Stock Price Forecasting using Back Propagation Neural Networks with Time and Profit Based Adjusted Weight Factors

In this paper, we showed a method to forecast the stock price using neural networks. Predicting the stock market is very difficult since it depends on several known and unknown factors. In recent years, one of the techniques that have been used popularly in this area is artificial neural network. The power of neural network is its ability to model a nonlinear process without a priori knowledge about the nature of the process. We used both feed forward neural network and simple recurrent neural network, trained by time and profit based back propagation algorithm with early stopping to make the prediction. The integration of profit and time factors with training procedure made an improvement in forecasted results for feed forward neural network. Moreover, the simple recurrent neural network with its ´time capture´ capabilities had better forecasted results than feed forward neural network in all experiments