[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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2019-10-6
GM and the UAW have moved closer to a deal to end the strike.GM and UAW Near Deal to End Strike as Pay Issues Remain.General Motors Co. and the United Auto Workers moved closer to a deal that would end the strike that’s divided them for almost three weeks.
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