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Idiosyncrasies and challenges of data driven learning in electronic trading

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  • Vangelis Bacoyannis
  • Vacslav Glukhov
  • Tom Jin
  • Jonathan Kochems
  • Doo Re Song

Abstract

We outline the idiosyncrasies of neural information processing and machine learning in quantitative finance. We also present some of the approaches we take towards solving the fundamental challenges we face.

Suggested Citation

  • Vangelis Bacoyannis & Vacslav Glukhov & Tom Jin & Jonathan Kochems & Doo Re Song, 2018. "Idiosyncrasies and challenges of data driven learning in electronic trading," Papers 1811.09549, arXiv.org, revised Nov 2018.
    • Handle: RePEc:arx:papers:1811.09549
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    File URL: http://arxiv.org/pdf/1811.09549
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    References listed on IDEAS

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    1. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
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    Cited by:

    1. Jia Wang & Hongwei Zhu & Jiancheng Shen & Yu Cao & Benyuan Liu, 2022. "Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements," Papers 2202.03158, arXiv.org.
    2. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    3. Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Hongwei Zhu, 2021. "CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets," Papers 2104.04041, arXiv.org.
    4. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.

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