# Volatility forecasting using Deep Learning and sentiment analysis

arXiv: https://arxiv.org/abs/2210.12464 · HTML: https://mlsanity.com/paper/2210.12464/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing](https://mlsanity.com/paper/1912.07700/) (2019)
- [Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning](https://mlsanity.com/paper/2110.03393/) (2021)
- [Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion](https://mlsanity.com/paper/2203.03279/) (2022)
- [Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization](https://mlsanity.com/paper/2203.05673/) (2022)

## Built on by (most cited first, 1 of 1)

- [HAN-Mamba: Hierarchical Selective State Space Networks for Multi-Scale Financial Volatility Forecasting](https://mlsanity.com/paper/2610.10323/) (2026)

## Abstract (arXiv)

Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. To classify public sentiment, we use a Convolutional Neural Network, which obtained data from Reddit global news headlines. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day's volatility to make forecasts. We employed this method on the past volatility of the S&P500 and the major BRICS indices to corroborate its effectiveness. Our results demonstrate that including sentiment can improve Deep Learning volatility forecasting models. However, in contrast to return forecasting, the performance benefits of including sentiment appear for volatility forecasting appears to be market specific.