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http://hdl.handle.net/2445/182314
Title: | Hierarchical Portfolio Optimization |
Author: | De Lio Pérego, Francisco |
Director/Tutor: | González Alastrué, José Antonio |
Keywords: | Anàlisi de conglomerats Anàlisi de sèries temporals Índexs borsaris Treballs de fi de grau Cluster analysis Time-series analysis Stock price indexes Bachelor's theses |
Issue Date: | 2021 |
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. |
Note: | 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 |
URI: | http://hdl.handle.net/2445/182314 |
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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