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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Supervised Learning for Financial Market Predictions |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 71 |
| Date | 2018 |
| Abstract Text | This thesis proposes a general framework combining portfolio optimization techniques with supervised learning. The ultimate goal is to outline an active portfolio management scheme that is to a large extent automated. The predic-tive models use time series of historical asset returns as the features and future returns as the targets. The predictions are in turn used as inputs to a portfolio optimization problem that leads to a portfolio allocation strategy. Together with detailed model description we also test the methods on a recent financial data set and analyze the feasibility of the entire framework. The main emphasis is put on comparison to benchmarks and performance assessment both in terms of prediction quality and risk and return profile of the strategy. |
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