BondBERT: What we learn when assigning sentiment in the bond market

• URL: http://arxiv.org/abs/2511.01869v1
• Date: Tue, 21 Oct 2025 09:18:03 GMT
• Title: BondBERT: What we learn when assigning sentiment in the bond market
• Authors: Toby Barter, Zheng Gao, Eva Christodoulaki, Jing Chen, John Cartlidge,
• Abstract summary: Bond markets respond differently to macroeconomic news compared to equity markets.<n>Most sentiment models, including FinBERT, are trained primarily on general financial or equity news data.<n>We introduce BondBERT, a transformer-based language model fine-tuned on bond-specific news.
• Score: 5.914817449895728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bond markets respond differently to macroeconomic news compared to equity markets, yet most sentiment models, including FinBERT, are trained primarily on general financial or equity news data. This mismatch is important because bond prices often move in the opposite direction to economic optimism, making general or equity-based sentiment tools potentially misleading. In this paper, we introduce BondBERT, a transformer-based language model fine-tuned on bond-specific news. BondBERT can act as the perception and reasoning component of a financial decision-support agent, providing sentiment signals that integrate with forecasting models. It is a generalisable framework for adapting transformers to low-volatility, domain-inverse sentiment tasks by compiling and cleaning 30,000 UK bond market articles (2018--2025) for training, validation, and testing. We compare BondBERT's sentiment predictions against FinBERT, FinGPT, and Instruct-FinGPT using event-based correlation, up/down accuracy analyses, and LSTM forecasting across ten UK sovereign bonds. We find that BondBERT consistently produces positive correlations with bond returns, achieves higher alignment and forecasting accuracy than the three baseline models, with lower normalised RMSE and higher information coefficient. These results demonstrate that domain-specific sentiment adaptation better captures fixed income dynamics, bridging a gap between NLP advances and bond market analytics.

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