Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders

Elior Nehemya, Yael Mathov, Asaf Shabtai, Yuval Elovici

Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021

In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize machine learning models to predict the market’s behavior and execute an investment strategy accordingly. However, machine learning models have been shown to be susceptible to input manipulations called adversarial examples. Despite this risk, the trading domain remains largely unexplored in the context of adversarial learning. In this study, we present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques to manipulate the input data stream in real time. The attacker creates a universal adversarial perturbation that is agnostic to the target model and time of use, which remains imperceptible when added to the input stream. We evaluate our attack on a real-world market data stream and target three …