Volatility forecasting using Deep Learning and sentiment analysis

TL;DR

This paper introduces a composite deep learning model combining sentiment analysis from Reddit headlines with historical volatility data to improve market volatility forecasts, showing market-specific benefits.

Contribution

It presents a novel composite model integrating CNN-based sentiment classification with LSTM-based volatility prediction, demonstrating improved accuracy over traditional methods.

Findings

Sentiment analysis enhances volatility forecast accuracy.

Market-specific benefits observed for sentiment inclusion.

Model tested on S&P 500 and BRICS indices.

Abstract

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.