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The short-term predictability of returns in order book markets: A deep learning perspective

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  • Lucchese, Lorenzo
  • Pakkanen, Mikko S.
  • Veraart, Almut E.D.

Abstract

This paper uses deep learning techniques to conduct a systematic large-scale analysis of order book-driven predictability in high-frequency returns. First, we introduce a new and robust representation of the order book, the volume representation. Next, we conduct an extensive empirical experiment to address various questions regarding predictability. We investigate if and how far ahead there is predictability, the importance of a robust data representation, the advantages of multi-horizon modeling, and the presence of universal trading patterns. We use model confidence sets, which provide a formalized statistical inference framework well suited to answer these questions. Our findings show that at high frequencies, predictability in mid-price returns is not just present but ubiquitous. The performance of the deep learning models is strongly dependent on the choice of order book representation, and in this respect, the volume representation appears to have multiple practical advantages.

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  • Lucchese, Lorenzo & Pakkanen, Mikko S. & Veraart, Almut E.D., 2024. "The short-term predictability of returns in order book markets: A deep learning perspective," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1587-1621.
    • Handle: RePEc:eee:intfor:v:40:y:2024:i:4:p:1587-1621
    DOI: 10.1016/j.ijforecast.2024.02.001
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    Cited by:

    1. Matteo Prata & Giuseppe Masi & Leonardo Berti & Viviana Arrigoni & Andrea Coletta & Irene Cannistraci & Svitlana Vyetrenko & Paola Velardi & Novella Bartolini, 2023. "LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study," Papers 2308.01915, arXiv.org, revised Sep 2023.
    2. Alvaro Arroyo & Alvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2023. "Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers," Papers 2306.05479, arXiv.org.

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