Convergence of Actual and Predicted Share Prices a An ADALINE Neural Network Approach
Accurate forecasting of share prices is needed for fund managers and institutional investors for hedging decisions. Robust forecasting results will not only increase the effectiveness of hedging and reduce the hedging costs but also provide benchmarks for controlling and decision making. Existing traditional models for forecasting share prices rarely produce fair results. In this paper we have applied neural net work ADALINE approach to forecast the share prices listed in the Malaysian stock exchange. Adaptive linear neural net uses a moving window approach in updating its weights while training and this improves the accuracy of forecasting. We applied this technique on four share prices at four learning rates and the results nicely converge with the actual prices at higher learning rates. Our findings will increase the confidence in forecasting and will be helpful for stakeholders immensely.
