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Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index

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  • Lin, Yong
  • Wang, Renyu
  • Gong, Xingyue
  • Jia, Guozhu

Abstract

We applied the multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlation behaviour between the USD/CNY exchange rate (UCER) and the Baidu index (Chines netizens’ behaviour big data, BI). We observed a significant anti-persistent cross-correlation between UCER and BI through MF-DCCA testing. Based on the cross-correlation between UCER and BI, a hybrid deep learning model WOA-STL-BI-LSTM combining network big data, decomposition and integration techniques, optimisation algorithms and LSTM is constructed to predict UCER returns, where BI is used as a potential predictor to predict UCER, and the predictability of BI on UCER was discussed. The experimental results show that BI can improve the forecasting accuracy of UCER returns, but not significantly. WOA-STL-BI-LSTM has a significant improvement in forecasting performance compared with the benchmark model and can be used as an advanced model for UCER returns forecasting. This paper provides a new approach and evidence for the study of UCER.

Suggested Citation

  • Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122004575
    DOI: 10.1016/j.physa.2022.127686
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