Modeling Stock Market Exchange Prices Using Artificial Neural Network: A Study of Amman Stock Exchange

Stock market represents an essential part of the economy in the Middle East, it is significant for shareholders and investors to estimate the stock price and select the best trading opportunity accurately in advance. This paper utilizes artificial neural network in the modeling of stock market exchange prices. The network was trained using supervised learning. Simulation was conducted for seven case study companies from Amman Stock Exchange representing both the service and manufacturing sectors. The case study companies were selected from different categories varies according to the degree of stock market stability. The model was evaluated by stock market brokers through the use of a questionnaire that was distributed in Amman Stock Exchange, the majority of the participants found the results acceptable. The use of ANN provides fast convergence; high precision, and strong forecasting ability for real stock prices which it turn will bring high return and reduce potential loss to stock brokers.