# Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

arXiv: https://arxiv.org/abs/2108.00480 · HTML: https://mlsanity.com/paper/2108.00480/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 67 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://mlsanity.com/paper/1904.04912/) (2019)
- [Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection](https://mlsanity.com/paper/2105.13727/) (2021)
- [SHAP values for Explaining CNN-based Text Classification Models](https://mlsanity.com/paper/2008.11825/) (2020)
- [Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units](https://mlsanity.com/paper/2105.10430/) (2021)
- [Building Cross-Sectional Systematic Strategies By Learning to Rank](https://mlsanity.com/paper/2012.07149/) (2020)

## Built on by (most cited first, 7 of 7)

- [Can LLMs Learn Macroeconomic Narratives from Social Media?](https://mlsanity.com/paper/2406.12109/) (2024)
- [DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions](https://mlsanity.com/paper/2210.04797/) (2022)
- [Word Embedding for Social Sciences: An Interdisciplinary Survey](https://mlsanity.com/paper/2207.03086/) (2022)
- [Fusing Narrative Semantics for Financial Volatility Forecasting](https://mlsanity.com/paper/2510.20699/) (2025)
- [Regime-aware financial volatility forecasting via in-context learning](https://mlsanity.com/paper/2603.10299/) (2026)
- [Deep Learning Enhanced Realized GARCH](https://mlsanity.com/paper/2302.08002/) (2023)
- [Optimal Text-Based Time-Series Indices](https://mlsanity.com/paper/2405.10449/) (2024)

## Abstract (arXiv)

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.