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

ArXiv ID: 2511.01869 “View on arXiv”

Authors: Toby Barter, Zheng Gao, Eva Christodoulaki, Jing Chen, John Cartlidge

Abstract

Bond markets respond differently to macroeconomic news compared to equity markets, yet most sentiment models are trained primarily on general financial or equity news data. However, bond prices often move in the opposite direction to economic optimism, making general or equity-based sentiment tools potentially misleading. 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. We propose 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). BondBERT’s sentiment predictions are compared 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, and achieves higher alignment and forecasting accuracy than the three baseline models. These results demonstrate that domain-specific sentiment adaptation better captures fixed income dynamics, bridging a gap between NLP advances and bond market analytics.

Keywords: Bond Sentiment, Domain Adaptation, Transformer Models (BondBERT), Fixed Income Analytics, Macro News, Fixed Income

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies established transformer architectures (FinBERT) to a new domain (bond market sentiment) with a clear dataset and benchmarking against multiple baselines, demonstrating strong empirical rigor with specific metrics (correlation, accuracy, LSTM forecasting). Mathematical complexity is moderate, focusing on model application and statistical evaluation rather than novel theoretical derivations.

  flowchart TD
    A["Research Goal: Develop Bond-Specific Sentiment Model"] --> B["Data Collection & Preprocessing<br/>30k UK Bond Articles 2018-2025"]
    B --> C["Model Development<br/>Fine-tune BondBERT & Baselines"]
    C --> D["Computational Analysis<br/>Event Correlation + LSTM Forecasting"]
    D --> E["Outcome 1: BondBERT Correlates Positively with Returns"]
    D --> F["Outcome 2: Outperforms FinBERT & FinGPT"]
    D --> G["Outcome 3: Validates Domain-Adapted Sentiment"]
    E & F & G --> H["Result: Gap Bridged between NLP & Fixed Income Analytics"]