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Adversarial trading

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  • Alexandre Miot

Abstract

Adversarial samples have drawn a lot of attention from the Machine Learning community in the past few years. An adverse sample is an artificial data point coming from an imperceptible modification of a sample point aiming at misleading. Surprisingly, in financial research, little has been done in relation to this topic from a concrete trading point of view. We show that those adversarial samples can be implemented in a trading environment and have a negative impact on certain market participants. This could have far reaching implications for financial markets either from a trading or a regulatory point of view.

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  • Alexandre Miot, 2020. "Adversarial trading," Papers 2101.03128, arXiv.org.
    • Handle: RePEc:arx:papers:2101.03128
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    References listed on IDEAS

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    1. Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2021. "Generative adversarial networks for financial trading strategies fine-tuning and combination," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 797-813, May.
    2. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
    3. Yaser Faghan & Nancirose Piazza & Vahid Behzadan & Ali Fathi, 2020. "Adversarial Attacks on Deep Algorithmic Trading Policies," Papers 2010.11388, arXiv.org.
    4. Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020. "A Data-driven Market Simulator for Small Data Environments," Papers 2006.14498, arXiv.org.
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