A Feed Forward Artificial Neural Nework for the Stock Market Forecasting Using Conventional Forecasts as Input Variables

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In this study, First, Milanka Price Index (MPI) of the Colombo Stock Exchange (CSE) was forecast using Linear Moving average (LMA), Simple Exponential Smoothing (SES) and Adaptive Response Religh's Single exponential Smoothing (ARRSES). Then, a Feed - Forward Artificial Neural Network (FFANN) approach was developed where the inputs of the neural network are the forecasted values from conventional forecasting techniques. Mean Absolute Percentage Error (MAPE) and Prediction Error Variance (PEV) were employed to measure the performance of LMA, SES, ARRESES and the proposed FFANN method. Finally, the results of the conventional approaches have been compared with that of the proposed FFANN approach.

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