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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Stock Market Prediction: When Freely Available Data Meets Machine Learning |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | This thesis aims to bring finance and machine learning together and examines whether a model can be created that is used to evaluate if the US stock market can be outperformed using solely freely available data excluding paid financial services of data providers entirely. Said examination is done for two time dimensions yearly and quarterly with a focus on both the complete market and a sector-based approach. Different machine learning techniques such as logistic regression, random forest, gradient boosting, and support vector machine are being used and applied for the task at hand. Although the results are less than ideal, the model performance may improve over time when more data is added. However, the sector-based results are more promising due to some sectors scoring higher in the examined model performance metrics. II |
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