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This paper proposes PALM, a low-rank adapter method to adapt financial language models to specific time points without full retraining, reducing look-ahead bias and computational costs in financial backtests.
This paper demonstrates that scaling point-in-time language models—trained exclusively on text available up to each calendar date—can substantially narrow the performance gap with unrestricted models, enabling valid backtests and causal inference in finance and social sciences. The authors train decoder-only transformers up to 4B parameters on 1 trillion chronologically filtered tokens and release the full pipeline.