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Can machine learning unlock new insights into high-frequency trading?

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  • G. Ibikunle
  • B. Moews
  • K. Rzayev

Abstract

We design and train machine learning models to capture the nonlinear interactions between financial market dynamics and high-frequency trading (HFT) activity. In doing so, we introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Both types of HFT strategies increase activity in response to information events and decrease it when trading speed is restricted, with liquidity-supplying strategies demonstrating greater responsiveness. Liquidity-demanding HFT is positively linked with latency arbitrage opportunities, whereas liquidity-supplying HFT is negatively related, aligning with theoretical expectations. Our metrics have implications for understanding the information production process in financial markets.

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  • G. Ibikunle & B. Moews & K. Rzayev, 2024. "Can machine learning unlock new insights into high-frequency trading?," Papers 2405.08101, arXiv.org.
    • Handle: RePEc:arx:papers:2405.08101
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    References listed on IDEAS

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    1. Brogaard, Jonathan & Carrion, Allen & Moyaert, Thibaut & Riordan, Ryan & Shkilko, Andriy & Sokolov, Konstantin, 2018. "High frequency trading and extreme price movements," Journal of Financial Economics, Elsevier, vol. 128(2), pages 253-265.
    2. Lee, Charles M. C. & Radhakrishna, Balkrishna, 2000. "Inferring investor behavior: Evidence from TORQ data," Journal of Financial Markets, Elsevier, vol. 3(2), pages 83-111, May.
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