Detecting Lookahead Bias in LLM Forecasts

Abstract:We develop a statistical procedure to detect lookahead bias in economic forecasts generated by large language models (LLMs). Using a date-only recall query for a firm-date pair, we estimate the probability that the LLM has internalized information about the realized outcome, a statistic we term Lookahead Propensity (LAP). LAP is materially positive throughout the in-sample period and collapses essentially to zero right after the training-data cutoff. We show that a positive interaction between LAP and the LLM forecast in an accuracy regression indicates lookahead-bias contamination, and apply the test to two forecasting tasks: news headlines predicting stock returns and earnings call transcripts predicting capital expenditures. In both applications, the LLM forecast's predictive power is amplified on high-LAP firm-date pairs, and the interaction loses significance on post-training-cutoff samples. Our test provides a cost-efficient, diagnostic tool for assessing the validity and reliability of LLM-generated forecasts.

General Finance (q-fin.GN); Machine Learning (cs.LG); Trading and Market Microstructure (q-fin.TR)

arXiv:2512.23847 [q-fin.GN]

(or arXiv:2512.23847v2 [q-fin.GN] for this version)

https://doi.org/10.48550/arXiv.2512.23847 Focus to learn more arXiv-issued DOI via DataCite

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