Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
Backtesting large language models (LLMs) on historical financial data is unreliable when their pre-training data include the evaluated events. An LLM trained in 2024 may already encode how stocks moved during 2018-2020. We name this failure parametric look-ahead bias and propose FinCAD, an inference-time adaptation of Context-Aware Decoding that attenuates contributions from memorised 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-adaptive rule that scales the CAD strength using a per-(entity, date) confidence signal. Across five 7-14B LLMs and five mega-cap eq...
| Authors | Weixian Waylon Li, Mengyu Wang, Tiejun Ma |
|---|---|
| Primary category | cs.AI |
| Published | 23 May 2026 |
| Last updated at source | 29 August 2026 |
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