Applying Optimized Mathematical Algorithms to Forecast Stock Price Average Accredited Banks in Tehran Stock Exchange and Iran Fara Bourse

Subject Areas : Financial Engineering

Negar Aghaeefar ¹ , Mohammad Ebrahim Mohammad Pourzarandi ² , Mohammad Ali Afshar Kazemi ³ , Mehrzad Minoie ⁴

1 - Faculty of Management, Islamic Azad University, Central Branch, Tehran, Iran.
2 - Faculty of Management, Islamic Azad University, Central Branch, Tehran, Iran.
3 - Faculty of Management, Islamic Azad University, Central Branch, Tehran, Iran.
4 - Faculty of Management, Islamic Azad University, Central Branch, Tehran, Iran.

Received: 2018-12-09 Accepted : 2019-02-19 Published : 2019-07-01

Keywords: Industry average, Optimization algorithm, Fuzzy time series, Golden Ratio algorithm, Forecasting stock price,

Abstract :

The effective role of capital in every country flows through giving guidelines for capital and resources, generalizing companies and sharing development projects with public, and also adding accredited companies stock market requires appropriate decision making for shareholders and investors who are willing to buy shares based on price mechanism. Forecasting stock price has always been a challenging task, since it is affected by many economic and non-economic factors and variables; therefore, selecting the best and the most efficient forecasting model is tough and essential. Up to now applying weighted mean called weighted mean price has been used to forecast industry average price for companies in the stock market and investors were forecasting based on this method. First we have identified 10 accredited banks in TSE and 10 banks in Iran Fara Bourse. In this article, by applying one of the mathematical optimizing techniques, industry means got calculated based on optimized parameters and compared with the industry average; in this statement we strived to find another variable that could forecast with less deviation. In the following study, by calculating frequency level of deviations, average for price forecasting in banking industry during five years is examined. Finally, the research suggests that, instead of using mean of industry average, it is better to use mean average of golden number, which will lead us to more accurate results.

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