GEOMETRIC BROWNIAN MOTION BASED NEW HYBRID STATISTICAL APPROACH FOR STOCK MARKET FORECASTING

Abstract

Capital Investments in the stock market is the easiest and fastest way of building the healthy financial foundation for future life. In the past few decades, stock markets around the world have become more institutionalized and advanced as the main forms of investments for making profit investments in numerous organizations as well as individuals to arrange their large investment funds to the general public. As a result, the stock market prediction has become one of the great challenges caused by its complexity and eruptive nature. Generally, stock prices are chaotic and show both linear and nonlinear behaviors. Therefore, the accuracy of the forecast might be enhanced by modeling the non-linear behaviors of the series as well. The main purpose of this study is to take an attempt to understand the behavioral patterns as well as seek to develop a new hybrid forecasting approach based on Geometric Brownian Motion (GBM) for estimating price indices in Colombo Stock Exchange (CSE), Sri Lanka. Indeed, the Autoregressive integrated moving average (ARIMA) approach is used as a comparison mode. The current study was carried out on the basis of CSE daily trading data from January 2010 to May 2018 were extracted and tabulated for calculations. Because of the nonlinear behavioral patterns in the CSE, the mean absolute percentage error analysis results suggested that new proposed hybrid model (HGBM) is highly accurate than traditional ARIMA (HGBM (0.521%) < ARIMA (7.18%)) for forecasting one day ahead predictions. Furthermore, the results reveal that, the new proposed model is more significant for investors to make their investment decisions precisely