A multiobjective portfolio optimization for energy assets using D-Optimal design and mixture design of experiments

• Autor(a) principal: Leal,Gustavo dos Santos
• Data de Publicação: 2022
• Outros Autores: Romão,Estevão Luiz, Reis,Daniel Leal de Paula Esteves dos, Balestrassi,Pedro Paulo, Paiva,Anderson Paulo de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Production
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100225
• topic: Portfolio optimization; D-Optimal design; Mixture design; Multivariate analysis

Abstract Paper aims Frequently, researchers try to find a better way to allocate assets in order to have maximum return and low variability in a portfolio as diverse as possible. This paper aims to apply D-Optimal Design in the context of Mixture Design and portfolio optimization to efficiently select the runs of the proposed experimental design. Originality A new approach to find the optimal weights that maximize the returns and minimize the risk using D-Optimal Design was used. A multi-response optimization problem considering returns, variability and entropy as functions of the weights was proposed. However, as there is a significant correlation between the objective functions, a Factor Analysis combined with FMSE to dimensionality reduction was used. Research method All the steps for both stages of the methodology applied in this paper are presented below: select real time series; predict one step ahead; generate a D-Optimal mixture design; apply weights and generate mathematical models; solve the optimization problem. Main findings Using the desirability method, the optimal values were determined, obtaining approximately 79% for the compound desirability function. The proposed method presented a 16.80% higher return with a 4.98% higher risk exposure if compared against Naïve method. Implications for theory and practice The proposed methodology can be applied to any portfolio optimization study. Mixture Design studies have already been proposed for modeling portfolio optimization problems. However, the D-Optimal Design proved to be adequate, which represents less computational effort.