China futures price forecasting based on online search and information transfer  

作　　者：Jingyi Liang Guozhu Jia 

机构地区：[1]College of Physical and Electronics Engineering,Sichuan Normal University,Chengdu,610000,China

出　　处：《Data Science and Management》2022年第4期187-198,共12页数据科学与管理（英文）

摘　　要：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 Internetbased 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.

关 键 词：Futures price forecasting Baidu index Google trends Transfer entropy Consumer price index Gray wolf optimizer Convolutional neural network Long short-term memory 

分 类 号：F42[经济管理—产业经济]