Can machine learning unlock new insights into high-frequency trading?
<|reference_start|>Can machine learning unlock new insights into high-frequency trading?: 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.<|reference_end|>
@article{ibikunle2024can,
    title={Can machine learning unlock new insights into high-frequency trading?},
    author={G. Ibikunle, B. Moews, K. Rzayev},
    journal={arXiv preprint arXiv:2405.08101},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08101},
    primaryClass={q-fin.CP cs.LG}
}