Forecasting returns on a stock market using Artificial Neural Networks and GARCH family models: Evidence of stock market S & P 500

Selmi, N.; Chaabene, S.; Hachicha, N.

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Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 4 Issue 2 pp. 203-210 , 2015

Forecasting returns on a stock market using Artificial Neural Networks and GARCH family models: Evidence of stock market S & P 500 Pages 203-210 Download PDF

Authors: Nadhem Selmi, Samira Chaabene, Nejib Hachicha

DOI: 10.5267/j.dsl.2014.12.002

Keywords: Artificial neural networks, Forecasts, GARCH, Non-linear modeling, Stock market forecasting

Abstract: In the area of financial stock market forecasting, many studies have focused on application of Artificial Neural Networks (ANNs). Due to its high rate of uncertainty and volatility, the stock markets returns forecasting by conventional methods became a difficult task. The ANNs is a relatively new and have been reported as good methods to predict financial stock market levels and can model flexible linear or non-linear relationship among variables. The aim of the study is to employ an ANN models to estimate and predict the dynamic volatility of the daily of S & P500 market returns. Results of ANN models will be compared with time series model using GARCH family models. The use of the novel model for conditional stock markets returns volatility can handle the vast amount of nonlinear data, simulate their relationship and give a moderate solution for the hard problem. The forecasts of stock index returns in the paper will be evaluated and compared, considering the MSE, RMSE and MAE forecasts statistic.

How to cite this paper

 Selmi, N., Chaabene, S & Hachicha, N. (2015). Forecasting returns on a stock market using Artificial Neural Networks and GARCH family models: Evidence of stock market S & P 500.Decision Science Letters , 4(2), 203-210.

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