Technical gazette, Vol. 31 No. 1, 2024.
Original scientific paper
https://doi.org/10.17559/TV-20230801000842
CBISI-LSTM Deep Learning Model for Short-term Cross-border Capital Flow Prediction
Yuchen Xiong
; School of Economics and Management, Southeast University, Nanjing 211189, China
Yihang Chu
; School of Economics and Management, Southeast University, Nanjing 211189, China
Keyang Zhan
; School of Economics and Management, Southeast University, Nanjing 211189, China
Bixuan Liu
; School of Mathematics, Southeast University, Nanjing 211189, China
Gang Xue
; School of Economics and Management, Tsinghua University, Beijing 100084, China
*
* Corresponding author.
Abstract
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.
Keywords
cross-border funds; data mining; investor sentiment; operation management; LSTM
Hrčak ID:
312903
URI
Publication date:
31.12.2023.
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