NDLI: Application of dynamic financial time-series prediction on the interval Artificial Neural Network approach with Value-at-Risk model

Content Provider	IEEE Xplore Digital Library
Author	Hsio-Yi Lin An-Pin Chen
Copyright Year	2008
Description	Author affiliation: Dept. of Finance, Ching-Yun Univ., Taoyuan (Hsio-Yi Lin) \|\| Inst. of Inf. Manage., Nat. Chiao-Tang Univ., Hsinchu (An-Pin Chen)
Abstract	Artificial neural networks (ANNs) are promising approaches for financial time-series prediction. This study adopts a hybrid approach, called a fuzzy BPN, consisting of a back-propagation neural network (BPN) and a fuzzy membership function which takes advantage of the ANNspsila nonlinear features and interval values instead of the shortcoming of ANNspsila single-point estimation. To employ the two characteristics mentioned above, a dynamic intelligent time-series forecasting system will be built more efficiently for practical financial predictions. Additionally, with the liberalization and opening of financial markets, the relationships among financial commodities became much closer and complicated. Hence, establishing a perfect measure approach to evaluate investment risk has become a critical issue. The objective of this study is not only to achieve higher efficiency in dynamic financial time-series predictions but also a more effective financial risk control with value-at-risk methodology, which is called fuzzy-VaR BPN model in this study. By extending to the financial market environment, it is expected that wider and more suitable applications in financial time-series and risk management problems would be covered. Moreover, the fuzzy-VaR BPN model would be applied to the Taiwan Top50 Tracker Fund to demonstrate the capability of our study.
Starting Page	3918
Ending Page	3925
File Size	258888
Page Count	8
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4634361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Reactive power Joints Conferences Predictive models Nonlinear dynamical systems Estimation
Content Type	Text
Resource Type	Article

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