Large language models are increasingly used in social sciences, but their training data can introduce lookahead bias and training leakage. A good chronologically consistent language model requires efficient use of training data to maintain accuracy despite time-restricted data. Here, we overcome this challenge by training a suite of chronologically consistent large language models, ChronoBERT and ChronoGPT, which incorporate only the text data that would have been available at each point in time. Despite this strict temporal constraint, our models achieve strong performance on natural language processing benchmarks, outperforming or matching widely used models (e.g., BERT), and remain competitive with larger open-weight models. Lookahead bias is model and application-specific because even if a chronologically consistent language model has poorer language comprehension, a regression or prediction model applied on top of the language model can compensate. In an asset pricing application predicting next-day stock returns from financial news embeddings, we find that ChronoBERT and ChronoGPT’s real-time outputs achieve Sharpe ratios comparable to a much larger and potentially biased Llama model. Our results demonstrate a scalable, practical framework to mitigate training leakage, ensuring more credible backtests and predictions across finance and other social science domains.

We examine the role of race and ethnicity in the mutual fund context at two distinct levels. At the fund manager level, we document a co-racial tilt—funds managed by minority-dominant (White-dominant) teams allocate larger portfolio weights to minority-led (White-led) firms. This tilt is not associated with superior performance. It diminishes as fund managers gain experience, suggesting the presence of inaccurate statistical discrimination. At the investor level, we find that minority-dominant funds are penalized similarly to White-dominant funds for poor performance but are not rewarded as much for superior performance. Overall, our results uncover race-related investment choices at both levels.