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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Return Predictability with Deep Learning in the Swiss Stock Market |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2020 |
| Abstract Text | I perform a comparative analysis of machine learning methods for measuring equity risk premiums in the Swiss stock market. The implemented methods are partial least squares (PLS), gradient boosted regression trees (GBRT), random forest (RF), deep neural networks (NN), and an equal-weighted ensemble of these models (ENS). Empirical ndings are promising. Machine learning methods surpass the linear benchmark model in terms of out-of-sample predictive R2. In addition, there are large economic gains|in the form of increased portfolio Sharpe ratios|to investors using machine learning forecasts. Furthermore, I identify the best-performing methods (ENS, NN, and PLS) and acknowledge the potential of machine learning for improving risk premium measurement. |
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