کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
409088 | 679053 | 2008 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Statistical models of KSE100 index using hybrid financial systems
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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 ANNARIMA and ANNARCH/GARCH with ANN model, it is found that the HFS ANNARCH/GARCH is superior to standard ANN and HFS ANNARIMA in forecasting KSE100 index.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 71, Issues 13–15, August 2008, Pages 2742–2746
Journal: Neurocomputing - Volume 71, Issues 13–15, August 2008, Pages 2742–2746
نویسندگان
Samreen Fatima, Ghulam Hussain,