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Detection of algorithmic trading

Bogoev, D.; Karam, A.

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Authors

D. Bogoev



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 Mar 28, 2024
Journal Physica A: Statistical Mechanics and its Applications
Print ISSN 0378-4371
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

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