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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Statistical Learning for Trend-Following and Momentum Strategies |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 35 |
| Date | 2022 |
| Abstract Text | Although momentum strategies are widely used and discussed in the finance literature, the issue of look-back period selection is often put on the back burner. Based on the idea of Levy and Lopes (2021), various methodologies will be used in order to dynamically choose the most effective look-back period. Compared to the above-mentioned paper, different statistically based strategies will be analysed and the case of cross-sectional strategies will also be explored. The results say that more or less complex statistical models lead to better performance for both the time-series and cross-sectional approaches. |
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