Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 26·5 min read
Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
TL;DR
Backtesting large language models (LLMs) on historical financial data suffers from look-ahead bias, where models inadvertently use future information. To address this, FinCAD was developed, which adapts Context-Aware Decoding to suppress memory of historical outcomes during inference without retraining.
✦ Why It Matters
Engineers can apply FinCAD to improve the reliability of financial models by reducing look-ahead bias in backtesting.
Key Takeaways
- Implement FinCAD to mitigate look-ahead bias in financial models.
- Use Context-Aware Decoding for better inference in LLMs.
- Monitor in-sample and out-of-sample performance metrics closely.
- Leverage adversarial bias-discovery techniques for model improvement.
Full Summary
Backtesting financial models using historical data can lead to unreliable results due to look-ahead bias, where models trained on past data inadvertently incorporate future outcomes. To mitigate this issue, FinCAD was created as an inference-time adaptation of Context-Aware Decoding, which suppresses an LLM's memory of historical outcomes without requiring retraining.
FinCAD employs an adversarial bias-discovery pipeline to learn a model-specific memory-activating prior prompt, combined with an entity- and date-adaptive rule that adjusts the suppression strength based on memorized dates. Testing across five large language models and five major equities, FinCAD achieved a reduction in in-sample returns by up to -67.1% on memorized dates while keeping out-of-sample returns within $8K and maintaining a Sharpe ratio close to the baseline.
Additionally, it improved the Spearman correlation between in-sample and out-of-sample performance from +0.779 to +0.846, indicating better predictive accuracy. These findings suggest that FinCAD can enhance the reliability of financial backtesting using LLMs.