Machine learning and policy shocks: predicting stock market reactions to the 2025 US steel and aluminum tariffs
Cuesta Mathis, Santiago Arturo (A.A. 2024/2025) Machine learning and policy shocks: predicting stock market reactions to the 2025 US steel and aluminum tariffs. Tesi di Laurea in Computational finance, Luiss Guido Carli, relatore Nicola Borri, pp. 34. [Bachelor's Degree Thesis]
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Abstract/Index
Historical context: the 2018 US steel and aluminum tariffs. Economic and trade impact. Impact on businesses and industries. Later evaluations and overall assessment. Economic repercussions of the 2025 tariffs: a machine learning perspective. Reintroduction of steel and aluminum tariffs in 2025. Selection of companies. Initial data analysis. Statistical significance of event impacts. Panel regression model for predicting stock returns. Training set. Test set.
References
Bibliografia: pp. 32-33.
| Thesis Type: | Bachelor's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18) |
| Chair: | Computational finance |
| Thesis Supervisor: | Borri, Nicola |
| Academic Year: | 2024/2025 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 09 Dec 2025 11:15 |
| Last Modified: | 09 Dec 2025 11:15 |
| URI: | https://tesi.luiss.it/id/eprint/44300 |
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