zakiyeh nikdel

Prediction and analysis of stock market movements are an important topic for researchers, traders and have got an important role in today’s economy. Variety in policies, such as government policies and economic policies affect the stock market and cause stock price changes. The predicting stock price movement on a daily basis due to the non-linear and chaotic stock price movements is a difficult task. There are several ways for predicting in stock market. Artificial intelligence techniques have been widely used to predict data with nonlinear and chaotic structure. One of these techniques is neural network. If neural network is trained correctly, then it has minimum error in predicting. In this research, we will train the multi layer perceptron neural network with 8 meta heuristics algorithms and we predict Tehran Exchange Dividend Price Index (TEDPIX). The Results show that grey wolf optimization has the minimum error in training of neural network.