Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
A new paper proposes a method to mitigate look-ahead bias in financial backtesting using large language models. The authors introduce FinCAD, which adapts Context-Aware Decoding to suppress an LLM's memory of historical outcomes. This approach aims to improve the reliability of backtesting results without the need for retraining the models.
- ▪Backtesting large language models on historical financial data is often unreliable due to look-ahead bias.
- ▪FinCAD is an inference-time adaptation that suppresses an LLM's memory of past outcomes.
- ▪The method significantly improves the correlation between in-sample and out-of-sample performance.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24564 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Computer Science > Artificial Intelligence arXiv:2605.24564 (cs) [Submitted on 23 May 2026] Title:Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models Authors:Weixian Waylon Li, Mengyu Wang, Tiejun Ma View a PDF of the paper titled Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models, by Weixian Waylon Li and 2 other authors View PDF HTML (experimental) 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.
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