FinSMART：基于市场对齐强化学习的算法交易金融情感分析

中文摘要 AI 辅助

生成式AI的最新进展通过后训练的金融大语言模型（LLMs）大幅提升了金融情感分析的效果。然而，现有方法仍局限于与市场无关的监督学习范式，依赖有限、静态且人工标注的数据集，因此无法适应不断变化的市场条件。为解决这一局限，我们提出FinSMART，首个用于金融情感分析的市场对齐强化学习框架，其利用已实现的市场结果直接优化情感信号。针对金融市场嘈杂、非平稳且多因素的特性，FinSMART整合了信号提取流程，结合市场感知数据过滤与离散非对称交易奖励，实现从具有经济意义的市场反馈中进行稳定的强化学习。实验结果表明，FinSMART在盈利能力、风险调整后表现及情感信号质量上显著优于现有最先进方法，较最强基线提升了220%的累计交易回报。此外，FinSMART框架可随时通过新观测到的金融文章及其已实现的市场结果替代昂贵的人工标注，自然支持市场感知的再训练。该再训练策略使模型能够持续适应不断变化的市场动态，较静态模型实现持续的性能提升。这些发现证明了市场对齐强化学习的实际适用性，并凸显其作为开发自适应金融LLMs的下一代范式的潜力。

英文摘要

Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). However, existing approaches remain confined to a market-agnostic, supervised learning paradigm that relies on limited, static and human-annotated datasets, and thus are incapable of adapting to evolving market conditions. To address this limitation, we introduce FinSMART, the first market-aligned reinforcement learning framework for financial sentiment analysis, which directly optimizes sentiment signals using realized market outcomes. To deal with the noisy, non-stationary, and multifactorial nature of financial markets, FinSMART incorporates a signal extraction pipeline that combines market-aware data filtering with a discrete asymmetric trading reward, enabling stable reinforcement learning from economically meaningful market feedback. Experimental results demonstrate that FinSMART significantly outperforms existing state-of-the-art methods in profitability, risk-adjusted performance, and sentiment signal quality, improving cumulative trading returns by 220% over the strongest baseline. Uniquely, the FinSMART framework naturally supports market-aware retraining, at any point in time, by replacing costly manual annotation with newly observed financial articles and their realized market outcomes. Such a retraining strategy enables the model to continuously adapt to changing market dynamics, resulting in consistent performance gains over its static counterpart. These findings demonstrate the practical applicability of market-aligned reinforcement learning and highlight its potential as a next-generation paradigm for developing adaptive financial LLMs.

发表机构

• Imperial College London（帝国理工学院）

机构由 AI 辅助整理，请以论文原文为准。