Adversarial trading

TL;DR

This paper explores how adversarial samples, originally studied in machine learning, can be applied to financial trading, potentially misleading market participants and impacting market stability and regulation.

Contribution

It introduces the concept of adversarial samples in a trading context and demonstrates their potential negative effects on financial markets.

Findings

Adversarial samples can be implemented in trading environments.

Such samples can mislead market participants.

Potential regulatory implications are identified.

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.