IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1811.09549.html
   My bibliography  Save this paper

Idiosyncrasies and challenges of data driven learning in electronic trading

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1811.09549
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.
    2. Takuya Shintate & Lukáš Pichl, 2019. "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," JRFM, MDPI, vol. 12(1), pages 1-15, January.
    3. Bryan Lim & Stefan Zohren & Stephen Roberts, 2019. "Enhancing Time Series Momentum Strategies Using Deep Neural Networks," Papers 1904.04912, arXiv.org, revised Sep 2020.
    4. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books," Papers 1811.10041, arXiv.org.
    5. Song, Youcheng & Wang, Haijun & Peng, Xiaotao & Sun, Duan & Chen, Rui, 2023. "Modeling land use change prediction using multi-model fusion techniques: A case study in the Pearl River Delta, China," Ecological Modelling, Elsevier, vol. 486(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1811.09549. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.