Paper: arXiv 2510.20699

Authors: Yaxuan Kong, Yoontae Hwang, Marcus Kaiser, Chris Vryonides, Roel Oomen, Stefan Zohren

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.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper utilizes deep learning and specialized loss functions, indicating moderate mathematical complexity, but places a strong emphasis on data handling, look-ahead bias mitigation, and backtesting against baselines, making it highly implementation-heavy.

Research Flowchart

  flowchart TD
  A["Research Goal: Forecast Financial Volatility using<br>Unstructured News + Structured Market Data"]
  B["Inputs: Price Series &<br>Point-in-Time News (Time Machine GPT)"]
  C["Methodology: M2VN<br>Multi-Modal Volatility Network"]
  D["Key Innovation:<br>Auxiliary Alignment Loss"]
  E["Computation:<br>Deep Learning Fusion & Alignment"]
  F["Outcome: Consistent Outperformance<br>Mitigates Look-ahead Bias"]

  A --> B
  B --> C
  C --> D
  D --> E
  E --> F

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2 of 46 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.