Portfolio Optimization By Using MeanSharp-βVaR and Multi Objective MeanSharp-βVaR Models

Shokoofeh Banihashemi, Sarah Navidi


The purpose of this paper is portfolio optimization which consists of stock screening, stock selection and capital allocation. In the first step, the stock companies screening by their financial data. For second step, we need some inputs and outputs for solving Data Envelopment Analysis (DEA) models. Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-VaR (MShV) model and the Multi Objective MeanSharp-VaR (MOMShV) model based on Range Directional Measure (RDM) that can take positive and negative values. Also, we consider one of downside risk measures namely Value at Risk (VaR) and try to decrease it. After using our proposed models, the efficient stock companies will select for making the portfolio. In the third step, Multi Objective Decision Making (MODM) model was used to specify the capital allocation to the stock companies that selected for the portfolio. Finally, a numerical example of the purposed method in Iranian stock companies is presented.

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