Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

Eghbal Rahimikia, Stefan Zohren and Ser-Huang Poon

Papers from arXiv.org

Abstract: We examine whether financial news can improve realised volatility forecasting using a parsimonious NLP-based framework that incorporates specialised financial word embeddings alongside general-purpose alternatives. News-only forecasts contain useful predictive information but generally do not outperform strong volatility-history benchmarks. Crucially, combining stock-related news forecasts with a strong volatility-history benchmark lowers forecast losses for several specifications and increases realised utility, providing evidence consistent with forecast complementarity. Performance varies across news types, embedding representations, and volatility regimes. SHAP attributions associate forecast variation with economically interpretable firm-specific and macroeconomic news themes.

Date: 2021-08, Revised 2026-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk, nep-for, nep-isf, nep-mst and nep-rmg
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