Existing works do not facilitate the identification of causalities in investors’ collective behaviors. We contributed to state of the art a new method for inferring investor networks based on the linear Granger Causality under a vector autoregressive (VAR) model, which can capture the trading similarities and information flows with a time lag in an investor network. This research provides new empirical results on the flows of information among investors.
Second, investor networks are often inferred for one specific security. We contributed to state of the art a multilayer network technique that can perform a market-wide analysis across multiple securities (see D1.1 the technical report for details).
Third, the current literature has barely addressed how the structure of investor networks reflects market conditions, which we did in this work package with an extensive data set. We contributed to the current literature on investor networks a method for comparing investor networks accurately. Investor networks have different features, and methods for comparing them have never been addressed before. Many investor nodes enter and leave the system before and during the crisis, making a topological comparison a potentially better methodological choice to reveal similar trading patterns. Investor networks between pre-crisis and crisis periods have very different structures. Moreover, the herding tendency, high synchronization in trade timing, and the emergence of investor hubs are observed during the crisis (see D2.1 the technical report for details).
Fourth, we contributed to the state of the art a method to capture the correlation between topological changes and stock price fluctuation. The most related works studied associations between the dynamically changing investor stock trading networks and stock price dynamics. In these works, the correlation relationships are unclear, or more robust statistical tests are needed to conclude such relationships. Some models can address this issue but do not fit to the low-frequency data that we use in this work (see D2.2 the technical report for details).
Lastly, we improved the algorithm for clique detection and analyzed the investor cliques. Furthermore, we contribute a method to track changes of investor clusters and cliques over time (see D3.1 the technical report for details).
