The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach

Abstract

In this paper, we extract the qualitative information from crude oil news headlines, and develop a novel VMD-BiLSTM model with investor sentiment indicator for crude oil forecasting. First, we construct a sentiment score considering cumulative effect from contextual data of oil news texts. Then, we adopt an event-based method and GARCH model to investigate the impact of news sentiment on returns and volatility. A non-recursive signal decomposition method, namely variational mode decomposition (VMD), is applied to decompose the historical crude oil return and volatility data into various intrinsic modes. After that, a bidirectional long short-term memory neural networks (BiLSTM) is introduced as the deep learning prediction model that integrates both the qualitative and quantitative model inputs. Our empirical results indicate that the shock of news sentiment significantly causes the fluctuation of oil futures prices, and news sentiment has an asymmetric impact on the volatility of oil futures. The incorporation of sentiment score is always helpful for improving the forecasting performances in all benchmark scenarios. Specifically, our proposed data-decomposition based deep learning model is more effective than several econometric and machine learning models.