现实波动率预测：通过金融词嵌入进行机器学习
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
AI总结：
本文研究新闻如何提升现实波动率预测，通过现代且操作简单的NLP框架，发现新闻内容在高波动日具有更强预测作用，结合领先指标可提升统计性能和经济收益。
摘要：
我们检验新闻是否能通过现代且操作简便的NLP框架提高现实波动率预测。新闻文本被转换为基于嵌入的表示，预测结果既作为独立的新闻模型评估，也作为标准现实波动率基准的补充。在跨股票样本的外推测试中，新闻包含有用的预测信息，与股票相关内容和高波动日相关性更强。将新闻信号与领先指标结合可带来一致的统计性能提升和经济意义的收益，可解释性分析突显对波动率最相关的新闻主题。
英文摘要：
We examine whether financial news can improve realised volatility forecasting using a parsimonious NLP-based framework that incorporates specialised financial word embeddings alongside general-purpose alternatives. News-only forecasts contain useful predictive information but generally do not outperform strong volatility-history benchmarks. Crucially, combining stock-related news forecasts with a strong volatility-history benchmark lowers forecast losses for several specifications and increases realised utility, providing evidence consistent with forecast complementarity. Performance varies across news types, embedding representations, and volatility regimes. SHAP attributions associate forecast variation with economically interpretable firm-specific and macroeconomic news themes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
