CBISI-LSTM Deep Learning Model for Short-term Cross-border Capital Flow Prediction
With the drastic fluctuation of the international financial market in recent years, the cross-border capital flow between Shanghai and Hong Kong has become increasingly active. The lack of effective and timely tracking monitoring and scientific management of cross-border capital flow in the capital...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2024-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/451999 |
| Summary: | With the drastic fluctuation of the international financial market in recent years, the cross-border capital flow between Shanghai and Hong Kong has become increasingly active. The lack of effective and timely tracking monitoring and scientific management of cross-border capital flow in the capital market will seriously affect the overall financial security of China's economy. This paper constructs the cross-border investor sentiment index CBISI based on principal component analysis and analyzes the impact of cross-border investor sentiment and cross-border capital flows by constructing the VAR model. In addition, CBISI is used as part of the input variable of LSTM to forecast the cross-border capital flow (NF). The findings of the study indicate that changes in cross-border investor sentiment will have a significant short-term impact on cross-border capital flows, and the addition of CBISI will improve the accuracy of cross-border flow estimates. |
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| ISSN: | 1330-3651 1848-6339 |