  Research on crude oil price forecasting has attracted tremendous attention
from scholars and policymakers due to its significant effect on the global
economy. Besides supply and demand, crude oil prices are largely influenced by
various factors, such as economic development, financial markets, conflicts,
wars, and political events. Most previous research treats crude oil price
forecasting as a time series or econometric variable prediction problem.
Although recently there have been researches considering the effects of
real-time news events, most of these works mainly use raw news headlines or
topic models to extract text features without profoundly exploring the event
information. In this study, a novel crude oil price forecasting framework,
AGESL, is proposed to deal with this problem. In our approach, an open domain
event extraction algorithm is utilized to extract underlying related events,
and a text sentiment analysis algorithm is used to extract sentiment from
massive news. Then a deep neural network integrating the news event features,
sentimental features, and historical price features is built to predict future
crude oil prices. Empirical experiments are performed on West Texas
Intermediate (WTI) crude oil price data, and the results show that our approach
obtains superior performance compared with several benchmark methods.
