LabelFusion-TS：融合大语言模型、Transformer编码器与金融时间序列以进行货币政策立场分类

中文摘要 AI 辅助

金融文本的生成与解读均处于市场环境中，但金融文本分类器几乎仅接收文本作为输入。本研究探讨金融时间序列是否可作为额外输入，用于将美联储沟通语句分类为鹰派、鸽派或中性。我们的系统LabelFusion-TS（Lfts）在LabelFusion（Lf）架构基础上新增该模态：小型投票网络整合三个独立训练的组件——微调后的RoBERTa编码器、经提示的大语言模型（LLM），以及基于发布前数月市场序列的时间序列Transformer融合集成模型。由于训练仅约有一千条标注语句，RoBERTa编码器先在LLM自动标注的语句上预训练，再在人工标签上微调。该系统以2015年前的联邦公开市场委员会（FOMC）沟通内容训练，在2015-2022年数据上评估，融合系统的加权F1值达70.2%，而零样本LLM仅为64.1%；且仅需240条人工标注语句即可实现超越。我们认为这为市场时间序列作为金融文本分类的输入模态提供了初步证据。

英文摘要

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.

发表机构

• Georg-August-Universität Göttingen（哥廷根大学）
• Hochschule Bielefeld (HSBI) - University of Applied Sciences and Arts（比勒费尔德应用科学与艺术大学）

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