A Universal and Interpretable Method for Enhancing Stock Price Prediction

The prediction of stock prices is a highly sought-after topic in the data mining field. In recent decades, many promising methods have been proposed and widely adopted for stock price prediction. However, these methods have inherent limitations, such as low accuracy, lack of transparency, and failure to consider the interactions among stock factors. To address these issues, we propose a UNIversal and interpretable framework for enhancing Stock Price Prediction (abbreviated to UniSPP), which is capable of modeling the interactions among stock factors. UniSPP first builds a fully connected graph, where the nodes and edges are the stock factors and interactions between them, respectively. However, it is a non-trivial task to discover a proper feature interaction subgraph from a large space, especially in discrete graph modeling. Therefore, UniSPP proposes a novel idea to mine the real factor interactions by iteratively sampling subgraphs and optimizing the sampling controller. Empirical studies show that our framework can be incorporated with many popular forecasting models and can effectively discover the suitable factor interaction, which can significantly improve the prediction results of existing models.