  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.
