Publicação
Asset allocation using machine learning
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Lameira, Pedro | |
| dc.contributor.author | Nimtz, Julius | |
| dc.date.accessioned | 2019-06-25T11:15:03Z | |
| dc.date.available | 2022-06-30T00:30:36Z | |
| dc.date.issued | 2019-01-23 | |
| dc.description.abstract | This paper,Asset Allocation using Machine Learning, proposes a two step model, forecasting rst volatility through an GJR-GARCH model and using a Support Vec- tor machine to do the investment decision between the market portfolio and a risk parity portfolio. Besides the volatility forecast, the Support Vector Machine is based on economic, price, fundamental and sentiment data. It manages to outper- form both the market (S&P 500) and a risk parity portfolio in terms of returns and risk adjusted returns. | pt_PT |
| dc.identifier.tid | 202225917 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/73608 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Support vector machine | pt_PT |
| dc.subject | Asset allocation | pt_PT |
| dc.subject | Risk parity | pt_PT |
| dc.subject | Volatility forecast | pt_PT |
| dc.title | Asset allocation using machine learning | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics | pt_PT |