In this study we propose an approach for individual investor to easily follow the value investors’ strategies based on the network analysis and machine learning on the bipartite investor-stock network constructed from a dataset of 13-F ﬁlings: quarterly investment information collected by SEC from institutional investors. We convert the bipartite graph into a directed investor-investor network, apply several analyses relating to Motif, PageRank and HITs, and detect secure investor communities by Louvain algorithm. We also apply JODIE, a temporal graph neural network model, on the original bipartite investor-stock graph to predict future investments of those investor communities. We then use these predictions to create a portfolio recommendation for individual investors.
2019-10 - 2019-12
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