Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
Researchers have developed FinCAD, a method to mitigate "parametric look-ahead bias" in large language models used for financial backtesting. This bias occurs because LLMs are pre-trained on data that includes future outcomes, making their historical backtests unreliable. FinCAD adapts LLM decoding at inference time to suppress memory of past events without retraining, significantly reducing in-sample backtest returns while preserving out-of-sample performance and rankings. AI
IMPACT Addresses a critical flaw in using LLMs for financial forecasting, potentially improving the reliability of AI-driven investment strategies.