Sentiment-aware volatility forecasting

Abstract

Recent advances in the integration of deep recurrent neural networks and statistical inferences have paved new avenues for joint modeling of moments of random variables, which is highly useful for signal processing, time series analysis, and financial forecasting. However, introducing explicit knowledge as exogenous variables has received little attention. In this paper, we propose a novel model termed sentiment-aware volatility forecasting (SAVING), which incorporates market sentiment for stock return fluctuation prediction. Our framework provides an ensemble of symbolic and sub-symbolic AI approaches, that is, including grounded knowledge into a connectionist neural network. The model aims at producing a more accurate estimation of temporal variances of asset returns by better capturing the bi-directional interaction between movements of asset price and market sentiment. The interaction is modeled using Variational Bayes via the data generation and inference operations. We benchmark our model with 9 other popular ones in terms of the likelihood of forecasts given the observed sequence. Experimental results suggest that our model not only outperforms pure statistical models, e.g., GARCH and its variants, Gaussian-process volatility model, but also outperforms the state-of-the-art autoregressive deep neural nets architectures, such as the variational recurrent neural network and the neural stochastic volatility model.