  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.
