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

TL;DR

This paper develops an optimized portfolio model for Iran's stock market using a neural network to predict stock returns and a genetic algorithm to design portfolios with varying risk levels, based on the Markowitz framework.

Contribution

It introduces a combined neural network and genetic algorithm approach for portfolio optimization tailored to Iran's stock market, incorporating risk prediction and weight constraints.

Findings

Successfully predicted stock returns using neural networks.

Designed eight portfolios with different risk profiles.

Demonstrated the effectiveness of the approach on real market data.

Abstract

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.