Deep limit order book forecasting: a microstructural guide

Antonio Briola, Silvia Bartolucci and Tomaso Aste

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: deep learning; econophysics; high frequency trading; limit order book; market microstructure (search for similar items in EconPapers)
JEL-codes: C32 C53 G14 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2025-07-22
New Economics Papers: this item is included in nep-big, nep-for and nep-mst
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Published in Quantitative Finance, 22, July, 2025. ISSN: 1469-7688

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:128950

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