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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Deep No-Arbitrage Asset Pricing |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2020 |
| Abstract Text | We replicate the approach of Chen, Pelger, and Zhu (2019) and apply a generalized non-linear asset pricing model to a large set of U.S. equity and macroeconomic data. We con rm a major part of their results for an extended time period and reduce the portfolio turnover of the model. Furthermore, we leverage a subset of their neural network architecture and propose an idea for an alternative approach to traditional portfolio optimization, which does not rely on estimated mean returns and covariance matrices as input parameters. We compare both models in detail. Based on the obtained results, our model cannot compete with the original approach in terms of realized Sharpe ratios, however it does exhibit a signi cantly lower turnover ratio and higher explained variation. I |
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