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Can machine learning unlock new insights into high-frequency trading?
- Publication Year :
- 2024
-
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.<br />Comment: 56 pages, 4 figures, 8 tables
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2405.08101
- Document Type :
- Working Paper