Accurate financial market forecasting requires diverse data sources, including historical price trends, macroeconomic indicators, and financial news, each contributing unique predictive signals. However, existing methods often process these modalities independently or fail to effectively model their interactions. In this paper, we introduce Cross-Modal Temporal Fusion (CMTF), a novel transformer-based framework that integrates heterogeneous financial data to improve predictive accuracy. Our approach employs attention mechanisms to dynamically weight the contribution of different modalities, along with a specialized tensor interpretation module for feature extraction. To facilitate rapid model iteration in industry applications, we incorporate a mature auto-training scheme that streamlines optimization. When applied to real-world financial datasets, CMTF demonstrates improvements over baseline models in forecasting stock price movements and provides a scalable and effective solution for cross-modal integration in financial market prediction.
View on arXiv@article{pei2025_2504.13522,
title={ Cross-Modal Temporal Fusion for Financial Market Forecasting },
author={ Yunhua Pei and John Cartlidge and Anandadeep Mandal and Daniel Gold and Enrique Marcilio and Riccardo Mazzon },
journal={arXiv preprint arXiv:2504.13522},
year={ 2025 }
}