International Journal of Advanced Engineering and Technology

Authors
Olotu SI, Fasiku AI

Abstract
Investing in stock is a reliable way individuals and institutional investors make profit. In the stock market (or capital market) company stocks, bonds, and other securities are traded at an agreed price. At each trading activity in the market, prices of individual stock are normally quoted. Because the stock price fluctuates due to market forces, a sudden downward trend in stock price could yield to a huge loss of capital and discourage a potential investor. Therefore a good prediction of the stock market price is the key to successful trading. Out of several techniques that have been researched to predict the market price, the artificial neural network (ANN) technique gave a more efficient prediction because it allows deeper analysis of large set of data especially those that have the tendency to fluctuate within a short period of time. The First Bank of Nigeria PLC shares dataset is used as case study in this work. In developing the neural network system the research adopts the Multi Layer Feed Forward (MLFF) neural network model. The network is trained using Backpropagation algorithm. The research takes opening price, closing price, number of deals on stock, volume of stock traded and trading day of the week of First Bank of Nigeria PLC shares history as a neural network input. The output of the network model is the predicted price.