Detecting Lookahead Bias in LLM Forecasts

Battle Count: 137

Highly relevant for quantitative trading: the paper directly addresses the validity of LLM-generated signals used in trading strategies (news sentiment predicting returns, earnings calls predicting capex). The finding that 32% of the LLM headline signal's predictive power in-sample is attributable to memorization rather than reasoning has direct implications for backtesting and live trading. The diagnostic is portable and low-cost, making it practical for quant teams to validate their LLM-based alpha signals before deployment. The post-cutoff placebo methodology is directly applicable to ensuring out-of-sample validity of ML-based trading signals.

Relatively low implementation complexity: requires only (1) querying an LLM with a simple date-only recall prompt, (2) reading first-token log-probabilities at the answer position, (3) computing LAP = P(up) + P(down), and (4) running a standard OLS regression with interaction terms. The CLAUDE.md file provides an end-to-end automated procedure. Main technical requirements are access to a model with exposed logprobs and standard econometric software. No retraining, no access to proprietary training data needed.

High reproducibility: uses open-source Llama-3.3-70B with publicly available checkpoints on HuggingFace. The detection procedure is specified in a CLAUDE.md instruction file available for download. Uses vLLM for inference with temperature=0 for deterministic outputs. Data sources (CRSP, Compustat, Bloomberg News, Thomson Reuters StreetEvents) are standard academic databases. However, Bloomberg News data requires subscription access.