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| RMB Exchange Rate Forecasting Driven by Cross-Market and Cross-Source Sentiment Analysis |
Cao Wei1,Liao Chenyue1,Zhang Fuwei2( ) |
1School of Economics, Hefei University of Technology, Hefei 230601, China
2School of Management, Hefei University of Technology, Hefei 230601, China |
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Abstract [Objective] This study aims to introduce cross-market and cross-source sentiment analysis into the RMB exchange rate forecasting model to improve the performance. [Methods] We built a CCSA-DL model for fusing cross-market and cross-source sentiment analysis. First, we used a BERT-TextCNN model to extract deep sentiment features from China and the United States respectively. Then, we shared them with LSTM-based deep features of exchange rate time series to achieve deep fusion, based on which exchange rate forecasting is realized with the help of SVM model. [Results] Compared with the baseline model, the CCSA-DL model achieved optimal performance in predicting indicators and economic returns. Especially compared with the LSTM prediction model, there was an average improvement of about 16.77% in the three evaluation indicators. [Limitations] The source of sentiment analysis data needs to be further expanded and optimized. [Conclusions] The CCSA-DL model with cross-market and cross-source sentiment analysis has better exchange rate forecasting performance and economic returns.
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Received: 02 November 2022
Published: 12 September 2023
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| Fund:National Natural Science Foundation of China(71801072);Hefei University of Technology Project for Young Teachers’ Research and Innovation，the Fundamental Research Funds for the Central Universities(JZ2020HGQA0181) |
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Corresponding Authors:
Zhang Fuwei，ORCID：0009-0002-6193-6669，E-mail：zhfuwei@126.com。
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