Author

D. Manoharan

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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 Preprint Sep 2026

Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling

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...

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