Chronologically Consistent Large Language Models

ArXiv ID: 2502.21206 “View on arXiv”

Authors: Unknown

Abstract

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, we find that ChronoBERT and ChronoGPT’s real-time outputs achieve Sharpe ratios comparable to a much larger Llama model, indicating that lookahead bias is modest. Our results demonstrate a scalable, practical framework to mitigate training leakage, ensuring more credible backtests and predictions across finance and other social science domains.

Keywords: Lookahead Bias, LLM (Large Language Model), Asset Pricing, Chronological Training, Natural Language Processing

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper employs sophisticated deep learning architectures (BERT, GPT) and complex training regimes, but the math is largely applied from established NLP literature rather than introducing novel theory. Empirical rigor is high, featuring extensive backtesting on financial news data, Sharpe ratio analysis, and comparisons to baselines, demonstrating a data-heavy, implementation-focused study.

  flowchart TD
    A["Research Goal: <br>Mitigate Lookahead Bias in LLMs <br>for Social Science"] --> B["Methodology: <br>Chronological Training"]
    B --> C{"Data Inputs: <br>Time-Restrained Text Data"}
    C --> D["Computational Process: <br>Train ChronoBERT & ChronoGPT"]
    D --> E["Outcomes 1: <br>NLP Benchmark Performance <br>(Match/Met BERT)"]
    D --> F["Outcomes 2: <br>Asset Pricing Application <br>(Stock Returns Prediction)"]
    F --> G["Result: <br>Comparable Sharpe Ratios <br>(Modest Lookahead Bias)"]