Portfolio optimization with Python
Motivation: The goal of a rational investor is to maximize portfolio return and minimize portfolio risk. Methodology: Markowitz portfolio optimization theory is used to obtain by the Python programming language the optimal portfolio on the efficient frontier. This optimal portfolio corresponds to th...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | México |
| Institución: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/6807 |
| Acceso en línea: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/6807 |
| Access Level: | acceso abierto |
| Palabra clave: | Markowitz theory optimal portfolio efficient frontier Python Jupyter Notebook Teoría de Markowitz portafolio óptimo frontera eficiente |
| Sumario: | Motivation: The goal of a rational investor is to maximize portfolio return and minimize portfolio risk. Methodology: Markowitz portfolio optimization theory is used to obtain by the Python programming language the optimal portfolio on the efficient frontier. This optimal portfolio corresponds to the point with the highest Sharpe ratio. To generate the efficient frontier with the feasible portfolios, multiple investment portfolios are simulated by implementing a code in Python with Jupyter Notebook. Results: The optimal composition of a portfolio constructed with Mexico Government Bonds, ETF’s, and shares of Wal-Mart Inc’s Mexico, is determined by a Sharpe ratio of 0.85; the portfolio has an expected return of 9.53% with a volatility of 6.55%. |
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