基于在线外汇新闻情感挖掘的汇率预测研究

In recent years, natural language processing (NLP) technology has been widely used to study the emotional polarity of unstructured text data such as financial news, financial commentary and social media, and the emotional polarity of these unstructured text data are utilized as proxy variables of investor sentiment to predict the volatility of financial market. Based on behavioral finance theory, an exchange rates forecasting with sentiment mining of online foreign exchange news is proposed in this dissertation by means of NLP and deep learning. This approach uses mutual information theory to construct the first sentiment lexicon in the field of foreign exchange. On the basis of sentiment lexicon of foreign exchange, the sentiment polarity of foreign exchange news is calculated by combining the basic lexicon constructed in this dissertation. The study shows that there is a Granger causality and long-term cointegration relationship between the sentiment polarity of foreign exchange news and USD/CNY exchange rate. Additionally, this study incorporates the sentiment polarity data of foreign exchange news and other financial data into deep learning approach. The empirical results show that our proposed approach has a significant effect on short-, medium-, and long-term volatility forecasting of USD/CNY exchange rate.