The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems powered by Deep Learning

TL;DR

This paper examines the security vulnerabilities of deep learning-based algorithmic trading systems, introducing ephemeral perturbations that can mislead models and degrade trading performance.

Contribution

It formalizes the concept of ephemeral perturbations and evaluates their impact on the security and profitability of DL-powered trading systems.

Findings

Small input changes can mislead DL models in trading

Adversarial perturbations reduce trading profitability

DL security in finance is underexplored

Abstract

We study the security of stock price forecasting using Deep Learning (DL) in computational finance. Despite abundant prior research on the vulnerability of DL to adversarial perturbations, such work has hitherto hardly addressed practical adversarial threat models in the context of DL-powered algorithmic trading systems (ATS). Specifically, we investigate the vulnerability of ATS to adversarial perturbations launched by a realistically constrained attacker. We first show that existing literature has paid limited attention to DL security in the financial domain, which is naturally attractive for adversaries. Then, we formalize the concept of ephemeral perturbations (EP), which can be used to stage a novel type of attack tailored for DL-based ATS. Finally, we carry out an end-to-end evaluation of our EP against a profitable ATS. Our results reveal that the introduction of small changes to the input stock prices not only (i) induces the DL model to behave incorrectly but also (ii) leads the whole ATS to make suboptimal buy/sell decisions, resulting in a worse financial performance of the targeted ATS.