Stock Price Forecasting: Geometric Brownian Motion and Monte Carlo Simulation Techniques

Abstract

This research paper aims to explore, compare and evaluate the predictive power of the Geometric Brownian Motion (GBM) and the Monte Carlo Simulation technique in forecasting the randomly selected 10 listed stocks in the SET50 of the Stock Exchange of Thailand (SET). The results shows that for the highest precision +/-0.5% of predicted 45 days return, the percentage of accuracy is at the highest of around 5% (or 500 times in 10,000 trials) for both GBM and Monte Carlo Simulation. It can be concluded that model accuracy in predicting end period returns is limited. Especially, predictive power of the models are declining towards the longer the evaluated timeframe. Comparing GBM and Monte Carlo Simulation in term of percentage of accuracy in predicting the end period returns, the two techniques are indifferent. For the predictive power of movements in prices, the GBM is a preferred technique. Besides, Monte Carlo Simulations yields a better accuracy especially in a longer period of evaluated timeframe. In conclusion, both techniques can predict stock prices within a highly accurate range. Thus, the techniques can be applied for stock price forecasting with limits mentioned.