Detection of algorithmic trading

Bogoev, D.; Karam, A.

Abstract

We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns. Quote volatility ratio is based on the rate of oscillation of the best ask and best bid quotes over an extremely short period of time; whereas price momentum ratio is based on identifying patterns of rapid upward or downward movement in prices. The two ratios are evaluated across several asset classes. We further run a two-stage Artificial Neural Network experiment on the quote volatility ratio; the first stage is used to detect the quote volatility patterns resulting from algorithmic activity, while the second is used to validate the quality of signal detection provided by our measure.

Citation

Bogoev, D., & Karam, A. (2017). Detection of algorithmic trading. Physica A: Statistical Mechanics and its Applications, 484, 168-181. https://doi.org/10.1016/j.physa.2017.04.157

• Journal Article Type: Article
• Acceptance Date: Apr 30, 2017
• Online Publication Date: May 10, 2017
• Publication Date: Oct 15, 2017
• Deposit Date: May 8, 2017
• Publicly Available Date: May 10, 2018
• Journal: Physica A: Statistical Mechanics and its Applications
• Print ISSN: 0378-4371
• Electronic ISSN: 1873-2119
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 484
• Pages: 168-181
• DOI: https://doi.org/10.1016/j.physa.2017.04.157
• Public URL: https://durham-repository.worktribe.com/output/1358815