Predicting the trading behavior of socially connected investors

Eight data sets that differ in whether they are used to predict investor trading in the same or subsequent periods with observations about the trading of neighbors in the social network. The periods are either daily or weekly windows. Moreover, we investigate separately investor influence over purchase and sale transactions. These three differences lead to eight distinct data sets. The size of the data sets ranges from just below 2,400 to almost 22 thousand observations. The labels are positive (set equal to 1) if an investor on a given day traded specific security in the same direction as at least one of his neighbors and negative (set equal to 0) otherwise. We use a sliding window with the size corresponding to the prediction window. In each window, for each ego investor, we create observations of instances of social influence in the neighborhood, given that at least one of the neighbors is active. An ego investor can be understood as a tippee and her neighbors as tippers. We record the specific behavior of investors in their neighborhood and, depending on the prediction period, the ego investors' behavior in the same or subsequent period. Initially, the data sets were highly imbalanced in terms of labels and, for this reason, were re-sampled to achieve a 1:3 label balance ratio.