The Performance of the Investment Return Prediction Models: Theory and Evidence

Abstract

The market structure has been adjusted in order to be as simple as possible in sense of its economic components. The aim of the investment return prediction is constructing as good models of the market movement as possible. As for as the stock market is concerned, the price rise of some stocks indicate the good results of the management of that company, while the price fall shows the inadequate management. Prompt and accurate information of the market movement enable the managers to take some measures which lead to optimal investment decision. The Autoregressive Moving Average (ARIMA) model is one of the most frequently linear models of the time series used for the investment return prediction. The prediction researches in the last years from the areas of Artificial Neural Networks (ANNs) indicate that ANNs with a combination of other prediction models give better prediction results. This research aim is to introduce a hybrid model ARIMA fuzzy-neural network for the prediction of the stock market index BELEX15 values. The research results indicate that the linear model ARIMA and fuzzy ANNs exhibit more superior investment return prediction performances.