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