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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | An improved feature screening technique for asset selection in the U.S. market |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 34 |
| Date | 2021 |
| Abstract Text | The purpose of this master’s thesis is to assess the profitability in the U.S. market of a newly-proposed asset selection technique, which is suitable in a high-dimensional con-text, i.e., when the number of assets is at least equal to the total number of observations. We focus on the out-of-sample portfolio performances, showing that the approach can potentially deliver good returns, but is unable to deal with bad market phases, when volatility increases. To overcome this limitation, we propose a risk-managed version of the asset selection technique that delivers much larger returns over the time frame ana-lyzed and overwhelms the performance of the commonly employed momentum strategy, even accounting for transaction costs. |
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