  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.
