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.

翻译：本研究提出了一种融合深度学习、文本分析与粒子群优化（PSO）的欧元/美元汇率预测新方法。通过引入在线新闻与分析文本作为定性数据，所提出的PSO-LSTM模型相较于传统计量经济学与机器学习模型展现出更优性能。研究采用了先进的文本挖掘技术，包括基于RoBERTa-Large模型的情感分析及LDA主题建模。实证结果突显了融入文本数据的显著优势，PSO-LSTM模型在预测性能上超越了SVM、SVR、ARIMA及GARCH等基准模型。消融实验揭示了各类文本数据对整体预测性能的贡献程度。本研究彰显了人工智能在金融领域的变革潜力，并为实时预测与多元数据源融合的未来研究方向开辟了道路。