IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1808.03668.html
   My bibliography  Save this paper

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

Author

Listed:
  • Zihao Zhang
  • Stefan Zohren
  • Stephen Roberts

Abstract

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.

Suggested Citation

  • Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
  • Handle: RePEc:arx:papers:1808.03668
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1808.03668
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven markets," Post-Print hal-00543545, HAL.
    2. Alexander Lipton & Umberto Pesavento & Michael G Sotiropoulos, 2013. "Trade arrival dynamics and quote imbalance in a limit order book," Papers 1312.0514, arXiv.org.
    3. Bacchetta, Philippe & Mertens, Elmar & van Wincoop, Eric, 2009. "Predictability in financial markets: What do survey expectations tell us?," Journal of International Money and Finance, Elsevier, vol. 28(3), pages 406-426, April.
    4. Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
    5. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
    6. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    7. Jim Gatheral & Roel Oomen, 2010. "Zero-intelligence realized variance estimation," Finance and Stochastics, Springer, vol. 14(2), pages 249-283, April.
    8. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
    9. 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.
    10. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven markets," Post-Print hal-00543540, HAL.
    11. Charles Cao & Oliver Hansch & Xiaoxin Wang, 2009. "The information content of an open limit‐order book," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(1), pages 16-41, January.
    12. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    13. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    14. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven markets," Post-Print hal-00543547, HAL.
    15. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven Markets," Working Papers hal-00515891, HAL.
    16. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven markets," Post-Print hal-00543544, HAL.
    17. Bollerslev, Tim & Marrone, James & Xu, Lai & Zhou, Hao, 2014. "Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(3), pages 633-661, June.
    18. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    19. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    20. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
    21. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven markets," Post-Print hal-00543546, HAL.
    22. Matthew F Dixon, 2017. "Sequence Classification of the Limit Order Book using Recurrent Neural Networks," Papers 1707.05642, arXiv.org.
    23. Avellaneda, Marco & Reed, Josh & Stoikov, Sasha, 2011. "Forecasting prices from level-I quotes in the presence of hidden liquidity," Algorithmic Finance, IOS Press, vol. 1(1), pages 35-43.
    24. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    25. Ioanid Rosu, 2010. "Liquidity and Information in Order Driven markets," Post-Print hal-00543541, HAL.
    26. Fischer, Thomas & Krauss, Christopher, 2017. "Deep learning with long short-term memory networks for financial market predictions," FAU Discussion Papers in Economics 11/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    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. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books," Papers 1811.10041, 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. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.
    4. 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.
    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).
    6. Bryan Lim & Stefan Zohren & Stephen Roberts, 2019. "Enhancing Time Series Momentum Strategies Using Deep Neural Networks," Papers 1904.04912, arXiv.org, revised Sep 2020.

    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. Alberto Ciacci & Takumi Sueshige & Hideki Takayasu & Kim Christensen & Misako Takayasu, 2020. "The microscopic relationships between triangular arbitrage and cross-currency correlations in a simple agent based model of foreign exchange markets," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    2. Aiusha Sangadiev & Rodrigo Rivera-Castro & Kirill Stepanov & Andrey Poddubny & Kirill Bubenchikov & Nikita Bekezin & Polina Pilyugina & Evgeny Burnaev, 2020. "DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data," Papers 2008.12152, arXiv.org.
    3. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    4. Dugast, J., 2013. "Limited attention and news arrival in limit order markets," Working papers 449, Banque de France.
    5. Yiqi Liu & Qiang Liu & Zhi Liu & Deng Ding, 2017. "Determining the integrated volatility via limit order books with multiple records," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1697-1714, November.
    6. Hautsch, Nikolaus & Huang, Ruihong, 2012. "The market impact of a limit order," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 501-522.
    7. Yosra Mefteh Rekik & Younes Boujelbene, 2015. "Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 33-43, April.
    8. Ingrid Lo & Stephen Sapp, 2011. "Belief Dispersion and Order Submission Strategies in the Foreign Exchange Market," Staff Working Papers 11-8, Bank of Canada.
    9. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
    10. Kozhan, Roman & Salmon, Mark, 2012. "The information content of a limit order book: The case of an FX market," Journal of Financial Markets, Elsevier, vol. 15(1), pages 1-28.
    11. Alessio Emanuele Biondo, 2020. "Information versus imitation in a real-time agent-based model of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(3), pages 613-631, July.
    12. Qi Zhao, 2020. "A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data," Papers 2010.07404, arXiv.org.
    13. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Mar 2020.
    14. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
    15. Paul Bilokon & Yitao Qiu, 2023. "Transformers versus LSTMs for electronic trading," Papers 2309.11400, arXiv.org.
    16. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    17. Martin D. Gould & Julius Bonart, 2015. "Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book," Papers 1512.03492, arXiv.org.
    18. Kieran Wood & Stephen Roberts & Stefan Zohren, 2021. "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers 2105.13727, arXiv.org, revised Dec 2021.
    19. Ymir Makinen & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Forecasting of Jump Arrivals in Stock Prices: New Attention-based Network Architecture using Limit Order Book Data," Papers 1810.10845, arXiv.org.
    20. Matthew F Dixon, 2017. "A High Frequency Trade Execution Model for Supervised Learning," Papers 1710.03870, arXiv.org, revised Dec 2017.

    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:1808.03668. 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.