Predicting the Trading Behavior of Socially Connected Investors

Kęstutis Baltakys*, Margarita Baltakienė, Negar Heidari, Alexandros Iosifidis, Juho Kanniainen

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearch

Abstract

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. We make the anonymized data set open to the public.
Original languageEnglish
JournalSSRN Electronic Journal
Publication statusSubmitted - 2023

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