Paper: arXiv 2601.13770

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

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.0/10 (capped from 8.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (35)

  1. Caution Ahead: Numerical Reasoning and Look‐Ahead Bias in AI Models (2026) — link
  2. DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining (2026) — link
  3. Detecting Lookahead Bias in LLM Forecasts — → 2512.23847
  4. A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs (2025) — link
  5. FinPos: A Position-Aware Trading Agent System for Real Financial Markets (2025) — link
  6. Fusing Narrative Semantics for Financial Volatility Forecasting — → 2510.20699
  7. QuantHarness: Price-Driven Multi-Agent LLMs for High-Frequency Trading (2025) — link
  8. Chain-of-Alpha: Unleashing the Power of Large Language Models for Alpha Mining in Quantitative Trading (2025) — link
and 27 more
  1. Language Model Guided Reinforcement Learning in Quantitative Trading — → 2508.02366
  2. The Memorization Problem: Can We Trust LLMs’Economic Forecasts? — → 2504.14765
  3. HedgeAgents: A Balanced-aware Multi-agent Financial Trading System — → 2502.13165
  4. MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series — → 2411.16585
  5. FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making (2024) — link
  6. FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models — → 2405.14767
  7. FinMem: A Performance-Enhanced LLM Trading Agent With Layered Memory and Character Design — → 2311.13743
  8. Assessing Look-Ahead Bias in Stock Return Predictions Generated by GPT Sentiment Analysis — → 2309.17322
  9. TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance — → 2309.03736
  10. Inverse Scaling: When Bigger Isn’t Better (2023) — link
  11. PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance (2023) — link
  12. Quantifying Memorization Across Neural Language Models (2022) — link
  13. Extracting Training Data from Large Language Models (2020) — link
  14. Lookahead Bias in Pretrained Language Models (2024) — link
  15. Profit Mirage: Revisiting Information Leak-age in LLM-based Financial Agents, 2025b
  16. : Large
  17. AI hedge fund: Open-source framework for LLM-based trading agents
  18. AutoTrader: An automated trading framework using LLMs
  19. Thematic Scoring: Quantifying Contextual Narratives using Language Models
  20. A Reflective LLM-based Agent to Guide Zero-shot
  21. A multi-modal foundation agent for financial trading
  22. : A GenAI Model for Stock
  23. Look-Ahead Bias in Large Language Models (LLMs): Implications and Applications in Finance
  24. When AI meets finance (Stock-Agent): Large language model-based stock trading in simulated real-world environments
  25. : LLM-infused risk-sensitive reinforcement learning for trading agents, 2025
  26. Alpha-GPT
  27. Multi-agents

10 of 35 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.