Paper: arXiv 2504.13522

Abstract

Accurate forecasting in financial markets requires integrating diverse data sources, from historical prices to macroeconomic indicators and financial news. However, existing models often fail to align these modalities effectively, limiting their practical use. In this paper, we introduce a transformer-based deep learning framework, Cross-Modal Temporal Fusion (CMTF), that fuses structured and unstructured financial data for improved market prediction. The model incorporates a tensor interpretation module for feature selection and an auto-training pipeline for efficient hyperparameter tuning. Experimental results using FTSE 100 stock data demonstrate that CMTF achieves superior performance in price direction classification compared to classical and deep learning baselines. These findings suggest that our framework is an effective and scalable solution for real-world cross-modal financial forecasting tasks.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics, including transformers, tensor representations, and Lasso regression for feature selection, indicating high complexity. It demonstrates strong empirical rigor by using real-world FTSE 100 data, reporting specific performance metrics (precision, recall, F1), and mentioning a GitHub repository and collaboration with an industry partner (Stratiphy) for prototyping.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Cross-Modal Financial Forecasting"] --> B["Data Collection:<br>FTSE 100, Macro Indicators, News"]
  B --> C["Methodology:<br>Cross-Modal Temporal Fusion<br>Transformer Framework"]
  C --> D["Computational Process:<br>Tensor Interpretation &<br>Auto-Training Pipeline"]
  D --> E["Outcome:<br>Superior Price Direction<br>Classification"]
  E --> F["Conclusion:<br>Scalable Cross-Modal<br>Solution"]