Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
arXiv:2605.24564v1 Announce Type: cross Abstract: Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trained in 2024 already "knows" which way 2018-2020 stocks moved. We name this failure parametric look-ahead bias and propose FinCAD, an inference-time adaptation of Context-Aware Decoding that suppresses an LLM's memory of historical outcomes without retraining. FinCAD pairs an adversarial bias-discovery pipeline that learns a model-specific memory-activating prior prompt with an entity- and date-a
The increasing integration of large language models into sophisticated financial applications necessitates robust methods to address inherent biases, as LLMs' pre-training data contains future knowledge relative to backtesting scenarios.
This development is crucial for ensuring the reliability and validity of AI-driven financial models, preventing erroneous strategies based on information not available at the time of historical events.
The proposed FinCAD methodology provides a concrete, technical solution to mitigate 'parametric look-ahead bias' in LLM-based financial backtesting, making these models more trustworthy and practical for financial analysis.
- · Quantitative finance firms
- · AI developers
- · Financial data science
- · Algorithmic trading
- · Uncritical adopters of LLMs in finance
- · Flawed backtesting methodologies
Financial institutions can now develop and deploy LLM-based trading and analysis tools with higher confidence and reduced risk of bias.
This validation could accelerate the adoption of advanced AI models across the financial sector, leading to more sophisticated and potentially efficient markets.
Improved AI-driven financial analysis may lead to new forms of market alpha or exacerbate existing informational advantages, depending on access and implementation.
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Read at arXiv cs.LG