Selected Malaysia stock predictions using artificial neural network

Abstract

Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. In this study, the feedforward backpropagation neural network with Levenberg-Marquardt training algorithm is used. Selected Malaysian stocks, namely Maybank and Tenaga, were modeled and simulated for trading using four trading strategies. The results show that ANN provide a highly accurate model for the stocks also realises profitable systems using all four trading strategies.