Long-Context and Multimodal Language Models for Financial Forecasting

Дата публикации: 27-08-2026 00:00:00

Financial markets produce vast amounts of multimodal data, including textual sources, such as financial news, earnings conference calls, and financial reports, coupled with structured numerical data, such as market prices and accounting statements. This combination of rich multimodal inputs and the natural availability of market-based supervision presents a compelling opportunity for the development of machine learning and natural language processing methods. However, financial data exhibits several unique challenges. Financial documents are long, complex, and domain-specific. The data has strong temporal structure and information must be interpreted in the context of prior related events. In addition, textual and numerical modalities must be jointly modeled to capture their unique patterns and interactions. While existing language models are remarkably capable, they remain limited in their ability to address these challenges, as they struggle with long-range temporal dependencies,...

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