Volatility forecasting using Deep Learning and sentiment analysis
This work addresses volatility prediction for financial markets, but it is incremental as it builds on existing deep learning methods by adding sentiment analysis.
The paper tackled volatility forecasting by combining deep learning with sentiment analysis from Reddit headlines, finding that including sentiment improves forecasts but the benefits are market-specific.
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.