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 news can improve realised volatility forecasting using a modern yet operationally simple NLP framework. News text is transformed into embedding-based representations, and forecasts are evaluated both as a standalone, news-only model and as a complement to standard realised volatility benchmarks. In out-of-sample tests on a cross-section of stocks, news contains useful predictive information, with stronger effects for stock-related content and during high volatility days. Combining the news-based signal with a leading benchmark yields consistent improvements in statistical performance and economically meaningful gains, while explainability analysis highlights the news themes most relevant for volatility.

Date: 2021-08, Revised 2026-01
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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