Abstract
In recent years, steel manufacturers have been playing a great role in economic growth, bringing about large amounts of stock exchange transactions in this industry. In the current study, we try to design a model to forecast stock price of steel industry, using artificial neural networks. To design the model, we used a three-layer network (five neurons in input layer, twelve neurons in the middle layer and one neuron in output layer), a sigmoid transfer function, 7% Alpha, 2% Etta and Windows Neural Network (WNN) software. The input variables of the network include net assets, P/E ratio, dividend per share (DPS), earning per share (EPS), amount of stock transactions, and stock price network output of companies being studied. The results from designed model show that if an artificial neural network is taught correctly, it can recognize the relationship between variables and it can help to forecast the stock price of steel industry with minimum error (35% in this research). Investors can forecast the stock price of steel manufacturing companies using these inputs variables and WNN software. Key words: Forecast, artificial neural networks, stock price forecasting, Tehran stock exchange.  
Highlights
The future belongs to those who do their best to plan for it
The results from designed model show that if an artificial neural network is taught correctly, it can recognize the relationship between variables and it can help to forecast the stock price of steel industry with minimum error (35% in this research)
To select the companies being studied, first main metals manufacturer companies in Tehran Stock Exchange are selected in which the most transactions in last years have been done on them and at the step the biggest steel manufacturer companies in the country and middle east are considered to forecast their stock price
Summary
The future belongs to those who do their best to plan for it. A successful business-person, investor, institute or organization should have the necessary plans by forecasting the future situation. Because of the importance of forecasting, different fields of human sciences try to work on this subject, like forecasting atmosphere situation, economic situation of communities, the earnings and expenditures of an institution, a country’s budget and, in this research, the forecast of steel industries’ stock price. National economies are influenced by stock performance. Stock is assumed as an investing accessible instrument for both master investors and the public. Stock is influenced by macroeconomic parameters and by lots of other factors; unknown factors and a great number of them results in lack of trust in investment
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