MagicNet: Memory-Aware Graph Interactive Causal Network for Multivariate Stock Price Movement Prediction

Quantitative trading is a prominent field that employs time series analysis today, attracting researchers who apply machine intelligence to real-world issues like stock price movement prediction. In recent literature, various types of auxiliary data have been integrated alongside stock prices to improve prediction accuracy, such as textual news and correlational information. However, they typically rely on directly related documents or symmetric price correlations to make predictions for a particular stock (we refer to as " self-influence"). In this paper, we propose a Memory-Aware Graph Interactive Causal Network (MagicNet) that considers both temporal and spatial dependencies in financial documents and introduces causality-based correlations between multivariate stocks in a hierarchical fashion. MagicNet involves a text memory slot for each stock to retain the most influential texts over time and contains a dynamic interaction graph based on causal relationships to aggregate interactive influences asymmetrically. We believe that MagicNet leverages influential texts across stocks and explores their interrelationships through a logical structure, improving predictions on multiple stocks (we refer to as "interactive-influence"). The effectiveness of MagicNet is demonstrated through experiments on three real-world datasets, where MagicNet outperforms existing state-of-the-art models, offering an intuitive framework for understanding how texts and correlations affect future stock prices.