Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
This work addresses financial forecasting for investors by providing a scalable multimodal approach, though it is incremental as it builds on existing fusion strategies.
The paper tackles stock movement prediction by integrating sentiment-enriched news embeddings with market indicators using a multimodal framework, resulting in improved performance over numeric-only baselines in backtesting.
We propose STONK (Stock Optimization using News Knowledge), a multimodal framework integrating numerical market indicators with sentiment-enriched news embeddings to improve daily stock-movement prediction. By combining numerical & textual embeddings via feature concatenation and cross-modal attention, our unified pipeline addresses limitations of isolated analyses. Backtesting shows STONK outperforms numeric-only baselines. A comprehensive evaluation of fusion strategies and model configurations offers evidence-based guidance for scalable multimodal financial forecasting. Source code is available on GitHub