Otimização do risco retorno de um portfolio de ações utilizando a programação binária
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Universidade Federal do Amazonas
Resumo
Building an investment portfolio is a complex task where countless variables not exclusively
linked to the assets themselves must be taken into account. The objective, however, is always
to obtain the maximum return by exposing yourself to the lowest possible risk. The literature
points out several methodologies for obtaining an optimal portfolio, involving programming
logic and mathematical modeling, however, many fails to verify the real effectiveness of
methodologies in the real world, in addition to too much complexity, remaining only in the
theoretical-academic part. In this work it was proposed the composition of a portfolio in a
similar way to the backpack problem using binary programming and the composition of the
efficient frontier by means of non-linear programming to verify its effectiveness in comparison
to a portfolio of 10 (ten) shares of a website investment. The programming variables were based
on Markowitz's Theory of Portfolio Selection and the following contributors to their studies.
From the portfolio chosen via binary programming, the efficient frontier was elaborated by
means of non-linear programming to analyze the performance of the investment site portfolio
during 30 days. Due to the complexity of the calculations, the Solver tool of the Microsoft Excel
program was used to carry out the programming both to obtain the portfolio of 10 (ten) shares
in the portfolio and to obtain the optimal portfolios that made up the efficient frontier. The
portfolio obtained exceeded the percentage performance obtained from the investment website
in the same period when considering the maximum possible return, the minimum global
variance and also in the naive distribution.
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BEVILAQUA, Nicolas Sampaio. Otimização do risco retorno de um portfolio de ações utilizando a
programação binária. 2021 81 f. Dissertação (Mestrado em Engenharia de Produção) - Universidade Federal do Amazonas, Manaus (AM), 2021.
