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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Machine Learning Based Views in a Generalized Black-Litterman Framework |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 35 |
| Date | 2017 |
| Abstract Text | The proposed hybrid model supports non-Gaussianity of the returns in the reference model and incorporation of prior views from exogenous information which is quanti ed into a joint alpha signal using machine learning methods. The predictive power of the hybrid model is investi- gated using Quantopian backtest engine. The introduction of subjective views derived from alternative datasets combined with COMFORT as the reference model leads to a faster adapt- ing model, which has extraordinary and steady risk monitoring properties, captures trends and leads to a higher Sharpe ratio. Two of our best model and their combination deliver net portfolio returns which are systematically higher than the returns of the S&P 500 index. |
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