为债券市场定制的语义模型，能更准确捕捉利率变动信号。
BondBERT: What we learn when assigning sentiment in the bond market
- 用3万篇英国债券新闻微调Transformer模型，适配低波动反向情绪任务
- 在10只英国国债上预测准确率超基线模型，与债市回报正相关性更强
- 适合量化交易、固定收益分析等需精准情绪信号的场景
债券市场对宏观经济消息的反应与股票市场不同，但多数情感模型主要基于通用金融或股票新闻数据训练。然而，债券价格常与经济乐观情绪呈反向变动，通用或股票类情感工具可能产生误导。本文提出BondBERT，一个针对债券新闻微调的Transformer语言模型，可作为金融决策支持系统的感知与推理组件，提供与预测模型融合的情感信号。通过整理清洗2018至2025年间3万篇英国债券市场文章，构建了适用于低波动、领域反向情感任务的可泛化框架。在十只英国主权债券上，以事件关联性、涨跌准确率及LSTM预测性能对比FinBERT、FinGPT和Instruct-FinGPT，结果显示BondBERT始终与债券回报呈现正相关，且在对齐度与预测精度上优于三个基线模型。结果表明，领域特定的情感适配更能捕捉固收市场的动态，弥合NLP进展与债券市场分析之间的差距。
原文摘要 · Abstract (English)
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.
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