融合市场时序数据与大模型，提升美联储政策立场分类准确率
LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification
- 用罗伯特、提示大模型和时序变换器三路并行，投票融合判断政策立场
- 仅用240条人工标注数据就超越零样本大模型，加权F1达70.2%
- 适合对金融文本分类和宏观政策分析感兴趣的读者
金融文本在市场环境中生成与解读，但现有分类器通常只处理文本本身。本文研究市场时序数据是否可作为辅助输入，用于判断美联储沟通文本的鹰派、鸽派或中性立场。提出 lfts{} 系统，在 f{} 架构基础上引入新模态：一个小型投票网络融合三个独立训练的组件——微调后的 RoBERTa 编码器、提示式大语言模型（LLM）以及基于前月市场序列的时序变换器集成。由于仅有约一千条人工标注句子，先用 LLM 自动标注语料预训练 RoBERTa，再进行微调。模型在截至2015年的FOMC文本上训练，评估期为2015–2022年，加权F1达70.2%，显著优于零样本LLM的64.1%。且在仅使用240条人工标注数据时即超越基准，初步证明市场时序数据在金融文本分类中的有效性。
原文摘要 · Abstract (English)
Financial text is produced and interpreted within a market environment, yet financial text classifiers almost always receive text alone. We study whether financial time series are useful as an additional input on the task of classifying sentences from Federal Reserve communication as hawkish, dovish, or neutral. Our system, \lfts{}, extends the \lf{} architecture with this modality: a small voting network combines three independently trained components, a fine-tuned RoBERTa encoder, a prompted large language model (LLM), and a fused ensemble of time-series transformers over the market series of the months preceding publication. Because only about a thousand annotated sentences are available for training, the RoBERTa encoder is first pre-trained on sentences annotated automatically by the LLM and only then fine-tuned on the human labels. Trained on Federal Open Market Committee (FOMC) communication up to 2015 and evaluated on 2015--2022, the fused system achieves 70.2\% weighted F1 -- against 64.1\% for the zero-shot LLM -- and overtakes it with as few as 240 human-labelled sentences. We take this as initial evidence for market time series as an input modality in financial text classification.
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