Online Portfolio Optimization with Exponential Gradient and Time Varying CAPM

João Daniel Madureira Yamim, Carlos Cristiano Hasenclever Borges, Yuri Resende Fonseca, Raul Fonseca Neto

Resumo


Since Harry Markowitz's seminal work in 1952, which initiated modern portfolio theory, portfolio allocation strategies have been intensely discussed in the literature. With the development of online optimization techniques, dynamic learning algorithms have proven to be an effective approach to build portfolios. The purpose of this paper was to implement a new version of the Exponential Gradient algorithm in which important information about the risk of stocks are considered in the algorithm's projection step. The portfolios built were compared with the Dow Jones Industrial Average Index (DJIA) and Best Constant Rebalanced Portfolio (BCRP). We used DJIA data from January 2000 to December 2017 with daily observations. The EG beta algorithm outperformed the DJIA in all tests performed, and it was very close to BCRP in periods of market upturn and was able to outperform it in downturns.

Palavras-chave


Exponential gradient; Portfolio optimization; Time varying CAPM

Texto completo:

PDF


DOI: http://dx.doi.org/10.21575/25254782rmetg2020vol5n21151

Apontamentos

  • Não há apontamentos.


Direitos autorais 2020 João Daniel Madureira Yamim

Revista Mundi Engenharia, Tecnologia e Gestão ISSN 2525-4782

Qualis: B4 - Interdisciplinar, B5 - Geografia, B5 - Administração Pública e de Empresas, Ciências Contábeis e Turismo, B5 - Comunicação e Informação, B5 - Engenharias III