Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

Eghbal Rahimikia; Stefan Zohren; Ser-Huang Poon

q-fin.CPAug 1, 2021

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

Authors: Eghbal Rahimikia, Stefan Zohren, Ser-Huang Poon

Organizations: Alliance Manchester Business School at the University of Manchester · Department of Engineering Science, University of Oxford · Alliance Manchester Business School, University of Manchester

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.

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Jun 10, 2026 · cs.CLJ/K move · Enter open · S save

Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI

Ali M Karaoglu, Shreyank N Gowda

Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit this problem using a zero-shot natural language processing framework, investigating whether models can extract actionable signals from financial news without domain-specific training. We design a structured pipeline that combines zero-shot natural language inference with temporal aggregation, explicitly modelling recency and event-dependent impact horizons when integrating information across articles. To address the need for transparency in high-stakes settings, we introduce a multi-layered explainability framework that links predictions to token-level, article-level, and aggregate evidence, and produces grounded natural language rationales. Across multiple models and prediction horizons, we find that zero-shot approaches consistently fail to outperform simple baselines, with particularly weak performance on negative movements, suggesting deeper structural limitations in mapping news sentiment to short-term price dynamics. However, explainability signals reliably distinguish between trustworthy and unreliable predictions, offering practical value even when accuracy is limited. These findings highlight the limits of zero-shot financial NLP and motivate a shift toward decision-support systems that prioritise transparency and uncertainty awareness. Code: https://github.com/alimert05/zero-shot-stock-xai

Financial Sentiment Analysis Zero-Shot Forecasting Market Next-Token Prediction Stochastic Volatility Explainable Artificial Intelligence Natural Language Processing Sentiment Analysis

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Abstract

Explore similar work

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