IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v3y2022i1d10.1007_s43069-022-00124-0.html
   My bibliography  Save this article

Predictive Market Making via Machine Learning

Author

Listed:
  • Abbas Haider

    (Ulster University)

  • Hui Wang

    (Ulster University)

  • Bryan Scotney

    (Ulster University)

  • Glenn Hawe

    (Ulster University)

Abstract

Market making (MM) is an important means of providing liquidity to the stock markets. Recent research suggests that reinforcement learning (RL) can improve MM significantly in terms of returns. In the latest work on RL-based MM, the reward is a function of equity returns, calculated based on its current price, and the inventory of MM agent. As a result, the agent’s return is maximised and liquidity is provided. If the price movement is known and this information is optimally utilised, there is potential that the MM agent’s return can be further improved. Important questions are, how to predict stock price movement, and how to utilise such prediction? In this paper, we introduce the concept of predictive market marking (PMM) and present our method for PMM, which comprises a RL-based MM agent and a deep neural network (DNN)-based price predictor. A key component of PMM is the consolidated price equation (CPE), which amalgamates an equity’s present and predicted market prices into a consolidated price, which is used to generate ask and bid quotes that reflect both current price and future movement. Our PMM method is evaluated against the state-of-the-art (RL-based MM) and a traditional MM method, using ten stocks and three exchange traded funds (ETFs). Out-of-sample backtesting showed that our PMM method outperformed the two benchmark methods.

Suggested Citation

  • Abbas Haider & Hui Wang & Bryan Scotney & Glenn Hawe, 2022. "Predictive Market Making via Machine Learning," SN Operations Research Forum, Springer, vol. 3(1), pages 1-21, March.
  • Handle: RePEc:spr:snopef:v:3:y:2022:i:1:d:10.1007_s43069-022-00124-0
    DOI: 10.1007/s43069-022-00124-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-022-00124-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43069-022-00124-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Laurent Deville, 2008. "Exchange Traded Funds: History, Trading, and Research," Springer Optimization and Its Applications, in: Constantin Zopounidis & Michael Doumpos & Panos M. Pardalos (ed.), Handbook of Financial Engineering, pages 67-98, Springer.
    2. Xiaodan Liang & Zhaodi Ge & Liling Sun & Maowei He & Hanning Chen, 2019. "LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, July.
    3. 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.
    4. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    5. Attila Ceffer & Janos Levendovszky & Norbert Fogarasi, 2019. "Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 281-303, June.
    6. Fuli Feng & Huimin Chen & Xiangnan He & Ji Ding & Maosong Sun & Tat-Seng Chua, 2018. "Enhancing Stock Movement Prediction with Adversarial Training," Papers 1810.09936, arXiv.org, revised Jun 2019.
    7. repec:dau:papers:123456789/903 is not listed on IDEAS
    8. 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.
    9. Nelson Granados & Alok Gupta & Robert J. Kauffman, 2010. "Research Commentary---Information Transparency in Business-to-Consumer Markets: Concepts, Framework, and Research Agenda," Information Systems Research, INFORMS, vol. 21(2), pages 207-226, June.
    10. Thomas Spooner & John Fearnley & Rahul Savani & Andreas Koukorinis, 2018. "Market Making via Reinforcement Learning," Papers 1804.04216, arXiv.org.
    11. Nicholas T. Chan and Christian Shelton, 2001. "An Adaptive Electronic Market-Maker," Computing in Economics and Finance 2001 146, Society for Computational Economics.
    12. Dong Zhao & Chunyu Huang & Yan Wei & Fanhua Yu & Mingjing Wang & Huiling Chen, 2017. "An Effective Computational Model for Bankruptcy Prediction Using Kernel Extreme Learning Machine Approach," Computational Economics, Springer;Society for Computational Economics, vol. 49(2), pages 325-341, February.
    13. Yan Zhang & Peter Trubey, 2019. "Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1043-1063, October.
    14. Sepehr Ramyar & Farhad Kianfar, 2019. "Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 743-761, February.
    15. D. Fernández-Arias & M. López-Martín & T. Montero-Romero & F. Martínez-Estudillo & F. Fernández-Navarro, 2018. "Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 275-297, June.
    16. Laurent Deville, 2008. "Exchange Traded Funds: History, Trading and Research," Post-Print halshs-00162223, HAL.
    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. Tristan Lim, 2022. "Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning," Papers 2211.01346, arXiv.org, revised Jan 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. Thomas Spooner & Rahul Savani, 2020. "Robust Market Making via Adversarial Reinforcement Learning," Papers 2003.01820, arXiv.org, revised Jul 2020.
    2. 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.
    3. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
    4. Thomas Spooner & John Fearnley & Rahul Savani & Andreas Koukorinis, 2018. "Market Making via Reinforcement Learning," Papers 1804.04216, arXiv.org.
    5. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    6. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    7. Qing-Qing Yang & Wai-Ki Ching & Jiawen Gu & Tak-Kuen Siu, 2020. "Trading strategy with stochastic volatility in a limit order book market," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(1), pages 277-301, June.
    8. Nelson Vadori & Leo Ardon & Sumitra Ganesh & Thomas Spooner & Selim Amrouni & Jared Vann & Mengda Xu & Zeyu Zheng & Tucker Balch & Manuela Veloso, 2022. "Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations," Papers 2210.07184, arXiv.org, revised Aug 2023.
    9. Joseph Jerome & Leandro Sanchez-Betancourt & Rahul Savani & Martin Herdegen, 2022. "Model-based gym environments for limit order book trading," Papers 2209.07823, arXiv.org.
    10. Jiafa He & Cong Zheng & Can Yang, 2023. "Integrating Tick-level Data and Periodical Signal for High-frequency Market Making," Papers 2306.17179, arXiv.org.
    11. Bruno Gašperov & Stjepan Begušić & Petra Posedel Šimović & Zvonko Kostanjčar, 2021. "Reinforcement Learning Approaches to Optimal Market Making," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    12. Saran Ahuja & George Papanicolaou & Weiluo Ren & Tzu-Wei Yang, 2016. "Limit order trading with a mean reverting reference price," Papers 1607.00454, arXiv.org, revised Nov 2016.
    13. Antoine Jacquier & Hao Liu, 2017. "Optimal liquidation in a Level-I limit order book for large tick stocks," Papers 1701.01327, arXiv.org, revised Nov 2017.
    14. Anatoliy Swishchuk, 2020. "Stochastic Modelling of Big Data in Finance," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1613-1630, December.
    15. Sumitra Ganesh & Nelson Vadori & Mengda Xu & Hua Zheng & Prashant Reddy & Manuela Veloso, 2019. "Reinforcement Learning for Market Making in a Multi-agent Dealer Market," Papers 1911.05892, arXiv.org.
    16. 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.
    17. Ewa Lechman & Adam Marszk, 2014. "Reshaping financial systems. New technologies and financial innovations - evidence from the United States, Mexico and Brazil," GUT FME Working Paper Series A 20, Faculty of Management and Economics, Gdansk University of Technology.
    18. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    19. Jian Guo & Saizhuo Wang & Lionel M. Ni & Heung-Yeung Shum, 2022. "Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence," Papers 2301.04020, arXiv.org.
    20. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.

    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:spr:snopef:v:3:y:2022:i:1:d:10.1007_s43069-022-00124-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.