Predicting the trading behavior of socially connected investors

  • Kestutis Baltakys (Creator)
  • Margarita Baltakiene (Creator)



We find that investors’ future trading decisions are driven by the patterns of their social neighborhood and the trading activity therein. Moreover, we provide evidence that investors weigh their social connections differently in terms of information transfer. Methodologically, we tackle the complex, cyclical patterns of investor social networks by graph neural networks, which allow us to propose a sophisticated way to predict the behavior of investors with data on their social connections. Our analysis is based on the unique data on observed social links through director (insider) positions on the same companies as well as links to family members, together with full investor-level market-wise transaction data. The data is available online at
Koska saatavilla14 heinäk. 2022

Field of science, Statistics Finland

  • 113 Tietojenkäsittely ja informaatiotieteet

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