Autonomous Market Intelligence: Agentic AI Nowcasting Predicts Stock Returns

Battle Count: 73

Directly addresses stock return prediction using AI-generated signals for implementable trading strategies. Documents daily alpha of 18.4 bps with Sharpe ratio of 2.43 on liquid Russell 1000 stocks. Provides detailed portfolio construction methodology, transaction cost analysis, factor exposure decomposition, and cross-sectional predictability patterns. The agentic framework represents a novel information-processing paradigm for quantitative trading that operates at daily frequency in an environment where such predictability is considered exceptionally rare.

The core methodology uses a web interface (not API) with enabled search, requiring daily manual or semi-automated querying of ~1000 stocks. Data extraction uses regex parsing of model outputs. Portfolio construction is straightforward (top-N ranking, value-weighted). However, the agentic nature means the AI's information retrieval is non-deterministic and cannot be precisely replicated. The web interface dependency (vs. API) adds operational complexity. No model training or fine-tuning is required - it uses a pre-trained model as-is.

The dataset is explicitly designed to be irreproducible. The nowcasting paradigm captures a unique temporal moment in AI-market co-evolution. Once the information environment passes, it can never be recreated. Future researchers cannot ask today's AI models to make predictions about today's stocks with today's information. Any later re-run uses a model whose parameters embed post-period data, carrying latent knowledge about subsequent outcomes. The retrieval environment is itself time-specific and cannot be reconstructed faithfully at scale. However, the methodology, prompt design, and GitHub repository are documented.