Fusing Narrative Semantics for Financial Volatility Forecasting

Battle Count: 110

Highly relevant to quantitative trading as volatility forecasting is fundamental to risk management, options pricing, position sizing, and portfolio optimization. The multi-modal approach combining price data with news narratives provides a more comprehensive market view. The look-ahead bias mitigation via TiMaGPT is particularly important for backtesting validity. The model's ability to capture volatility spikes and timing of sharp upward shifts is directly applicable to trading strategies. However, the daily frequency and 1-step-ahead horizon limit direct application to high-frequency trading.

The architecture involves multiple components: multi-modal feature encoder, TimesNet backbone with spectral filtering, gated cross-modal fusion, InfoNCE alignment loss, and volatility projection head. Requires access to TiMaGPT model weights, FNSPID news dataset, and careful temporal alignment of news with price data. Hyperparameter tuning involves multiple dimensions (latent dimension, alignment dimension, temperature, inception width, top-k signal). The training pipeline with year-by-year TiMaGPT weight advancement adds complexity. However, code is publicly available.

The paper provides a public GitHub repository with preprocessed dataset, raw news article data, and implementation code. Training details including hyperparameter search grids, learning rates, and evaluation metrics are clearly specified. Three random seeds are used for each experiment. However, the exact TiMaGPT model weights and FNSPID dataset access may require additional steps.