The comparative analyses of the nonparametric methods for investment return prediction

Abstract

The financial market is complex, evolving and dynamic system, which has an extremely non-linear movement. Thus, investment return prediction represents a significant challenge, especially because of its great diversity, unsteadiness and unstructured data with a high degree of instability and pronounced hidden connections. It is known that accurate prediction of the stock market indexes is very important for the development of effective trading strategies in investments. The main objective of the research is to perform the comparative analyses of different nonparametric methods, that is, fuzzy artificial neural networks (fuzzyANN) and genetic algorithm artificial neural networks (GAANN) for predicting the movements of the stock market indexes. The survey is conducted on the BELEX15, SBITOP, BUX and CROBEX stock market indexes. Model estimates were carried out through the prediction error MAE, MAPE and RMSE. The research results point to the adequacy of the nonparametric methods application in investments.