Chiang Mai Journal of Science

In Malaysian’s economy there are many factors needed to be considered in order to maintain the economic growth. For instance, prices, interest rates, and employment level always react with each other in the form of nonlinear relationship. In a nonlinear system, the effect depends on the values of other inputs, thus the relationship is a higher-order function. Neural network (NN) is an approach that can cater nonlinear problems, and an implementation of an algorithm inspired by research into the brain. NN is a technology in which computer learns directly from data, thereby assisting in classification, function estimation, data compression, and similar tasks. In this paper, we introduce neural network model with an improved backpropagation error function for predicting profitability of selected firms at Kuala Lumpur Stock Exchange (KLSE). The results obtained are compared to the standard backpropagation model with mean square error function (MSE).