How Robust are Limit Order Book Representations under Data Perturbation?

2021/10/10 by Wu, Yufei, Mahfouz, Mahmoud, Magazzeni, Daniele +1 · 1 citation
#FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Trading and Market Microstructure (q-fin.TR)

paper · doi:10.48550/arxiv.2110.04752

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.

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