  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.
