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Forecasting short-horizon mid-price movements from limit order book (LOB) data is central to algorithmic trading, yet most deep LOB forecasters are point predictors: they output a direction or a displacement, but never indicate which of their forecasts can be trusted. We introduce UQ-LOB, a lightweight, encoder-agnosti...
A model-agnostic framework is introduced that compares a trained forecaster's predictive distributions before and after injecting mechanically valid counterfactual messages, defining short-horizon model-implied market impact and providing evidence that pretrained Limit Order Book forecasters can be repurposed for scena...
This work introduces LOBERT, a general-purpose encoder-only foundation model for LOB data suitable for downstream fine-tuning and achieves leading performance in tasks such as predicting mid-price movements and next messages, while reducing the required context length compared to previous methods.
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