跨市场跨来源情感分析驱动的人民币汇率预测研究  

RMB Exchange Rate Forecasting Driven by Cross-Market and Cross-Source Sentiment Analysis

作　　者：操玮 廖臣悦 张福伟 Cao Wei;Liao Chenyue;Zhang Fuwei(School of Economics,Hefei University of Technology,Hefei 230601,China;School of Management,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]合肥工业大学经济学院,合肥230601 [2]合肥工业大学管理学院,合肥230601

出　　处：《数据分析与知识发现》2023年第12期75-87,共13页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:71801072);中央高校基本科研业务费专项资金资助合肥工业大学青年教师科研创新启动专项项目(项目编号:JZ2020HGQA0181)的研究成果之一。

摘　　要：[目的]将跨市场跨来源情感分析引入人民币汇率预测模型中,提升汇率趋势的预测效果。[方法]构建融合跨市场跨来源情感分析的CCSA-DL模型:采用BERT-TextCNN模型分别提取中美两国官方媒体与个人投资者的深层情感特征,并与基于LSTM的汇率时序深层特征实现融合共享,在此基础上借助SVM模型实现汇率预测。[结果]与基线模型相比,CCSA-DL模型在预测指标和经济收益的表现上均达到最优,尤其与LSTM预测模型对比,在3个评价指标上有平均约16.77%的提升。[局限]情感分析数据来源有待进一步拓展和优化。[结论]引入跨市场跨来源情感分析的CCSA-DL模型具有较优的汇率预测效果和经济收益。[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.

关 键 词：人民币汇率预测 跨市场跨来源情感分析 深度学习 

分 类 号：F832[经济管理—金融学] G350[文化科学—情报学]