Using different learning algorithms in the stock price prediction by using neural networks

Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerableattention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity,dynamics and turbulence, thus the implicit relationship between the stock price and predictors is quite dynamic. Hence, it isdifficult to tackle the stock price prediction problems effectively by using only single soft computing technique.In this research, in the first step, the possibility of predicting stock price of National Iranian Copper Industries Company wasstudied. Then, for predicting of stock price after one day neural network of MLP by learning algorithm of Levenberg-Marquardt were used. Then optimize structure of neural network was trained with the standard BP algorithm, the learningrate is 3/0 has the best performance. And for this learning rate, sensitive of standard BP algorithm was calculated to minimizelocal. At the end, standard BP algorithm with momentum is used. The results showed that predicting by standards BPalgorithm with momentum is better than the standard BP algorithm.