More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability is underexplored. In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet.

本文探讨使用社交媒体和机器学习技术进行股票预测时所面临的对抗攻击问题，通过对多种股票预测模型进行实验，提出了一种对抗攻击方法，并通过贪心优化算法生成语义相近但含有扰动的内容来达到攻击目的。结果表明，这种攻击方法可以稳定地使股票预测的成功率降低并导致巨大的交易损失。

一言既出，价值千金：对推特的对抗攻击诱导股票预测误差