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

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions