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

Eleftherios Giovanis

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

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References


R. D. Aryal R.D. and W. Yao-Wu, W. “Neural Network Forecasting of the Production Level of Chinese Construction Industry,” Journal of comparative international management, vol. 29, pp. 319-33, 2003

N.R. Swanson and H. White, “A model selection approach to real time macroeconomic forecasting using linear models and artificial neural networks”, Review of Economics and Statistics, vol. 79, pp. 540-50, 1997

N.R. Swanson and H. White, “Forecasting economic time series using adaptive versus non-adaptive and linear versus nonlinear econometric models”, International Journal of Forecasting, vol. 13, pp. 439-61, 1997

A. Keles, M. Kolcak and A. “The adaptive neuro-fuzzy model for forecasting the domestic debt”, Knowledge-Based Systems, vol. 21, no. 8, pp. 951-957, 2008

W. H. Greene, Econometric Analysis. 6th ed.., Prentice Hall, New Jersey, 2008

Bollerslev, T. “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, vol. 31, pp. 307-327, 1986.

D. F. Specht “A Generalized Regression Neural Network”, IEEE Transactions on Neural Networks, vol. 2, pp. 568-576, 1991

D. A. Dickey and W. A. Fuller “Distribution of the Estimators for Autoregressive Time Series with a Unit Root”, Journal of the American Statistical Association, vol. 74, pp. 427–431, 1979

D. Kwiatkowski, P .C. B. Phillips, P. Schmidt and Y. Shin “Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root”, Journal of Econometrics, vol. 54, pp. 159-178, 1992




DOI: https://doi.org/10.26483/ijarcs.v2i2.5990

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