This study introduces a novel approach for EUR/USD exchange rate forecasting that integrates deep learning, textual analysis, and particle swarm optimization (PSO). By incorporating online news and analysis texts as qualitative data, the proposed PSO-LSTM model demonstrates superior performance compared to traditional econometric and machine learning models. The research employs advanced text mining techniques, including sentiment analysis using the RoBERTa-Large model and topic modeling with LDA. Empirical findings underscore the significant advantage of incorporating textual data, with the PSO-LSTM model outperforming benchmark models such as SVM, SVR, ARIMA, and GARCH. Ablation experiments reveal the contribution of each textual data category to the overall forecasting performance. The study highlights the transformative potential of artificial intelligence in finance and paves the way for future research in real-time forecasting and the integration of alternative data sources.

本研究针对欧元/美元汇率预测中缺乏有效数据整合的问题，提出了一种结合深度学习、文本分析和粒子群优化（PSO）方法的新模型。通过引入在线新闻和分析文本作为定性数据，PSO-LSTM模型在预测效果上显著优于传统的计量经济学和机器学习模型，显示出人工智能在金融领域的变革潜力。

基于预训练语言模型和深度学习方法的欧元/美元汇率预测文本挖掘研究