Taking over the Stock Market: Adversarial Perturbations Against Algorithmic Traders

Elior Nehemya, Yael Mathov, Asaf Shabtai, Yuval Elovici

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 different trading algorithms. We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points, in both white-box and black-box settings. Finally, we present various mitigation methods and discuss their limitations, which stem from the algorithmic trading domain. We believe that these findings should serve as a warning to the finance community regarding the threats in this area and promote further research on the risks associated with using automated learning models in the trading domain.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationApplied Data Science Track - European Conference, ECML PKDD 2021, Proceedings
EditorsYuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano
PublisherSpringer Science and Business Media Deutschland GmbH
Pages221-236
Number of pages16
ISBN (Print)9783030865139
DOIs
StatePublished - 1 Jan 2021
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: 13 Sep 202117 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12978 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
CityVirtual, Online
Period13/09/2117/09/21

Keywords

  • Adversarial examples
  • Algorithmic trading

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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