A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction
Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong, Sijia Liu, Oluwasanmi Koyejo
Abstract
More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather information and predict movements stock prices. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability given necessary constraints 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.
BibTeX
@inproceedings{xie-etal-2022-word,
title = "A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction",
author = "Xie, Yong and
Wang, Dakuo and
Chen, Pin-Yu and
Xiong, Jinjun and
Liu, Sijia and
Koyejo, Oluwasanmi",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.43/",
doi = "10.18653/v1/2022.naacl-main.43",
pages = "587--599"
}