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http://hdl.handle.net/2445/182314
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DC Field | Value | Language |
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dc.contributor.advisor | González Alastrué, José Antonio | - |
dc.contributor.author | De Lio Pérego, Francisco | - |
dc.date.accessioned | 2022-01-13T08:54:58Z | - |
dc.date.available | 2022-01-13T08:54:58Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://hdl.handle.net/2445/182314 | - |
dc.description | Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2020-2021, Tutor: José Antonio González Alaustré | ca |
dc.description.abstract | The field of Portfolio Optimization has historically had a very hard time as the Mathematical Models at its availability are based on certain assumptions one can not afford to make in the financial markets, making naive approaches all-too entic-ing. In this project we have introduced the assumption that the different stocks in the financial markets have a hierarchical structure and have allowed ourselves to be inspired by it to build portfolios through a Machine Learning approach. We have employed the Hierarchical Risk Parity algorithm and tested minor variations relat-ing to the dissimilarity measure it makes use of. The tests were conducted with historical daily closing price data from 2014 to 2020 for 440 stocks in the S&P 500 index. Results suggest most of the tested Hierarchical Risk Parity variants are ro-bust and can compete with the Equal Weights Portfolio. We mainly encourage the use of two dissimilarity measures, the standard one, a correlation based metric and Dynamic Time Warping. The former is suggested to the pessimistic investor while the latter to the hopeful yet conservative investor. To optimistic investors with a high risk tolerance the recommendation would be to use the traditional Equal Weights portfolio among the asset allocation methods considered in this project. | ca |
dc.format.extent | 60 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.rights | cc-by-nc-nd (c) De Lio Pérego, 2021 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.source | Treballs Finals de Grau (TFG) - Estadística UB-UPC | - |
dc.subject.classification | Anàlisi de conglomerats | cat |
dc.subject.classification | Anàlisi de sèries temporals | cat |
dc.subject.classification | Índexs borsaris | cat |
dc.subject.classification | Treballs de fi de grau | - |
dc.subject.other | Cluster analysis | eng |
dc.subject.other | Time-series analysis | eng |
dc.subject.other | Stock price indexes | eng |
dc.subject.other | Bachelor's theses | eng |
dc.title | Hierarchical Portfolio Optimization | ca |
dc.type | info:eu-repo/semantics/bachelorThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Treballs Finals de Grau (TFG) - Estadística UB-UPC |
Files in This Item:
File | Description | Size | Format | |
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TFG-EST Francisco De Lio.pdf | 2.26 MB | Adobe PDF | View/Open |
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