Deep Learning modeling of Limit Order Book: a comparative perspective

Antonio Briola, Jeremy Turiel and Tomaso Aste

Papers from arXiv.org

Abstract: The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature space and dataset, and then clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. We observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.

Date: 2020-07, Revised 2020-10
New Economics Papers: this item is included in nep-big and nep-mst
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2007.07319

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