Fusing Narrative Semantics for Financial Volatility Forecasting

2025-10-23

AI Summary

This paper addresses the challenges of heterogeneous modality alignment between time series and news text, as well as lookahead bias, in financial volatility forecasting. To this end, we propose the Multimodal Volatility Network (M2VN), which introduces a novel temporal alignment loss to enforce strict chronological consistency between numerical market features and semantic news representations. Crucially, news embeddings are generated by Time Machine GPT—ensuring they are strictly lagged relative to the prediction timestamp—to eliminate forward-looking information leakage. M2VN jointly models open-market indicators and temporally aligned news semantics. Empirical evaluations across multiple real-world datasets demonstrate that our method consistently outperforms state-of-the-art baselines, achieving an average 12.7% reduction in RMSE. The framework significantly enhances both predictive accuracy and robustness, offering more reliable support for financial risk early warning systems.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured news data. M2VN leverages the representational power of deep neural networks to address two key challenges in this domain: (i) aligning and fusing heterogeneous data modalities, numerical financial data and textual information, and (ii) mitigating look-ahead bias that can undermine the validity of financial models. To achieve this, M2VN combines open-source market features with news embeddings generated by Time Machine GPT, a recently introduced point-in-time LLM, ensuring temporal integrity. An auxiliary alignment loss is introduced to enhance the integration of structured and unstructured data within the deep learning architecture. Extensive experiments demonstrate that M2VN consistently outperforms existing baselines, underscoring its practical value for risk management and financial decision-making in dynamic markets.

Problem

• Fusing numerical and textual data for volatility forecasting
• Mitigating look-ahead bias in financial prediction models
• Aligning heterogeneous modalities through deep learning architecture

Innovation

• Fuses time series features with unstructured news data
• Uses Time Machine GPT for temporally consistent news embeddings
• Introduces auxiliary alignment loss for multimodal integration