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Machine learning and speed in high-frequency trading

Author

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  • Arifovic, Jasmina
  • He, Xue-zhong
  • Wei, Lijian

Abstract

The creative destruction wrought by high-frequency algorithmic trading has raised increasing concerns about the effect of machine learning behaviors and ultra high-frequency trading on financial markets. By employing a genetic algorithm with a classifier system as an adaptive learning tool, we address some of these concerns by studying a dynamic limit order market model with asymmetric information and varying speeds of high-frequency trading (HFT). We show that HFT benefits uninformed traders, improves information efficiency but reduces market liquidity. We find that there is a trade-off where a competition effect erodes the information and speed advantages of high-frequency traders, increasing trading speeds of HF traders reduces market liquidity but generates a hump-shaped relationship to the profitability of high-frequency traders and information efficiency. This research finds there may be potential benefits to throttling the trading speed arms race to improve market efficiency. We also find that strategic algorithmic trading compensates for diminishments in speed advantages, providing an insight on machine behavior in the FinTech age.

Suggested Citation

  • Arifovic, Jasmina & He, Xue-zhong & Wei, Lijian, 2022. "Machine learning and speed in high-frequency trading," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:dyncon:v:139:y:2022:i:c:s0165188922001439
    DOI: 10.1016/j.jedc.2022.104438
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    References listed on IDEAS

    as
    1. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    2. Ladley, Daniel, 2020. "The high frequency trade off between speed and sophistication," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    3. Rosu , Ioanid, 2016. "Fast and Slow Informed Trading," HEC Research Papers Series 1123, HEC Paris.
    4. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    5. Axel Groß‐KlußMann & Nikolaus Hautsch, 2013. "Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(8), pages 724-742, December.
    6. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. "An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-1689, December.
    7. F. Douglas Foster & Xue-Zhong He & Junqing Kang & Shen Lin, 2019. "The Microstructure of Endogenous Liquidity Provision," Research Paper Series 402, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. Michael Kopel & Herbert Dawid, 1998. "On economic applications of the genetic algorithm: a model of the cobweb type," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 297-315.
    9. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    10. Theissen, Erik, 2000. "Market structure, informational efficiency and liquidity: An experimental comparison of auction and dealer markets," Journal of Financial Markets, Elsevier, vol. 3(4), pages 333-363, November.
    11. Olivier Brandouy & Angelo Corelli & Iryna Veryzhenko & Roger Waldeck, 2012. "A re-examination of the "zero is enough" hypothesis in the emergence of financial stylized facts," Post-Print hal-00951003, HAL.
    12. Olivier Brandouy & Angelo Corelli & Iryna Veryzhenko & Roger Waldeck, 2012. "A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 223-248, October.
    13. Dawid, Herbert, 1999. "On the convergence of genetic learning in a double auction market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1545-1567, September.
    14. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    15. Goettler, Ronald L. & Parlour, Christine A. & Rajan, Uday, 2009. "Informed traders and limit order markets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 67-87, July.
    16. Eric Budish & Peter Cramton & John Shim, 2015. "Editor's Choice The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1547-1621.
    17. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    18. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    19. 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.
    20. Yan, Yuxing & Zhang, Shaojun, 2012. "An improved estimation method and empirical properties of the probability of informed trading," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 454-467.
    21. Lijian Wei & Xiong Xiong & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2017. "The effect of genetic algorithm learning with a classifier system in limit order markets," Published Paper Series 2017-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    22. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    23. Giulia Iori & Carl Chiarella, 2002. "A simple microstructure model of double auction markets," Computing in Economics and Finance 2002 44, Society for Computational Economics.
    24. Routledge, Bryan R, 1999. "Adaptive Learning in Financial Markets," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1165-1202.
    25. Chen Yao & Mao Ye, 2018. "Why Trading Speed Matters: A Tale of Queue Rationing under Price Controls," Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2157-2183.
    26. Albert J. Menkveld, 2016. "The Economics of High-Frequency Trading: Taking Stock," Annual Review of Financial Economics, Annual Reviews, vol. 8(1), pages 1-24, October.
    27. Kluger, Brian D. & McBride, Mark E., 2011. "Intraday trading patterns in an intelligent autonomous agent-based stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 226-245, August.
    28. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    29. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: I. Empirical facts," Post-Print hal-00621058, HAL.
    30. Matteo Aquilina & Eric Budish & Peter O'Neill, 2021. "Quantifying the high-frequency trading "arms race"," BIS Working Papers 955, Bank for International Settlements.
    31. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    32. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-541, June.
    33. Routledge, Bryan R., 2001. "Genetic Algorithm Learning To Choose And Use Information," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 303-325, April.
    34. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    35. Olivier Brandouy & Angelo Corelli & Iryna Veryzhenko & Roger Waldeck, 2012. "A re-examination of the "zero is enough" hypothesis in the emergence of financial stylized facts," Post-Print hal-00951015, HAL.
    36. Ioanid Rosu, 2012. "Order Choice and Information in Limit Order Markets," Post-Print hal-00712067, HAL.
    37. Matteo Aquilina & Eric Budish & Peter O'Neill, 2021. "Quantifying the High-Frequency Trading "Arms Race"," NBER Working Papers 29011, National Bureau of Economic Research, Inc.
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    1. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).

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    More about this item

    Keywords

    High-frequency trading; Price efficiency; Machine learning; Genetic algorithm; Limit order market;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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