Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance

ArXiv ID: 2601.13770 “View on arXiv”

Authors: Mostapha Benhenda

Abstract

We introduce Look-Ahead-Bench, a standardized benchmark measuring look-ahead bias in Point-in-Time (PiT) Large Language Models (LLMs) within realistic and practical financial workflows. Unlike most existing approaches that primarily test inner lookahead knowledge via Q\&A, our benchmark evaluates model behavior in practical scenarios. To distinguish genuine predictive capability from memorization-based performance, we analyze performance decay across temporally distinct market regimes, incorporating several quantitative baselines to establish performance thresholds. We evaluate prominent open-source LLMs – Llama 3.1 (8B and 70B) and DeepSeek 3.2 – against a family of Point-in-Time LLMs (Pitinf-Small, Pitinf-Medium, and frontier-level model Pitinf-Large) from PiT-Inference. Results reveal significant lookahead bias in standard LLMs, as measured with alpha decay, unlike Pitinf models, which demonstrate improved generalization and reasoning abilities as they scale in size. This work establishes a foundation for the standardized evaluation of temporal bias in financial LLMs and provides a practical framework for identifying models suitable for real-world deployment. Code is available on GitHub: https://github.com/benstaf/lookaheadbench

Keywords: Point-in-Time (PiT) LLMs, Look-ahead bias, Alpha decay, Temporal generalization, Financial LLM evaluation, Financial Markets (General)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper focuses on benchmarking LLMs against look-ahead bias using practical trading workflows and standard metrics, with limited advanced mathematics but extensive empirical implementation, code, and backtesting.

  flowchart TD
    A["Research Goal: Evaluate look-ahead bias<br>in Point-in-Time (PiT) LLMs"] --> B["Data: Financial text datasets<br>spanning multiple market regimes"]
    B --> C["Methodology: Compare standard LLMs<br>vs. PiT LLMs on practical workflows"]
    C --> D["Computation: Calculate alpha decay<br>and performance thresholds"]
    D --> E["Outcome 1: Standard LLMs<br>show significant lookahead bias"]
    D --> F["Outcome 2: PiT LLMs improve<br>generalization as they scale"]
    E & F --> G["Conclusion: Foundation for<br>standardized temporal bias evaluation"]