Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning

ACL ARR 2026 January Submission2237 Authors

Keywords: Large Language Models, Reinforcement Learning, Alpha Screening, Quantitative Trading

Abstract: Signal decay and regime shifts pose recurring challenges for data-driven investment strategies in non-stationary markets. Conventional time-series and machine learning approaches, which rely primarily on historical correlations, often struggle to generalize when the economic environment changes. While large language models (LLMs) offer strong capabilities for processing unstructured information, their potential to support quantitative factor screening through explicit economic reasoning remains underexplored. Existing factor-based methods typically reduce alphas to numerical time series, overlooking the semantic rationale that determines when a factor is economically relevant. We propose Alpha-R1, an 8B-parameter reasoning model trained via reinforcement learning for context-aware alpha screening. Alpha-R1 reasons over factor logic and real-time news to evaluate alpha relevance under changing market conditions, selectively activating or deactivating factors based on contextual consistency. Empirical results across multiple asset pools from markets across different countries show that Alpha-R1 consistently outperforms benchmark strategies and exhibits improved robustness to alpha decay.

Paper Type: Long

Research Area: Financial Applications and Time Series

Research Area Keywords: Reinforcement learning, LLM/AI agents, Financial/business NLP, Fine-tuning, Prompting, Transfer learning / domain adaptation, Applications

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data analysis

Languages Studied: English, Chinese

Submission Number: 2237