How Robust are Limit Order Book Representations under Data Perturbation?

Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni and Manuela Veloso

Papers from arXiv.org

Abstract: The success of machine learning models in the financial domain is highly reliant on the quality of the data representation. In this paper, we focus on the representation of limit order book data and discuss the opportunities and challenges for learning representations of such data. We also experimentally analyse the issues associated with existing representations and present a guideline for future research in this area.

Date: 2021-10
New Economics Papers: this item is included in nep-big, nep-fmk and nep-mst
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2110.04752

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