IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2110.05479.html
   My bibliography  Save this paper

Towards Robust Representation of Limit Orders Books for Deep Learning Models

Author

Listed:
  • Yufei Wu
  • Mahmoud Mahfouz
  • Daniele Magazzeni
  • Manuela Veloso

Abstract

The success of deep learning-based limit order book forecasting models is highly dependent on the quality and the robustness of the input data representation. A significant body of the quantitative finance literature focuses on utilising different deep learning architectures without taking into consideration the key assumptions these models make with respect to the input data representation. In this paper, we highlight the issues associated with the commonly-used representations of limit order book data from both a theoretical and practical perspectives. We also show the fragility of the representations under adversarial perturbations and propose two simple modifications to the existing representations that match the theoretical assumptions of deep learning models. Finally, we show experimentally how our proposed representations lead to state-of-the-art performance in both accuracy and robustness utilising very simple neural network architectures.

Suggested Citation

  • Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "Towards Robust Representation of Limit Orders Books for Deep Learning Models," Papers 2110.05479, arXiv.org, revised Dec 2022.
    • Handle: RePEc:arx:papers:2110.05479
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2110.05479
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books," Papers 1811.10041, arXiv.org.
    2. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
    3. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    4. Alec N. Kercheval & Yuan Zhang, 2015. "Modelling high-frequency limit order book dynamics with support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1315-1329, August.
    5. Justin A. Sirignano, 2019. "Deep learning for limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 549-570, April.
    6. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.
    7. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
    8. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.
    9. Abergel,Frédéric & Anane,Marouane & Chakraborti,Anirban & Jedidi,Aymen & Muni Toke,Ioane, 2016. "Limit Order Books," Cambridge Books, Cambridge University Press, number 9781107163980, January.
    10. Frédéric Abergel & Anirban Chakraborti & Aymen Jedidi & Ioane Muni Toke & Marouane Anane, 2016. "Limit Order Books," Post-Print hal-02177394, HAL.
    11. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 852-866, December.
    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. Andrea Coletta & Joseph Jerome & Rahul Savani & Svitlana Vyetrenko, 2023. "Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness," Papers 2306.12806, arXiv.org.
    2. Matteo Prata & Giuseppe Masi & Leonardo Berti & Viviana Arrigoni & Andrea Coletta & Irene Cannistraci & Svitlana Vyetrenko & Paola Velardi & Novella Bartolini, 2023. "LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study," Papers 2308.01915, arXiv.org, revised Sep 2023.

    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. Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "How Robust are Limit Order Book Representations under Data Perturbation?," Papers 2110.04752, arXiv.org.
    2. Antonio Briola & Jeremy Turiel & Tomaso Aste, 2020. "Deep Learning modeling of Limit Order Book: a comparative perspective," Papers 2007.07319, arXiv.org, revised Oct 2020.
    3. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.
    4. Zihao Zhang & Bryan Lim & Stefan Zohren, 2021. "Deep Learning for Market by Order Data," Papers 2102.08811, arXiv.org, revised Jul 2021.
    5. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Nov 2024.
    6. Jiwon Jung & Kiseop Lee, 2024. "Attention-Based Reading, Highlighting, and Forecasting of the Limit Order Book," Papers 2409.02277, arXiv.org, revised Nov 2024.
    7. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    8. Lorenzo Lucchese & Mikko Pakkanen & Almut Veraart, 2022. "The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective," Papers 2211.13777, arXiv.org, revised Oct 2023.
    9. Ilia Zaznov & Julian Kunkel & Alfonso Dufour & Atta Badii, 2022. "Predicting Stock Price Changes Based on the Limit Order Book: A Survey," Mathematics, MDPI, vol. 10(8), pages 1-33, April.
    10. Matteo Prata & Giuseppe Masi & Leonardo Berti & Viviana Arrigoni & Andrea Coletta & Irene Cannistraci & Svitlana Vyetrenko & Paola Velardi & Novella Bartolini, 2023. "LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study," Papers 2308.01915, arXiv.org, revised Sep 2023.
    11. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    12. Martin Magris & Mostafa Shabani & Alexandros Iosifidis, 2022. "Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets," Papers 2203.03613, arXiv.org, revised Jan 2023.
    13. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    14. Antonio Briola & Jeremy Turiel & Riccardo Marcaccioli & Alvaro Cauderan & Tomaso Aste, 2021. "Deep Reinforcement Learning for Active High Frequency Trading," Papers 2101.07107, arXiv.org, revised Aug 2023.
    15. Peng Wu & Marcello Rambaldi & Jean-Franc{c}ois Muzy & Emmanuel Bacry, 2019. "Queue-reactive Hawkes models for the order flow," Papers 1901.08938, arXiv.org.
    16. Thomas Spooner & Rahul Savani, 2020. "Robust Market Making via Adversarial Reinforcement Learning," Papers 2003.01820, arXiv.org, revised Jul 2020.
    17. Svitlana Vyetrenko & David Byrd & Nick Petosa & Mahmoud Mahfouz & Danial Dervovic & Manuela Veloso & Tucker Hybinette Balch, 2019. "Get Real: Realism Metrics for Robust Limit Order Book Market Simulations," Papers 1912.04941, arXiv.org.
    18. Junyi Li & Xitong Wang & Yaoyang Lin & Arunesh Sinha & Micheal P. Wellman, 2020. "Generating Realistic Stock Market Order Streams," Papers 2006.04212, arXiv.org.
    19. Charles-Albert Lehalle & Eyal Neuman, 2019. "Incorporating signals into optimal trading," Finance and Stochastics, Springer, vol. 23(2), pages 275-311, April.
    20. Michael Karpe & Jin Fang & Zhongyao Ma & Chen Wang, 2020. "Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation," Papers 2006.05574, arXiv.org, revised Sep 2020.

    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:2110.05479. 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.