Muscat securities market index (MSM30) prediction using Single Layer LInear Counterpropagation (SLLIC) neural network

The mankind has higher interest to find accurate tools for prediction especially at the financial sector. Many forecasting methods have been suggested through the history ranging from linear regression methods to nonlinear time series fitting approaches. Emerging countries market like security market in Sultanate of Oman has more importance in the international scenario according to its wide opportunities in investment sector. The MSM 30 index is used to control and benchmark the prices tendency for shares listed in Muscat security market. Predicting the future values of MSM 30 is very important for the investment sector in Oman. This research proposed the use of Single Layer LInear Counter propagation (SLLIC) neural network as a forecasting tool for MSM 30 index, and the resulted network are called SLLIC MSM 30 network. The performance of the SLLIC MSM 30 was tested and compared with other prediction models used with financial time series like linear regression and Radial Basis Function neural network. The test results shows that SLLIC MSM 30 network has good approximation and prediction capabilities for the MSM 30 index.