Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance

Despite the success of machine learning models, the literature lacks their applications to identify the exploitation of non-public information. We address this gap by developing a tool, which ranks investors based on their suspiciousness. We achieve this by predicting the future trading decisions of retail investors based on their social connections in an insider network. Particularly, the high predictability of the investor's trading behavior with data on her/his social neighborhood can indicate that she/he takes advantage of her/his social connections and trades on non-public information. Our system captures complex and cyclical patterns in investor social networks with Graph Networks trained with full investor-level transaction data. We show that using data on social neighborhoods significantly improves the model's performance in predicting investors’ trading behavior. The results are robust regarding the different groups of investors, trading windows, trading directions, and outliers. We demonstrate the tool by ranking suspicious investors and companies, with 12 out of 153 companies having a statistically significant concentration of directors (p-value < 0.05) with suspicious trading behavior. The system provides regulators with a valuable tool for prioritizing market surveillance efforts.