Abstract
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.
Highlights
Crude oil plays a significant role in the global economy, for nearly one-third of the world’s energy consumption comes from it
The proposed framework, AGESL, is a hybrid framework that integrates the mean price predicted by Autoregressive Integrated Moving Average (ARIMA), volatility forecasted by GARCH, sentiment, and the price predicted by LSTM module, by using a fully connected neural network
From the above discussion and analysis, we can conclude that with the benefits of the Event Extraction module, the proposed framework AGESL significantly outperforms all other models listed in this paper in terms of RMSE, MAPE, DS, and DM test
Summary
Crude oil plays a significant role in the global economy, for nearly one-third of the world’s energy consumption comes from it. Overall, existing research on crude oil price forecasting can be categorized into three main classes: econometric models (including time-series models), machine learning or deep learning methods, and hybrid approaches. Huang et al.: Forecasting Crude Oil Price Using Event Extraction (NNs) are the most typical machine learning methods due to their extraordinary ability in modeling nonlinearity and volatility [13], [14]. For Attach event, "Baghdad", "cameraman", "American tank" and "Palestine Hotel" are its arguments with corresponding role Place, Victim, Instrument and Target, respectively This is a somewhat more complex example with three arguments shared, which is more challenging than the simple case with one event type in one sentence. (3) A novel framework AGESL that integrates multi-channel information (e.g., historical prices, latent event types, event arguments, and four sentimental factors) is proposed for forecasting crude oil price series.
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