Look-Ahead-Bench

A standardized benchmark for detecting look-ahead bias in Large Language Models for finance

---

Overview

Look-Ahead-Bench is a standardized benchmark designed to measure look-ahead bias in Large Language Models (LLMs) used for financial decision-making.

Instead of evaluating isolated question-answering or factual recall, the benchmark evaluates end-to-end trading behavior using realistic agentic trading workflows. It measures how model performance degrades when moving from potentially memorized historical periods to genuinely unseen market regimes, allowing practitioners to distinguish true predictive reasoning from memorization-driven performance.

---

Why Look-Ahead Bias Matters

Financial LLMs are often trained on web-scale data containing post-hoc market analyses and explicit descriptions of historical asset performance. This can lead to training data leakage, where models appear predictive in backtests but fail catastrophically once deployed beyond their knowledge cutoff.

Look-Ahead-Bench provides a practical way to detect this issue before real capital is at risk.

---

Benchmark at a Glance

• Evaluation style: End-to-end agentic trading
• Framework: AI Hedge Fund (multi-agent LLM system)
• Metric: Alpha and Alpha Decay
• Assets: AAPL, MSFT, GOOGL, NVDA, TSLA
• Periods:
  • P1 (In-sample): Apr 2021 – Sep 2021
  • P2 (Out-of-sample): Jul 2024 – Dec 2024

---

Models Evaluated

• Standard LLMs: Llama 3.1 (8B, 70B), DeepSeek 3.2
• Point-in-Time LLMs: Pitinf-Small, Pitinf-Medium, Pitinf-Large

---

Key Findings (Summary)

• Standard foundation models exhibit severe alpha decay when evaluated out-of-sample.
• Larger standard models often generalize worse due to stronger memorized priors.
• Point-in-Time (PiT) models show stable or improving performance as they scale.
• Demonstrates a clear scaling paradox in the presence of look-ahead bias.

---

The benchmark runs fully in simulation and does not require live trading or brokerage APIs.

---

How to Contribute

Look-Ahead-Bench is intended to evolve into a community-standard diagnostic for temporal bias in financial LLMs. Contributions are strongly encouraged and can take many forms, ranging from incremental improvements to major extensions of the benchmark.

1. Expand the Asset Universe

The current benchmark focuses on five large-cap U.S. technology stocks, which are especially prone to memorization. Valuable contributions include:

• Adding small-cap stocks, which are less represented in training corpora
• Expanding to non-tech sectors (e.g., Healthcare, Industrials, Energy)
• Supporting international equities
• Extending to other asset classes (ETFs, commodities, FX, crypto)

These extensions help test whether observed behavior generalizes beyond highly visible assets.

---

2. Add New Trading Agents or Frameworks

Look-Ahead-Bench currently relies on the AI Hedge Fund framework. The benchmark would benefit greatly from the integration of additional agentic systems, including (but not limited to):

• FinGPT
• FinMem / FinCon
• FinRL-DeepSeek
• TradingAgents
• Hedge-Agents
• FinAgent, FinRobot, StockAgent
• QuantAgent and factor-based LLM systems

Each added agent should follow the same point-in-time constraints and evaluation protocol to ensure fair comparison.

---

3. Extend Model Coverage

You are encouraged to evaluate:

• New open-source foundation models
• Proprietary or commercial LLMs (when legally permissible)
• Alternative Point-in-Time or temporally aware models
• Models with explicit decontamination or logit-adjustment techniques

When adding a model, please clearly document:

• Training cutoff assumptions
• Prompting strategy
• Inference parameters

---

4. Improve Bias Diagnostics and Metrics

While alpha decay is the core metric, the benchmark is intentionally extensible. Possible contributions include:

• Alternative decay or stability metrics
• Statistical tests for memorization or leakage
• Confidence-weighted or risk-adjusted performance measures
• Cross-period or rolling-window evaluations

The goal is to strengthen the benchmark’s ability to separate reasoning from recall.

---

5. Expand Backtesting Methodology

More advanced validation techniques are welcome, including:

• Multiple non-overlapping evaluation periods
• Synthetic or counterfactual market scenarios
• Stress tests across different market regimes
• Anti-overfitting diagnostics (e.g., RAS-style methods)

---

Contribution Guidelines

• Open an issue to discuss major changes before implementation

• Submit a pull request with:

  • Clear motivation
  • Reproducible experiments
  • Documentation of assumptions

All contributions should aim to improve realism, robustness, and bias detection.

---