The Black Tuesday Attack: how to crash the stock market with adversarial examples to financial forecasting models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914107557412864 |
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| author | Hofweber, Thomas Bergl, Jefrey Reyes, Ian Sadovnik, Amir |
| author_facet | Hofweber, Thomas Bergl, Jefrey Reyes, Ian Sadovnik, Amir |
| contents | We investigate and defend the possibility of causing a stock market crash via small manipulations of individual stock values that together realize an adversarial example to financial forecasting models, causing these models to make the self-fulfilling prediction of a crash. Such a crash triggered by an adversarial example would likely be hard to detect, since the model's predictions would be accurate and the interventions that would cause it are minor. This possibility is a major risk to financial stability and an opportunity for hostile actors to cause great economic damage to an adversary. This threat also exists against individual stocks and the corresponding valuation of individual companies. We outline how such an attack might proceed, what its theoretical basis is, how it can be directed towards a whole economy or an individual company, and how one might defend against it. We conclude that this threat is vastly underappreciated and requires urgent research on how to defend against it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18990 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Black Tuesday Attack: how to crash the stock market with adversarial examples to financial forecasting models Hofweber, Thomas Bergl, Jefrey Reyes, Ian Sadovnik, Amir Cryptography and Security Computational Engineering, Finance, and Science We investigate and defend the possibility of causing a stock market crash via small manipulations of individual stock values that together realize an adversarial example to financial forecasting models, causing these models to make the self-fulfilling prediction of a crash. Such a crash triggered by an adversarial example would likely be hard to detect, since the model's predictions would be accurate and the interventions that would cause it are minor. This possibility is a major risk to financial stability and an opportunity for hostile actors to cause great economic damage to an adversary. This threat also exists against individual stocks and the corresponding valuation of individual companies. We outline how such an attack might proceed, what its theoretical basis is, how it can be directed towards a whole economy or an individual company, and how one might defend against it. We conclude that this threat is vastly underappreciated and requires urgent research on how to defend against it. |
| title | The Black Tuesday Attack: how to crash the stock market with adversarial examples to financial forecasting models |
| topic | Cryptography and Security Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2510.18990 |