Title: FinSentLLM: Multi-LLM and Structured Semantic Signals for Enhanced Financial Sentiment Forecasting Show Chinese title

Title: FinSentLLM：用于增强金融情感预测的多LLM和结构化语义信号

Abstract: Financial sentiment analysis (FSA) has attracted significant attention, and recent studies increasingly explore large language models (LLMs) for this field. Yet most work evaluates only classification metrics, leaving unclear whether sentiment signals align with market behavior. We propose FinSentLLM, a lightweight multi-LLM framework that integrates an expert panel of sentiment forecasting LLMs, and structured semantic financial signals via a compact meta-classifier. This design captures expert complementarity, semantic reasoning signal, and agreement/divergence patterns without costly retraining, yielding consistent 3-6% gains over strong baselines in accuracy and F1-score on the Financial PhraseBank dataset. In addition, we also provide econometric evidence that financial sentiment and stock markets exhibit statistically significant long-run comovement, applying Dynamic Conditional Correlation GARCH (DCC-GARCH) and the Johansen cointegration test to daily sentiment scores computed from the FNSPID dataset and major stock indices. Together, these results demonstrate that FinSentLLM delivers superior forecasting accuracy for financial sentiment and further establish that sentiment signals are robustly linked to long-run equity market dynamics. Show Chinese abstract

Abstract: 金融情感分析（FSA）已引起广泛关注，最近的研究越来越多地探索大型语言模型（LLMs）在这一领域的应用。 然而，大多数工作仅评估分类指标，未能明确情感信号是否与市场行为一致。 我们提出了FinSentLLM，一个轻量级多LLM框架，整合了一个专家小组的情感预测LLMs，以及通过紧凑的元分类器结构化的语义金融信号。 这种设计在不进行昂贵重新训练的情况下，捕捉了专家互补性、语义推理信号以及一致性/分歧模式，在Financial PhraseBank数据集上的准确率和F1分数上比强基线模型取得了3-6%的一致提升。 此外，我们还提供了实证证据，表明金融情感和股票市场表现出统计上显著的长期共同运动，将动态条件相关GARCH（DCC-GARCH）和Johansen协整检验应用于从FNSPID数据集和主要股票指数计算出的每日情感得分。 综上所述，这些结果表明FinSentLLM在金融情感预测方面具有优越的准确性，并进一步确立了情感信号与长期股权市场动态之间有坚实的联系。