FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

2026-07-30

AI Summary

This study addresses the limitations of existing financial sentiment analysis methods, which rely on static human annotations and struggle to adapt to dynamic market conditions. To overcome this, the authors propose the first market-aligned reinforcement learning framework that replaces manual labels with real market feedback, enabling an end-to-end optimization pipeline for financial large language models. The approach incorporates market-aware data filtering, a discrete asymmetric trading reward mechanism, and dynamic retraining at arbitrary time points to adaptively refine sentiment signals. Empirical results demonstrate that the proposed method significantly outperforms state-of-the-art baselines in terms of profitability, risk-adjusted returns, and sentiment signal quality, achieving a 220% improvement in cumulative trading returns over the strongest baseline.

Abstract

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.

Problem

• financial sentiment analysis
• market-agnostic
• supervised learning
• evolving market conditions
• static datasets

Innovation

• market-aligned reinforcement learning
• financial sentiment analysis
• adaptive financial LLMs
• trading reward design
• market-aware retraining