Adversarial trading
This research addresses the potential vulnerability of financial markets to adversarial attacks, which is a problem for traders and regulators, by showing how adversarial samples can be used to mislead trading algorithms.
This paper explores the application of adversarial samples in a trading environment, demonstrating that these imperceptible data modifications can negatively impact certain market participants. The study shows that such samples can be implemented in a trading context.
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.