Paper: arXiv 2512.23847

Authors: Zhenyu Gao, Wenxi Jiang, Yutong Yan

Abstract

We develop a statistical test to detect lookahead bias in economic forecasts generated by large language models (LLMs). Using state-of-the-art pre-training data detection techniques, we estimate the likelihood that a given prompt appeared in an LLM’s training corpus, a statistic we term Lookahead Propensity (LAP). We formally show that a positive correlation between LAP and forecast accuracy indicates the presence and magnitude of lookahead bias, and apply the test to two forecasting tasks: news headlines predicting stock returns and earnings call transcripts predicting capital expenditures. Our test provides a cost-efficient, diagnostic tool for assessing the validity and reliability of LLM-generated forecasts.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper develops a formal statistical test with econometric propositions but relies on established MIA metrics rather than novel, heavy derivations, placing it in the moderate math range. It demonstrates empirical rigor by applying the test to real financial datasets (news headlines and earnings calls) and validating with out-of-sample bootstrap analysis, though it lacks code or backtest-ready implementation details.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Test for Lookahead Bias<br>in LLM Economic Forecasts"] --> B{"Data & Input:<br>Financial Text Data"}
  B --> C["Process: Estimate<br>Lookahead Propensity LAP<br>via P(data in training corpus)"]
  C --> D{"Computation:<br>Correlation Analysis<br>LAP vs Forecast Accuracy"}
  D --> E{"Statistical Test:<br>Significant Positive Correlation?"}
  E -- Yes --> F["Outcome: Lookahead Bias<br>Detectable & Quantifiable"]
  E -- No --> G["Outcome: Bias Absent<br>or Insufficient Data"]

Cited References (56)

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  5. Dated data: Tracing knowledge cutoffs in large language models (2024)
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and 48 more
  1. Language models are few-shot learners (2020)
  2. Expected returns and large language models (2022)
  3. Generative AI for economic research: Use cases and implications for economists (2023)
  4. Experimental evidence on the productivity effects of generative artificial intelligence (2023)
  5. … and the cross-section of expected returns (2016)
  6. Leakage and the reproducibility crisis in ML-based science (2022) — link
  7. Extracting training data from large language models (2021)
  8. On the opportunities and risks of foundation models (2021) — link
  9. Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel (1975)
  10. How to talk when a machine is listening: Corporate disclosure in the age of AI (2023)
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  12. Intellectual property protection lost and competition: An examination using large language models (2022)
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  14. ChatGPT and Corporate Policies — → 2409.17933
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  16. Harnessing Generative AI for Economic Insights — → 2410.03897
  17. Lookahead bias in pretrained language models (2024)
  18. Detecting pretraining data from large language models (2024)
  19. Entity neutering (2025)
  20. Chronologically Consistent Large Language Models — → 2502.21206
  21. Structured Beliefs and Fund Performance: An LLM -Based Approach (2024)
  22. Instruction Tuning Chronologically Consistent Language Models (2025) — link
  23. DatedGPT : Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining (2026) — link
  24. The Memorization Problem: Can We Trust LLMs’ Economic Forecasts? — → 2504.14765
  25. Why language models hallucinate (2025) — link
  26. Impact of pretraining term frequencies on few-shot reasoning (2022)
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  39. Large language models: An applied econometric framework (2025)
  40. Llama 2: Open foundation and fine-tuned chat models (2023) — link
  41. Efficient Memory Management for Large Language Model Serving with PagedAttention (2023)
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  48. CFOs Meet LLMs (2026)

9 of 56 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.