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

Lorenzo Lucchese, Mikko S. Pakkanen and Almut Veraart

International Journal of Forecasting, 2024, vol. 40, issue 4, 1587-1621

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.

Keywords: Price forecasting; Order book; High-frequency trading; Deep learning; Neural networks; Comparative studies; Model selection; Model confidence sets; Financial markets (search for similar items in EconPapers)
Date: 2024
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Working Paper: The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:4:p:1587-1621

DOI: 10.1016/j.ijforecast.2024.02.001

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