用新闻文本+AI模型预测欧元美元汇率，效果优于传统方法
EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods
- 结合新闻情感与主题分析，用PSO优化LSTM模型
- 相比SVM、ARIMA等模型，预测误差降低12.3%
- 适合量化交易员和金融数据分析者参考
本研究提出一种融合深度学习、文本挖掘与粒子群优化（PSO）的EUR/USD汇率预测新方法。通过引入在线新闻和分析文本作为定性数据，采用RoBERTa-Large进行情感分析，LDA进行主题建模，构建PSO-LSTM模型。实证结果表明，该模型显著优于SVM、SVR、ARIMA和GARCH等基准模型。消融实验揭示了各类文本数据对整体性能的贡献。研究验证了人工智能在金融领域的变革潜力，为实时预测与多源异构数据融合提供了新路径。
原文摘要 · Abstract (English)
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.
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