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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Time-Aware Centralities and Embeddings of Nodes for Influence Prediction in Evolving Socio-Financial Networks |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Financial markets are complex and constantly evolving systems, where investors make decisions based on market conditions, company performance, and global economic trends. However, recent studies suggest that peer effects can also play a significant role in shaping investment decisions. Peer effects refer to the influence that one's peers have on their decision-making, and in the context of financial decision-making, can cause investors to follow trends in herding behavior. This influence process can result in cascading behavior, where the actions of a few investors can trigger a chain reaction of buying or selling, leading to significant price movements. The impact of peer effects has been amplified by social networks that have revolutionized the way we communicate and share information. In this thesis, we investigate the role of peer effects in financial markets and their impact on cascading behavior. Using a real-life evolving socio-financial network, we aim to quantify the extent to which individual investors influence the generation of cascading behavior, with a particular focus on the spatio-temporal features of individual investors within the network. We formulate a prediction task that forecasts the influence of individual users by utilizing various centrality measures and time-aware node embeddings. We evaluate the effectiveness of these centrality measures and time-aware node embeddings in predicting the influence of users in generating cascades of trades through the network. Our study contributes to a better understanding of the spatio-temporal factors that facilitate cascading behavior in financial markets, highlighting the need to understand their impact in various contexts, including real-life socio-financial networks. |
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