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

More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather information and predict certain stocks' prices (meme stock). However, text-based models are known to be vulnerable to adversarial attacks, but whether stock prediction models have similar adversarial vulnerability is underexplored. In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models (StockNet, FinGRU, FinLSTM). 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, with capabilities of causing thousands of dollars loss (with Long-Only Buy-Hold-Sell investing strategy) by simply concatenating a perturbed but semantically similar tweet.