Keywords
Abstract
Stock market prediction has traditionally relied on historical price and volume data, yet the rapidly expanding availability of alternative data sources—including financial news, earnings reports, social media sentiment, and even satellite imagery—presents unprecedented opportunities for improving predictive accuracy. The integration of these diverse data modalities with traditional market data requires sophisticated cross-modal learning frameworks that can effectively fuse heterogeneous information types while respecting the unique statistical properties of each modality. This paper proposes Multi-Modal Stock State Space Graph (MM-S3G), a framework that extends the Stock State Space Graph architecture through cross-modal attention mechanisms that integrate textual, visual, and traditional financial data for enhanced stock trend prediction. Our approach builds upon the S3G model introduced by Lu, Hu, and Zhang, incorporating modality-specific encoders, cross-attention fusion layers, and a unified graph representation of market dynamics. Through extensive experiments on benchmark datasets combining stock prices with news headlines, earnings call transcripts, and satellite-based economic indicators, we demonstrate that multi-modal fusion substantially improves prediction accuracy compared to price-only baselines, with cross-attention mechanisms enabling the model to learn meaningful correspondences between modalities. Our work contributes to the growing literature on alternative data in quantitative finance, providing a principled framework for integrating diverse data sources into graph-based market models.
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