Forecasting Stock Market Prices using Geometric Brownian Motion by Applying the Optimal Volatility Measurement

Abstract

Investing in the Malaysian stock market can be overwhelming due to the abundance of possibilities, necessitating informed decision-making to navigate the volatile market. This study addresses a common problem faced by investors venturing into the stock market, where instability and fluctuations pose significant risks, leading to financial losses stemming from inadequate knowledge about suitable stocks for investment. Unlike many studies that focus on long-term forecasting methods, this research adopts the Geometric Brownian Motion (GBM) model for short-term investment analysis. The study aims to identify the most effective volatility measurement model, develop a forecasting model using GBM based on the chosen volatility model, and evaluate the accuracy of the GBM model using Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD). Four volatility models, which include simple, log, high-low, and high-low-closed volatility are analysed to determine the most effective volatility measurement model. Four months of daily stock data are collected to ensure accuracy excluding factors such as seasonality, politics, natural disasters, and wars. Findings indicate that the simple volatility model is the most suitable for forecasting stock market trends using the GBM model, demonstrating high accuracy based on MSE, MAPE and MAD.