Evaluating LLMs in Finance Requires Explicit Bias Consideration

Battle Count: 75

Highly relevant to quantitative trading as it directly addresses the validity of backtesting, signal generation, and deployment claims for LLM-based trading systems. The five biases (look-ahead, survivorship, narrative, objective, cost) are fundamental concerns in quantitative strategy evaluation. The framework's emphasis on net utility under realistic costs, latency-aware simulation, and point-in-time data access directly impacts strategy development and validation. The paper warns that current LLM-for-finance papers may produce inflated results that cannot support deployment claims, which is critical for practitioners evaluating LLM-based trading tools.

The framework itself is conceptually straightforward as a checklist with binary pass/fail criteria. However, implementing the requirements (point-in-time data access, dynamic universe construction, latency-aware simulation, epistemic calibration) requires significant infrastructure investment. The paper acknowledges that the required data (point-in-time fundamentals, delisting histories, archived disclosures) is costly and often proprietary. The checklist template is designed to be easily fillable, but the underlying data and system requirements are substantial.

The paper provides a GitHub repository with an interactive dashboard, checklist template, and literature review. The framework is presented as a structured checklist with clear pass/fail criteria. However, as a position paper, it does not provide empirical experiments with specific models. The user study methodology is described but raw data access is limited. The checklist template is available for adoption.