LLMs Don't Know What They Know: Benchmarking Temporal Self-Knowledge for Financial Backtesting

Lingfei Qian, Yan Wang, Xueqing Peng, Yupeng Cao, Yuechen Jiang, Zhuohan Xie, Anke Xu, Wenbo Cao, Jiarui Chen, Zehan Li, Jimin Huang, Alejandro Lopez-Lira, Yuehua Tang, Sophia Ananiadou, Jian-Yun Nie

LLMs Don't Know What They Know: Benchmarking Temporal Self-Knowledge for Financial Backtesting: 34 upvotes on Hugging Face Daily Papers, #7 of 34 papers on 2026-11-09. Day-by-day upvote history.

Upvotes over time

It had 21 upvotes by its first day, 33 upvotes by the next day, 34 upvotes by day 3, 34 upvotes by day 7, and has 34 now. It ranked #20 of 133 papers in its week and #59 of 340 in its month.

Financial backtests of large language models (LLMs) are meaningful only if the model did not already know the outcomes it is asked to predict. Practitioners guard against this by trusting a vendor's stated knowledge cutoff, by asking the model what it knows, or by instructing it to answer as of a past date. To verify whether any of these safeguards can be trusted, we introduce FinDendro, a benchmark of 2,226 dated U.S. facts from 1999 to 2026, covering monthly macroeconomic series, month-end stock prices and Federal Reserve decisions. Each fact yields four prompts: a value question, Recall and Predict questions that pair each candidate value with a claim of knowing or guessing without a declared date, and a Predict question with a historical declared date. Controls for persistence and dominant outcomes keep chance accuracy near 0.27. Results show that for most frontier models, self-reports understate knowledge. GPT-5 answers 76% of controlled facts correctly but claims to know 38%, and answers 61% of the facts it says it does not know. Knowledge claims can persist beyond the measured knowledge boundary, by up to seven quarters, and a declared date removes most of the claims of prior knowledge while leaving accuracy unchanged. Backtests inside a model's training window therefore cannot be certified leak-free by self-report, instruction or stated cutoff. These findings motivate behavioral measurement of temporal knowledge boundaries, and FinDendro provides a benchmark and procedure for selecting backtest periods based on demonstrated factual knowledge.

Paper page on Hugging Face · arXiv

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