Journal of Information Technology

, Volume 33, Issue 1, pp 19–30 | Cite as

Effects of transparency: analyzing social biases on trader performance in social trading

  • Florian GlaserEmail author
  • Marten Risius
Research Article


Social Trading platforms combine the trading functionalities of classical online broker services with the communication and interaction features of social networks. Next to following other users’ profiles, a main characteristic of social trading platforms is the possibility to follow other users by automatically copying their trades. By being a technologically based financial intermediary that enables individual profit maximization, social trading platforms constitute a contemporary example of financialization. Our empirical analysis of the behavior of traders on a social trading platform provides new insights on financialization related questions regarding the influence of transparency and interaction in delegated investment environments. The disposition effect is a well-studied behavioral bias of investors and traders. Human investors tend to realize returns of their winning positions too early and let unfavorable positions accumulate losses for too long. We find that on social trading platforms the traders’ sensitivity to the disposition effect is influenced by the amount of attention they receive from their followers who invested capital into the traders’ strategy. These novel insights propose a link between principal-agent theory and the disposition effect induced by transparency mechanisms. We extend the literature on trader-investor interaction channels in social trading networks. The results obtained in a social network environment are of high relevance for regulators who have a strong focus on customer protection and financial services regulation. They also provide guidelines for platform designers, traders, investors and social trading platform operators.


social trading digitalization of financial services financialization disposition effect agency theory individual trading performance 


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Copyright information

© Association for Information Technology Trust 2016

Authors and Affiliations

  1. 1.Goethe University FrankfurtFrankfurt am MainGermany
  2. 2.University of MannheimMannheimGermany

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