Ensemble with radial basis function neural networks for Casablanca stock market returns prediction

Author

Lahmiri, Salim

Author_Institution

Sch. of Manage., ESCA, Casablanca, Morocco

fYear

2014

Firstpage

469

Lastpage

474

Abstract

We present a radial basis function neural network (RBFNN) ensemble system (ES) to predict Casablanca Stock Exchange (CSE) returns based on its microstructure modeling. Its performance is compared to each RBFNN component and the conventional auto-regressive moving average (ARMA) process. Based on the mean of absolute errors (MAE) and mean of squared errors (MSE), the forecasting results showed that the RBFNNES outperformed each of its RBFNN components and also the traditional ARMA model. Our obtained results suggest that the proposed approach could be promising for CSE returns modeling and forecasting.

Keywords

autoregressive moving average processes; cost-benefit analysis; forecasting theory; mean square error methods; radial basis function networks; stock markets; ARMA process; CSE return forecasting; CSE return modeling; CSE returns; Casablanca Stock Exchange returns; Casablanca stock market return prediction; MAE; MSE; RBFNN components; RBFNN ensemble system; auto-regressive moving average process; mean absolute errors; mean squared errors; microstructure modeling; radial basis function neural networks; Artificial neural networks; Autoregressive processes; Forecasting; Predictive models; Radial basis function networks; Stock markets; ARMA Process; Ensemble System; Forecasting; Radial Basis Function Neural Network; Stock Market;

fLanguage

English

Publisher

ieee

Conference_Titel

Complex Systems (WCCS), 2014 Second World Conference on

Print_ISBN

978-1-4799-4648-8

Type

conf

DOI

10.1109/ICoCS.2014.7060978

Filename

7060978