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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
- Use FinCAD to mitigate look-ahead bias in financial models.
- Implement memory suppression techniques without retraining LLMs.
- Evaluate model performance using Spearman correlation for better predictions.
- Test across various entities to optimize backtesting results.
How It Works
FinCAD modifies the inference process of LLMs by suppressing their memory of historical outcomes. It uses an adversarial bias-discovery pipeline to identify and activate specific memory prompts, which are then adjusted based on the entity and date being analyzed.
This allows the model to focus on relevant data while avoiding the pitfalls of look-ahead bias.