PETRONAS’s Stock Price Forecasting: An Application of Neural Network Model
Keywords:Neural Network, ARIMA, Error Magnitude, Directional Accuracy
Many researches have been carried out to investigate the influences of oil prices on stock market by using oil and gas as the indicator. The study of the stock
market is necessary since it gives a better insight for the future economic growth in the country. However, stock price forecasting somewhat can be pretty challenging at some circumstances due to the existing nature between the relationship of the nonlinearity and unpredictability in financial market. Since neural network have been proven reliable in many studies to overcome the non-linearity relationships, this study focuses on PETRONAS’s stock prices and decided to come out with ANN model to forecast the stock prices against ARIMA (3,1,2) as the benchmark model. Besides, the common error magnitude in most forecast evaluations have been ignoring the importance to evaluate the directional accuracy as well. Therefore, this study had also considered the directional movement to measure the forecast accuracy and the result indicated that both models successfully predicted the directional movements with their MAPE values were below than 5% margin of error.