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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Towards Deep Sector Rotation |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2022 |
| Abstract Text | This thesis analyses stock return predictability on a sector level using machine learning techniques. The two main goals are investigating whether returns exhibit any predictability and using predictive models to construct simple sorted portfolios that rotate between the sectors. Specifically we are interested in seeing whether non-linear deep learning techniques can improve the performance of standard linear models. For this purpose, US sector aggregated data is used, for the period 1990-2021. We first show the potential added value of machine learning models in a regime switching simulation study. Next, for the market data, state-of-the-art RNN and CNN models are able to achieve slightly positive out-sample R2 values. Moreover, portfolios built based on these predictions are able to outperform benchmark portfolios on cumulative returns and Sharpe ratio. Given that similar predictive power is found in the European market, this research supports the conclusion that returns exhibit slight predictability. |
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