Simple ensemble-averaging model based on generalized regression neural network in financial forecasting problems

Introduces an ensemble-averaging model based on a GRNN (generalized regression neural network) for financial forecasting. The model trains all input individually using GRNNs and uses a simple ensemble-averaging committee machine to improve the accuracy performance. In a financial problem, there are many different factors that can effect the asset price movement at different times. An experiment is implemented in two different data sets, S&P 500 index and currency exchange rate. The predictive abilities of the model are evaluated on the basis of root mean squared error, standard deviation and percent direction correctness. The study shows a promising result of the model in both data sets.