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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Statistical Learning and Testing for Optimal Portfolio Strategy Choice |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 43 |
| Date | 2021 |
| Abstract Text | In this thesis, we systematically implemented the pretest-based approach proposed by Kazak and Pohlmeier (2019a) and applied it to do parameter selection for momentum strategies. We first showed the necessity and difficulty of optimal strategy selection via naive examples and then use the pretest-based method in Kazak and Pohlmeier (2019a) to choose optimal strategy. To verify the effectiveness of the proposed method, especially in terms of out-of-sample performance for momentum strategy, we conducted extensive experiment with various settings. Results showed that although the meta strategy selected by the statistical learning and testing approach performs worse than the benchmark occasionally, for most of the time it can outperform the benchmark strategy, indicating that the algorithm can successfully identify and choose the winning strategy. In terms of the failure cases, we hypothesis that this is due to market regime change, which is not predictable from the momentum information only. |
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