China futures price forecasting based on online search and information transfer

Jingyi Liang · Guozhu Jia

37 citations0/0 files storedData Science and Management

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications. This paper 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), andlong 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, 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.

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References

2004Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards 2019CNNpred: CNN-based stock market prediction using a diverse set of variables 2020A CNN-LSTM-Based Model to Forecast Stock Prices 2020Stock price prediction using deep learning and frequency decomposition 2020Gold volatility prediction using a CNN-LSTM approach 2018Online big data-driven oil consumption forecasting with Google trends 2018Predicting the direction of stock markets using optimized neural networks with Google Trends 2020Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models

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