Mining for the Preference of Funds based on Subgraph Embedding of Fund-Stock Networks

The preference of fund managers on various stocks forms the inner structure of the capital market. Data mining for the preference of funds from financial big data has attracted significant attention. In this paper, we study the preference features of Chinese capital market through mutual fund holdings data from 2010 to 2019. Complex fund-stock network structures are constructed according to the intersection of mutual fund managers' holdings. Further, a sub-graph extracting and embedding technology is introduced to make a quantitatively description of the preference of funds. Based on these embedding results, general fund correlation network can be constructed. The structure characteristics are demonstrated to be strongly correlated with performance of the funds. Empirical evidence from the financial data verifies the effectiveness of the proposed method.