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 focu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2022-12-01
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| Series: | Data Science and Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764922000376 |
| _version_ | 1797809095297728512 |
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| author | Jingyi Liang Guozhu Jia |
| author_facet | Jingyi Liang Guozhu Jia |
| author_sort | Jingyi Liang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:47:27Z |
| format | Article |
| id | doaj.art-8248a4e69d104ea2a4c1c5f53bc9efa6 |
| institution | Directory Open Access Journal |
| issn | 2666-7649 |
| language | English |
| last_indexed | 2024-03-13T06:47:27Z |
| publishDate | 2022-12-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj.art-8248a4e69d104ea2a4c1c5f53bc9efa62023-06-08T04:19:57ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492022-12-0154187198China futures price forecasting based on online search and information transferJingyi Liang0Guozhu Jia1College of Physical and Electronics Engineering, Sichuan Normal University, Chengdu, 610000, ChinaCorresponding author.; College of Physical and Electronics Engineering, Sichuan Normal University, Chengdu, 610000, ChinaThe 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.http://www.sciencedirect.com/science/article/pii/S2666764922000376Futures price forecastingBaidu indexGoogle trendsTransfer entropyConsumer price indexGray wolf optimizer |
| spellingShingle | Jingyi Liang Guozhu Jia China futures price forecasting based on online search and information transfer Data Science and Management Futures price forecasting Baidu index Google trends Transfer entropy Consumer price index Gray wolf optimizer |
| title | China futures price forecasting based on online search and information transfer |
| title_full | China futures price forecasting based on online search and information transfer |
| title_fullStr | China futures price forecasting based on online search and information transfer |
| title_full_unstemmed | China futures price forecasting based on online search and information transfer |
| title_short | China futures price forecasting based on online search and information transfer |
| title_sort | china futures price forecasting based on online search and information transfer |
| topic | Futures price forecasting Baidu index Google trends Transfer entropy Consumer price index Gray wolf optimizer |
| url | http://www.sciencedirect.com/science/article/pii/S2666764922000376 |
| work_keys_str_mv | AT jingyiliang chinafuturespriceforecastingbasedononlinesearchandinformationtransfer AT guozhujia chinafuturespriceforecastingbasedononlinesearchandinformationtransfer |