FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning
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摘要
FinSMART introduces a market-aligned reinforcement learning framework for financial sentiment analysis that directly optimizes sentiment signals using realized market outcomes, enabling continual adaptation through market-aware retraining [page::0][page::1]. The approach uses a dual-filter reward with asymmetric incentives and a GRPO optimization to align sentiment with profitable market activity, achieving substantial improvements in profitability, risk-adjusted performance, and signal quality compared with state-of-the-art methods (e.g., FinDPO), including a 220% increase in cumulative trading returns over the strongest baseline and improved RankIC [page::0][page::1][page::2]. The framework further demonstrates the practicality of market-aware retraining—replacing costly human labels with observed articles and outcomes to sustain performance gains under evolving market dynamics [page::6][page::7].
