Track: Modeling and Simulation

Abstract

This research examined the potential of the Geometric Brownian Motion (GBM) method as an accurate and effective forecasting method compared to the Artificial Neural Network (ANN) method.  The number of days the volatility and drift are moved were also determined and this was used to perform the forecast of stock prices of holding companies registered with the Philippine Stock Exchange and also compared to the ANN method.  The results showed that the average perentage error of the GBM method was 6.21% or an accuracy of 93.79% while the ANN method generated an average percentage error of 8.83% for the three-year period or an accuracy of 91.17%.  This manifests that using the GBM method in forecasting stock prices of these sample holding companies yielded a better accuracy than that of using the ANN method.  Hence, it also indicates that the GBM method is more effective than the ANN method in forecasting stock prices of these sample holding companies. This further boosts the potential of the GBM method as a good forecasting method in determining future stock prices of holding companies.  In addition to this, the GBM method provides potential investors to determine which investment activities to pursue.