Abstract:
The high volatile fluctuations with instability patterns are common phenomenon in the
Colombo Stock Exchange (CSE), Sri Lanka. In the CSE context, very few studies have been
focused and attempted to find out the new methodologies for forecasting stock price
indices under the high volatility. The purpose of this study is to propose a new hybrid
forecasting approach based on Geometric Brownian Motion to forecast stock market data
under the unstable volatility.
The model selection criterion results of Akaike information criterion and Schwarz
criterion suggested that, ARIMA (4, 1, 3) and ARIMA (1, 1, 1) approaches are suitable for
predicting ASPI and SL20 price indices during the time period between 2010 January to
2016 December. Furthermore, the model accuracy testing results of mean absolute
percentage error (MAPE) (GBM-ANN(0.024)<ARIMA (4,1,3)(0.124)) and Mean absolute
deviation (MAD) (GBM-ANN(0.324)<ARIMA (4,1,3)(1.251)), suggested that new proposed
GBM-ANN hybrid approach is the most suitable for forecasting price indices under the
high volatility than traditional forecasting mechanisms.
