KANFormer for Predicting Fill Probabilities via Survival Analysis in Limit Order Books

Jinfeng Zhong (), Emmanuel Bacry (), Agathe Guilloux () and Jean-François Muzy ()
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Jinfeng Zhong: CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique, UP8 - Université Paris 8
Emmanuel Bacry: CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Agathe Guilloux: HeKA | U1346 - Health data-and model-driven approaches for Knowledge Acquisition - INSERM - Institut National de la Santé et de la Recherche Médicale - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique - UPCité - Université Paris Cité
Jean-François Muzy: SPE - Laboratoire « Sciences pour l’Environnement » (UMR CNRS 6134 SPE) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]

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Abstract: This paper introduces KANFormer, a novel deep-learning-based model for predicting the time-to-fill of limit orders by leveraging both market- and agent-level information. KANFormer combines a Dilated Causal Convolutional network with a Transformer encoder, enhanced by Kolmogorov–Arnold Networks (KANs), which improve nonlinear approximation. Unlike existing models that rely solely on a series of snapshots of the limit order book, KANFormer integrates the actions of agents related to LOB dynamics and the position of the order in the queue to more effectively capture patterns related to execution likelihood. We evaluate the model using CAC 40 index futures data {with labeled orders}. The results show that KANFormer outperforms existing works in both calibration (Right-Censored Log-Likelihood, Integrated Brier Score) and discrimination (C-index, time-dependent AUC). We further analyze feature importance over time using SHAP (SHapley Additive exPlanations). Our results highlight the benefits of combining rich market signals with expressive neural architectures to achieve accurate and interpretable predictions of fill probabilities.

Keywords: Limit order books; Survival analysis; Fill probability predictions; Order submissions; High-frequency trading (search for similar items in EconPapers)
Date: 2025-12-05
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