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Deep limit order book forecasting: a microstructural guide

Author

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  • Briola, Antonio
  • Bartolucci, Silvia
  • Aste, Tomaso

Abstract

We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release ‘LOBFrame’, an open-source code base to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art deep learning models' forecasting capabilities. Our results are twofold. We demonstrate that the stocks' microstructural characteristics influence the efficacy of deep learning methods and that their high forecasting power does not necessarily correspond to actionable trading signals. We argue that traditional machine learning metrics fail to adequately assess the quality of forecasts in the Limit Order Book context. As an alternative, we propose an innovative operational framework that evaluates predictions' practicality by focusing on the probability of accurately forecasting complete transactions. This work offers academics and practitioners an avenue to make informed and robust decisions on the application of deep learning techniques, their scope and limitations, effectively exploiting emergent statistical properties of the Limit Order Book.

Suggested Citation

  • Briola, Antonio & Bartolucci, Silvia & Aste, Tomaso, 2025. "Deep limit order book forecasting: a microstructural guide," LSE Research Online Documents on Economics 128950, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:128950
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    File URL: http://eprints.lse.ac.uk/128950/
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    References listed on IDEAS

    as
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    1. Sabrina Aufiero & Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2025. "Mapping Microscopic and Systemic Risks in TradFi and DeFi: a literature review," Papers 2508.12007, arXiv.org.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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