Comparison of Risk Factors for Investing in Tehran Stock Exchange Using Smart Neural Network (Forecasting Tehran Stock Exchange with Neural Networks)

Document Type : Original Article

Authors

1 Department of accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran

2 Accounting Department of Sistan and Baluchestan University, Zahedan,

3 Department of Accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran

Abstract

In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. Risk managers need to decide when to leave a portfolio unhedged to generate profit and when to hedge in order to control downside risk. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. While science and technology parks and towns and development centers have been established before 2002 in Iran and their number has been continually increased, there has been only a seminar presented by Tehran Securities and Exchange Organization, in Agricultural Bank, 2001, about venture capital in the country. Gradually, venture capital has been presented as a new mechanism for financing to entrepreneurs and a bill has been represented to the cabinet by Management and Planning Organization to define an annual definite budgetary in a general manner. The industry of venture capital has been under the attention of many organizations and science centers during recent years. The Institute of Elites’ technological development, on behalf of Centre for Innovation and Technology Cooperation, Presidency of the Islamic Republic of Iran, Management and Planning Organization of Iran, Industrial Development and Renovation Organization of Iran, Centre for New Industries of Iran. In this paper, we extend the field of expert systems, forecasting, and model by applying an Artificial Neural Network. ANN model is applied to forecast market volatility. The results show an overall improvement in forecasting using the neural network is compared to the linear regression method.

Keywords