Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

[2108.00480] Realised Volatility Forecasting: Machine Learning via Financial Word Embedding Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Finance > Computational Finance arXiv:2108.00480 (q-fin) [Submitted on 1 Aug 2021 ( v1 ), last revised 31 Aug 2026 (this version, v7)] Title: Realised Volatility Forecasting: Machine Learning via Financial Word Embedding Authors: Eghbal Rahimikia , Stefan Zohren , Ser-Huang Poon View a PDF of the paper titled Realised Volatility Forecasting: Machine Learning via Financial Word Embedding, by Eghbal Rahimikia and 2 other authors View PDF HTML (experimental) 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. Subjects: Computational Finance (q-fin.CP) ; Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2108.00480 [q-fin.CP] (or arXiv:2108.00480v7 [q-fin.CP] for this version) https://doi.org/10.48550/arXiv.2108.00480 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.2139/ssrn.3895272 Focus to learn more DOI(s) linking to related resources Submission history From: Eghbal Rahimikia [ view email ] [v1] Sun, 1 Aug 2021 15:43:57 UTC (8,349 KB) [v2] Wed, 1 Mar 2023 19:47:21 UTC (12,471 KB) [v3] Fri, 15 Nov 2024 12:28:11 UTC (13,832 KB) [v4] Tue, 19 Nov 2024 17:33:22 UTC (13,838 KB) [v5] Thu, 8 Jan 2026 00:40:43 UTC (3,678 KB) [v6] Mon, 13 Apr 2026 23:00:19 UTC (3,678 KB) [v7] Mon, 31 Aug 2026 20:19:10 UTC (3,701 KB) Full-text links: Access Paper: View a PDF of the paper titled Realised Volatility Forecasting: Machine Learning via Financial Word Embedding, by Eghbal Rahimikia and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-fin.CP < prev | next > new | recent | 2021-08 Change to browse by: cs cs.CL cs.LG q-fin References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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