Author

Eljas Linna

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#artificial intelligence Preprint Sep 2026

UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting

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...

D. Manoharan, Eljas Linna, K. Baltakys et al. · 0 citations

#artificial intelligence Nov 2025

LOBERT: Generative AI Foundation Model for Limit Order Book Messages

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.

Eljas Linna, K. Baltakys, Alexandros Iosifidis et al. · arXiv.org · 3 citations