Social trading offers individual investors a new access to financial markets. In the aftermath of the Global Financial Crisis, several social trading platforms emerged. As of January 2016, more than 30 platforms worldwide offer several millions of registered users access to financial markets. Social trading is based on the idea of the sharing economy. Investors share information and investment strategies and trust the wisdom of the crowd: the many are able to make superior decisions than an individual. The project studies the value of information shared on social trading platforms and the impact of social trading on financial markets.

In a first paper, I analyze, together with two colleagues, whether the data and opinions published on a crowd-sourced content service for financial markets contain valuable information for future investment decisions. Our analysis in “Swarm intelligence? Stock opinions of the crowd and stock returns” provides strong evidence that this is indeed the case. Based on algorithms with the minimum regret property we demonstrate why a crowd consensus can have predictive power over market prices. Our argument is that the platform is designed more efficiently for prediction than financial markets as the online platform facilitates a transparent exchange of information and uses an aggregator algorithm which is optimized for prediction rather than the redistribution of wealth.

In a second paper, I study the interaction-based relations of traders from a large social trading platform. On many social trading platforms, traders can observe and duplicate, manually or automatically, the trading strategies of other users. These copying and mirroring activities constitute activity-based relations of investors in a network model. Using individual trader’s transaction data, I study the determinants of these relations. Moreover, I identify the driving forces and the opinion leaders within the online trading network as the nodes with the highest centrality and the highest force of infection, respectively. Relying on recent insights from epidemiological research, I maintain that centrality identifies the most central traders in the network, while the expected force quantifies the most influential traders and their spreading power. In the paper, I study the behavior and characteristics that set central and influential traders apart from other traders. As social trading gains market share and trading volume from social trading platforms increases, influential investors may influence market outcomes.

Building on the second paper, I study (together with a co-author) the determinants of relationships between investors in social trading networks. We observe that–similar to the literature on mutual fund flow–relationships are mostly determined by past performance. In addition, our analyses reveal a difference in the advisor selection process between established and new traders. Moreover, as previous literature shows that many users lose money on these platforms, we study how investors can make the most of social trading. Our results indicate that investors can significantly raise the probability to increase their trading wealth by selecting traders based on the centrality of a user as measured by PageRank or past performance, not by selecting them on the number of other advisees.


  • The research project is supported by the Fritz Thyssen Stiftung (Grant number


  • Pelster, M. (2017): ‘I’ll Have What S/he’s Having: A Case Study of a Social Trading Network’, Proceedings of the International Conference on Information Systems 2017.

Working Paper:

  • Breitmayer, B.; Massari, F.; Pelster, M. (2016): ‘Swarm intelligence? Stock opinions of the crowd and stock returns’.
  • Kapraun, J.; Pelster, M. (2017): ‘Relying on others’ investment skills: About the dynamics of relationships in social trading networks’.