Abstract
Stock ranking prediction is an effective method for screening high investment value stocks in the future and can strongly assist investors in making decisions. However, this task is also challenging. In recent years, the role of stock relationship information in ranking prediction has been gradually recognized, and the hypergraph has been introduced to analyze the complex group-wise relationships between stocks. However, the application of hypergraph to stock relationship analysis still faces two major issues: unreasonable hyperedge construction and inappropriate aggregation operation in graph convolution that does not conform to the actual market. To solve these problems, we propose an attribute-driven fuzzy hypergraph network (AFHGN). Compared to the traditional hypergraph, AFHGN provides the following advantages: (1) The incidence matrix is constructed via fuzzy clustering to describe the relationships between stocks more reasonably. (2) An attribute-driven gate unit is introduced in the graph convolution to simulate the influence of stocks in the real market. (3) Trend weights are created to enhance the ability of stock embeddings to represent trends. The effectiveness of our algorithm has been verified by conducting a large number of experiments on real data. In addition, the investment simulation proves that our algorithm has better profitability than the state-of-the-art (SOTA) algorithms.
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