Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
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Abstract (arXiv)
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
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 18 Aug 2025.
Builds on
10 of 15 references.
References · 10
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- 2024Qwen2.5 Technical Reportcited by 4,942
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- 2023YaRN: Efficient Context Window Extension of Large Language Modelscited by 601
- 2024Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysiscited by 15
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- 2020Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecastingcited by 5
- 2023Boosting the Performance of Transformer Architectures for Semantic Textual Similaritycited by 1
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Citing papers per year: 2025: 2.
2 papers.