Geometric Brownian Motion with Ito's lemma approach to evaluate market fluctuations: A case study on Colombo Stock Exchange

The data forecasting in economical time series provides a significant guidance for making decisions in the financial markets today. It can be widely applied for solving economic problems with uncertainty and instability patterns. Forecasting high volatile fluctuations with instability behavioral patterns make complicated problems in stock markets around the world today. The main focus of this study is to develop a forecasting model based on Geometric Brownian Motion approach for estimating share price indices in short-term investments in the Colombo Stock Exchange (CSE), Sri Lanka. Furthermore, traditional ARIMA approach was used to compare the predictions. The results reveal that, the new proposed model is more significant for investors in making their investment decisions wisely.