Statistical models of KSE100 index using hybrid financial systems

This paper utilizes hybrid financial systems (HFSs) to model Karachi Stock Exchange index data, KSE100, for short-term prediction. These HFSs developed for this purpose are combination of artificial neural networks (ANN) and ARIMA or autoregressive conditional heteroskedasticity/generalized autoregressive conditional heteroskedasticity (ARCH/GARCH) models. We compared ANN with ARIMA and ARCH/GARCH on the basis of forecast mean square error (FMSE), ANN gave better forecasting performance and out played ARIMA and ARCH/GARCH models. While comparing the performance of HFSs of ANN(ARIMA) and ANN(ARCH/GARCH) with ANN model, it is found that the HFS ANN(ARCH/GARC)H is superior to standard ANN and HFS ANNARIMA in forecasting KSE100 index. (C) 2008 Elsevier B.V. All rights reserved.