACIII Vol.22 No.2 pp. 224-235
doi: 10.20965/jaciii.2018.p0224


Discovering Expert Traders on Social Trading Services

Woonyeol Lee and Qiang Ma

Graduate School of Informatics, Kyoto University
Yoshidahonmachi, Sakyo-ku, Kyoto 606-8501, Japan

July 10, 2017
January 5, 2018
March 20, 2018
social trading services, discovering experts, data mining, financial information systems

Social trading services, which are financial services connected with social networking services, are currently in the spotlight. Users can follow and automatically imitate expert traders’ trades using social trading services. Finding expert traders who exhibit an exceptional and consistent performance for users to follow is a key challenge in this field. We propose a ranking mechanism with three measures to address this issue: performance, risk, and consistency. We estimated traders’ performance, risk, and consistency levels by comparatively analyzing their trading histories and news data. In addition, we propose a system called Whom to Follow (W2F) to help users discover expert traders by utilizing this ranking mechanism. W2F visualizes the ranking results, and provides feedback functions to help users reach decisions regarding who to follow. We conducted experiments to test and then validate the proposed ranking mechanism in terms of the ranking accuracy, profit, and ranking stability. We also conducted a user experiment to demonstrate the feasibility of W2F.*

* This paper is an extended version of “Whom to Follow on Social Trading Services? A System to Support Discovering Expert Traders,” 10th Int. Conf. on Digital Information Management (ICDIM) 2015, pp. 188-193.

Cite this article as:
W. Lee and Q. Ma, “Discovering Expert Traders on Social Trading Services,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.2, pp. 224-235, 2018.
Data files:
  1. [1] Social trading (Wikipedia), [accessed June 1, 2015]
  2. [2] W. Pan, Y. Altshuler, and A. Pentland, “Decoding Social Influence and the Wisdom of the Crowd in Financial Trading Network,” SocialCom/PASSAT, pp. 203-209, 2012.
  3. [3] eToro, [accessed June 1, 2015]
  4. [4] Zulutrade, [accessed June 1, 2015]
  5. [5] Y.-Y. Liu, J. C. Nacher, T. Ochiai, M. Martino, and Y. Altshuler, “Prospect Theory for Online Financial Trading,” APS March Meeting, pp. 1-22, 2014.
  6. [6] J. Bollen, H. Mao, and X. Zeng, “Twitter Mood Predicts the Stock Market,” J. of Computational Science, Vol.2, No.1, pp. 1-8, 2011.
  7. [7] T. Preis, D. Reith, and H. E. Stanley, “Complex Dynamics of Our Economic Life on Different Scales: Insights from Search Engine Query Data,” Philosophical Trans. of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol.368, No.1933, pp. 5707-5719, 2010.
  8. [8] Y.-H. Lui and D. Mole, “The Use of Fundamental and Technical Analyses by Foreign Exchange Dealers: Hong Kong Evidence,” J. of Int. Money and Finance, Vol.17, No.3, pp. 535-545, 1998.
  9. [9] A. El-Korany, “Integrated Expert Recommendation Model for Online Communities,” Int. J. of Web & Semantic Technology, Vol.4, No.4, pp. 19-29, 2013.
  10. [10] Stackoverflow, [accessed June 1, 2015]
  11. [11] G. A. Wang, J. Jiao, A. S. Abrahams, W. Fan, and Z. Zhang, “ExpertRank: A Topic-Aware Expert Finding Algorithm for Online Knowledge Communities,” Decision Support Systems, Vol.54, No.3, pp. 1442-1451, 2013.
  12. [12] J. Zhang, J. Tang, and J. Li, “Expert Finding in a Social Network,” DASFAA 2007, pp. 1066-1069, 2007.
  13. [13] E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, “Finding High-Quality Content in Social Media,” WSDM 2008, pp. 183-194, 2008.
  14. [14] J. Li, W. Peng, T. Li, T. Sun, Q. Li, and J. Xu, “Social Network User Influence Sense-Making and Dynamics Prediction,” Expert Systems with Applications, Vol.41, No.11, pp. 5115-5124, 2014.
  15. [15] K. Almgren and J. Lee, “A Hybrid Framework to Predict Influential Users on Social Networks,” Int. Conf. on Digital Information Management (ICDIM) 2015, pp. 103-108, 2015.
  16. [16] C. Macdonald, D. Hannah, and I. Ounis, “High Quality Expertise Evidence for Expert Search,” ECIR 2008, pp. 283-295, 2008.
  17. [17] Q. Ma and M. Yoshikawa, “Ranking People Based on Metadata Analysis of Search Results,” Web Information Systems Engineering (WISE) 2008 Workshops, pp. 48-60, 2008.
  18. [18] Y.-W. Cheung and C. Y.-P. Wong, “The Performance of Trading Rules on Four Asian Currency Exchange Rates,” Multinational Finance J., Vol.1, No.1, pp. 1-22, 1997.
  19. [19] A. Hirabayashi, C. Aranha, and H. Iba, “Optimization of the Trading Rule in Foreign Exchange Using Genetic Algorithm,” Proc. of the 11th Annual Conf. on Genetic and Evolutionary Computation, ACM 2009, pp. 1529-1536, 2009.
  20. [20] L. Bauwens, W. B. Omrane, and P. Giot, “News Announcements, Market Activity and Volatility in the Euro/Dollar Foreign Exchange Market,” J. of Int. Money and Finance, Vol.24, No.7, pp. 1108-1125, 2005.
  21. [21] F. Jin, N. Self, P. Saraf, P. Butler, W. Wang, and N. Ramakrishnan, “Forex-Foreteller: Currency Trend Modeling Using News Articles,” Proc. of the 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, ACM 2013, pp. 1470-1473, 2013.
  22. [22] SocialTradingGuru, [accessed June 1, 2015]
  23. [23] ForexFactory, [accessed June 1, 2015]
  24. [24] M. Dostert and D. Kelly, “Users’ Stopping Behaviors and Estimates of Recall,” SIGIR 2009, pp. 820-821, 2009.

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Last updated on Jul. 23, 2024