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A series of point-in-time language models designed to remain uninformed about future information, addressing temporal bias in LLMs for time-series forecasting applications.

We demonstrate that semantic shifts in news language can be causally linked to financial market shocks, with partisan differences influencing predictive power. Text signals prove particularly valuable during unexpected events like COVID-19.

Demonstrates that traditional machine learning methods combining fundamental data with text embeddings outperform current LLMs at credit rating forecasting, highlighting limitations of LLMs for multimodal financial tasks.

Using transformer-based language models to extract features from Reddit posts for COVID-19 forecasting, outperforming traditional epidemiological data in predicting upward trend signals.