AI’s predictable memory in financial analysis

Look-ahead bias in Large Language Models (LLMs) arises when information that would not have been available at the time of prediction is included in the training data and inflates prediction performance. This paper proposes a practical methodology to quantify look-ahead bias in financial applications. By prompting LLMs to retrieve historical stock returns without context, we construct a proxy to estimate memorization-driven predictability. We show that the bias varies predictably with data frequency, model size, and aggregation level: smaller models and finer data granularity exhibit negligible bias. Our results help researchers navigate the trade-off between statistical power and bias in LLMs. • We identify look-ahead bias by querying the model to recall a specific information with no context. • Look ahead bias is more severe for lower frequencies, indexes, and large models. • When working with relatively small models at high frequencies look-ahead bias is insignificant.