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

• URL: http://arxiv.org/abs/2405.08101v1
• Date: Mon, 13 May 2024 18:28:39 GMT
• Title: Can machine learning unlock new insights into high-frequency trading?
• Authors: G. Ibikunle, B. Moews, K. Rzayev,
• Abstract summary: We introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Our metrics have implications for understanding the information production process in financial markets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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