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Optimal capital allocation between different assets is an important financialproblem, which is generally framed as the portfolio optimization problem.General models include the single-period and multi-period cases. Thetraditional Mean-Variance model introduced by Harry Markowitz has been thebasis of many models used to solve the portfolio optimization problem. Theoverall goal is to achieve the highest return and lowest risk in portfoliooptimization problems. In this paper, we will present an optimal portfoliobased the Markowitz Mean-Variance-Skewness with weight constraints model forshort-term investment opportunities in Iran's stock market. We will use aneural network based predictor to predict the stock returns and measure therisk of stocks based on the prediction errors in the neural network. We willperform a series of experiments on our portfolio optimization model with thereal data from Iran's stock market indices including Bank, Insurance,Investment, Petroleum Products and Chemicals indices. Finally, 8 differentportfolios with low, medium and high risks for different type of investors(risk-averse or risk taker) using genetic algorithm will be designed andanalyzed.

Designing an Optimal Portfolio for Iran's Stock Market with Genetic Algorithm using Neural Network Prediction of Risk and Return Stocks