Alpha-R1: LLM-Based Alpha Screening for Investment Strategies
Analysis
This paper addresses the challenge of alpha decay and regime shifts in data-driven investment strategies. It proposes Alpha-R1, an 8B-parameter reasoning model that leverages LLMs to evaluate the relevance of investment factors based on economic reasoning and real-time news. This is significant because it moves beyond traditional time-series and machine learning approaches that struggle with non-stationary markets, offering a more context-aware and robust solution.
Key Takeaways
- •Proposes Alpha-R1, an LLM-based model for context-aware alpha screening.
- •Uses reinforcement learning to train the model.
- •Evaluates alpha relevance based on factor logic and real-time news.
- •Demonstrates improved robustness to alpha decay compared to benchmark strategies.
- •Addresses the limitations of traditional time-series and machine learning approaches in non-stationary markets.
Reference
“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.”