PREDICTION OF STOCK VALUES IN STOCK MARKET USING ARTIFICIAL NEURAL NETWORK: A REVIEW

Abstract

This paper proposes a novel constraint bagging forecasting method for stock price prediction. In the proposed approach, each of predictors is firstly constructed by training on a set of samples produced by bootstrapping using neural networks. The goal of this project is thus to experiment with ANNs and to evaluate performance of ANN models in studying stock price patterns in time by attempting to predict future results of a time-series by simply studying patterns in the time series of stock prices. In this paper, two kinds of neural networks, a feed forward multilayer Perceptron (MLP) and an Elman recurrent network, are used to predict a company’s stock value based on its stock share value history. The experimental results show that the application of MLP neural network is more promising in predicting stock value changes rather than Elman recurrent network and linear regression method. However, based on the standard measures that will be presented in the paper we find that the Elman recurrent network and linear regression can predict the direction of the changes of the stock value better than the MLP.