Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism

TL;DR

This paper introduces MSGCA, a novel multimodal fusion framework with gated cross-attention for stock movement prediction, effectively integrating diverse data sources to improve prediction stability and accuracy.

Contribution

The study proposes a new architecture that robustly fuses multimodal stock data using gated cross-attention, addressing data sparsity and semantic conflicts.

Findings

Achieves up to 31.6% performance improvement on four datasets.

Outperforms existing methods in multimodal stock prediction.

Enhances stability of multimodal data fusion.

Abstract

The accurate prediction of stock movements is crucial for investment strategies. Stock prices are subject to the influence of various forms of information, including financial indicators, sentiment analysis, news documents, and relational structures. Predominant analytical approaches, however, tend to address only unimodal or bimodal sources, neglecting the complexity of multimodal data. Further complicating the landscape are the issues of data sparsity and semantic conflicts between these modalities, which are frequently overlooked by current models, leading to unstable performance and limiting practical applicability. To address these shortcomings, this study introduces a novel architecture, named Multimodal Stable Fusion with Gated Cross-Attention (MSGCA), designed to robustly integrate multimodal input for stock movement prediction. The MSGCA framework consists of three integral components: (1) a trimodal encoding module, responsible for processing indicator sequences, dynamic documents, and a relational graph, and standardizing their feature representations; (2) a cross-feature fusion module, where primary and consistent features guide the multimodal fusion of the three modalities via a pair of gated cross-attention networks; and (3) a prediction module, which refines the fused features through temporal and dimensional reduction to execute precise movement forecasting. Empirical evaluations demonstrate that the MSGCA framework exceeds current leading methods, achieving performance gains of 8.1%, 6.1%, 21.7% and 31.6% on four multimodal datasets, respectively, attributed to its enhanced multimodal fusion stability.