Abstract:
Data forecasting and analysing is a process which can be used for mak
ing
future predictions. Miscellaneous type of forecasting methodologies can be
seen in the literature. Generally, these traditional approaches have been
referring to use formal statistical methods for employing time series data
under the stationary and nor mality assumptions. However, most of these
traditional approaches have been shown the poor realistic under the high
volatility with non stationary conditions.
The main purpose of this study is to take an attempt to understand the
behavioral patterns and s eek to develop a new hybrid forecasting approach
for forecasting financial data under the high volatile fluctuations. The
results are implemented on Colombo stock exchange (CSE), Sri Lanka over
the six year period from June 2009 to November 2015.
The metho
dology of this study is running under the three main phases as
follows. In the first phase, stock market validations are identified using the
traditional time series approach namely autoregressive integrated moving
average (ARIMA). In the second part, vola tility patterns are identified using
Geometric Brownian Motion (GBM) algorithms. In the last stage, Artificial
Neural Network and GBM based proposed ANN GBM hybrid approach
was applied to predict the results.
According to the error analysis results, new p
roposed ARIMA GBM is
highly accurate (less than 10%) with lowest RMSE error values.
Furthermor e, the RMSE reveal that (RMSE[ARIMA]> RMSE [GBM]
>RMSE[ANN_ RMSE[ANN_GBM]), new proposed ANN_GBM
model is more significant and gives best solution for predict ing short term
predictions in high volatility fluctuations than traditional forecasting
approaches.
