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

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.