用新闻题材和情绪预测市场通胀预期
Predicting market inflation expectations with news topics and sentiment
摘要：
这项研究提出了一种新的方法，将新闻主题及其相关的情绪纳入八个国家的盈亏平衡通胀率（BEIR）走势的预测与成熟的债券市场。我们校准了五类机器学习模型，包括每个国家的基于叙述的功能，并发现它们通常优于不包括此类功能的相应基准。我们发现Logistic Regression和XGBoost分类器可以在不同国家/地区提供最佳性能。我们用特征重要性分析补充了这些结果，表明经济和金融主题是我们预测中的关键性能驱动因素，与健康和政府相关的主题也有贡献。我们通过Granger因果关系检验了新闻叙事对BEIR的跨国溢出效应，并证实了美国和德国存在这种溢出效应，而我们研究中考虑的其他五个国家只受到当地叙事的影响。
英文摘要：
This study presents a novel approach to incorporating news topics and their associated sentiment into predictions of breakeven inflation rate (BEIR) movements for eight countries with mature bond markets. We calibrate five classes of machine learning models including narrative-based features for each country, and find that they generally outperform corresponding benchmarks that do not include such features. We find Logistic Regression and XGBoost classifiers to deliver the best performance across countries. We complement these results with a feature importance analysis, showing that economic and financial topics are the key performance drivers in our predictions, with additional contributions from topics related to health and government. We examine cross-country spillover effects of news narrative on BEIR via Graphical Granger Causality and confirm their existence for the US and Germany, while five other countries considered in our study are only influenced by local narrative.
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。
