China futures price forecasting based on online search and information transfer

The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications. This study combines data from the Baidu index (BDI), Google trends (GT), and transfer entropy (TE) to forecast a wide range of futures prices with a focus on China. A forecasting model based on a hybrid gray wolf optimizer (GWO), convolutional neural network (CNN), and long short-term memory (LSTM) is developed. First, Baidu and Google dual-platform search data were selected and constructed as Internet-based consumer price index (ICPI) using principal component analysis. Second, TE is used to quantify the information between online behavior and futures markets. Finally, the effective Internet-based consumer price index (ICPI) and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn, soybean, polyvinyl chloride (PVC), egg, and rebar futures. The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices. Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data. Our proposed framework can provide predictive decision support for government leaders, market investors, and production activities.