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Figure 1 | EPJ Data Science

Figure 1

From: Mapping individual behavior in financial markets: synchronization and anticipation

Figure 1

Method for generating the Synchronization and Anticipation networks. (Top-left) From the position time series \(N_{i}(t)\) and \(N_{j}(t)\) of each pair of investors i and j we determine the activity period of each investor (\(A_{i}\) and \(A_{j}\)) together with the corresponding overlapping period of activity \(A_{ij}\). (Top-center) Considering only values within the overlapping period we codify the position time series into symbols, using in this particular case with embedding dimension \(m=2\), to generate symbolic time series X and Y. (Top-right) We use these symbolic time series to compute the values for Mutual Information \(I_{ij}\) and Transfer of Entropy \(T_{ij}\). In parallel, we apply a bootstrapping process to X and Y to extract a distribution of null values \(I_{ij}^{\ast }\) and \(T_{ij}^{\ast }\), which we use to apply the FDR procedure explained in “Methods”. Then, we keep all values within the 95% Confidence Interval, manually setting the rest to 0 for the non-significant, to create the adjacency lists for Synchronization Network (Center-right) and Anticipation Network (Bottom-right) for REP market. The networks are generated considering investors as nodes and edge weight as the corresponding values of \(I_{ij}\) and \(T_{ij}\) respectively. Size of the nodes is proportional to the node degree for Synchronization network and to the out-degree for the Anticipation network. Numbers inside each node are used as an ID of the investor

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