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Machine learning and speed in high-frequency trading

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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

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    2. Carè, Rosella & Cumming, Douglas, 2024. "Technology and automation in financial trading: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 71(C).
    3. Breckenfelder, Johannes, 2024. "Competition among high-frequency traders and market quality," Journal of Economic Dynamics and Control, Elsevier, vol. 166(C).
    4. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    5. Choudhary, Priya & Thenmozhi, M., 2024. "Fintech and financial sector: ADO analysis and future research agenda," International Review of Financial Analysis, Elsevier, vol. 93(C).
    6. Buis, Boyd & Pieterse-Bloem, Mary & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2024. "Gamma positioning and market quality," Journal of Economic Dynamics and Control, Elsevier, vol. 164(C).
    7. Kou, Mingting & Yang, Yuanqi & Chen, Kaihua, 2024. "Financial technology research: Past and future trajectories," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 162-181.
    8. Quintiliani, Andrea, 2025. "Small and medium-sized enterprises and sustainable transition: Role of FinTech in a country's banking ecosystem," Research in International Business and Finance, Elsevier, vol. 73(PA).

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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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