Generative AI for Stock Selection

Battle Count: 90

Highly relevant to quantitative trading. The paper directly addresses the feature engineering bottleneck in systematic equity strategies, provides implementable results with realistic execution lags (T+1 to T+2), includes transaction cost analysis, portfolio construction methodology, and demonstrates Sharpe ratios of 1.14-1.63 in ensemble configurations. The work bridges LLM capabilities with practical quantitative finance constraints including point-in-time data, look-ahead bias avoidance, and dollar-neutral portfolio construction. Results span multiple market regimes and asset classes (analyst estimates, options, price-volume).

Moderate-to-high complexity. Requires: (1) LLM API access (GPT-4.1) with RAG infrastructure and curated knowledge base, (2) DSPy framework for programmatic prompting, (3) LightGBM for tabular ML, (4) proprietary financial data from multiple vendors, (5) sophisticated backtesting infrastructure with point-in-time data handling, (6) portfolio construction with dollar-neutral constraints, (7) transaction cost modeling, (8) feature validation pipeline with schema compliance, sparsity checks, and similarity suppression. The feature generation loop with iterative discovery and feedback is non-trivial to implement correctly.

The paper provides detailed methodology, feature code examples, and evaluation protocols. However, it relies on proprietary datasets (TrueBeats from ExtractAlpha, SpiderRock options data, EDI price-volume data) and a specific vendor identifier system (CWIQ Code). The knowledge base documentation is not publicly available. While the framework description is thorough, exact reproduction requires access to commercial data vendors and the specific GPT-4.1 API. No GitHub repository is mentioned. The paper acknowledges AI writing assistants were used for manuscript preparation.