Morning Edition · Wednesday, July 15, 2026Published at 1:32 AM EDT · New York
New Work Trains 'Point-in-Time' Language Models to Strip Out Lookahead Bias
The method restricts training data by date so a model cannot embed information from the future, aiming to make financial backtests and causal studies valid.
A paper on scaling point-in-time language models tackles a problem that quietly corrupts a lot of empirical work in finance and the social sciences. Large language models (LLMs) trained on unrestricted internet corpora inevitably absorb inf…
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Part of a tracked trend
Temporal Leakage and ML Backtest Integrity
As LLMs enter quantitative finance and empirical social science, controlling for future-information leakage becomes a recurring requirement for defensible results, favoring providers of auditable, date-bounded models.
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