Application of a Modified Generalized Regression Neural Networks Algorithm in Economics and Finance

Abstract:     
In this paper we propose an alternative and modified Generalized Regression Neural Networks Autoregressive model (GRNN-AR) in S&P 500 and FTSE 100 index returns, as also in Gross domestic product growth rate of Italy, USA and UK. We compare the forecasts with Generalized Autoregressive conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models. The results indicate that GRNN outperform significant the conventional econometric models and can be an efficient alternative tool for forecasting. The MATLAB algorithm we propose is provided in appendix for further applications, suggestions, modifications and improvements.

Keywords: Autoregressive Moving Average, Forecasting, GARCH, Generalized Regression Neural Networks, MATLAB, Stock Returns

JEL Classification: C23,C45, C53, C63

Accepted Paper Series