Forecasting Stock Price Trend by Artificial Neural Networks (Case Study: Isfahan Oil Refinery Company)

Artificial neural networks (ANN) are mathematical models inspired by human’s neural and brain system. This research deals with the next day price forecasting in Tehran’s stock market by MLP, and attempts, by various methods, to reduce the prediction error. High pricing of stocks may lead to low demand for negotiable stocks and the failure of privatization. Raising various doubts in the negotiation of public properties, low pricing results in the long-term failure of negotiation policies. With respect to the importance of this issue, the newness of stock market and the lack of financing institutes and investment banks in Iran, prediction of stock price trend and its ascending and descending order can influence the decisions and strategies of managers. Various variables affect stock prices among which the role of economic indices, such as exchange rate / oil price and gold price is significant.
The purpose of the present study is to predict the final prices of stocks by utilizing daily data through neural networks. The results indicate that the ANN model has low error and high explanatory and thus considerable forecasting power.