Browsing by Author "Lin, Hsio-Yi"

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An Integrated Home Financial Investment Learning Environment Applying Cloud Computing in Social Network Analysis
(2011)
Wen, Mao-Ping
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Lin, Hsio-Yi
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Chen, An-Pin
;
Yang, Chyan
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資訊管理與財務金融系註：原資管所+財金所
This paper tried to apply cloud computing technology in social network analysis for a comprehensive home financial learning environment that individual investors may use as a reference in establishing web-based learning and investment platforms. The major contributions were described in three parts. First, this paper advanced the social network analysis technology to be able to handle millions of nodes and links. Second, we demonstrate how cloud computing can be applied to advanced computing in social network. Third, we performed several intelligent analyses on a very popular social network, IHFILE, to identify some interesting and important features of it. In addition to analyzing a homogeneous social network such as IHFILE, we also propose direction of how cloud computing can be performed on a social network analysis as our future work.
Application of Dynamic Financial Time-Series Prediction on the Interval Artificial Neural Network Approach with Value-at-Risk Model
(2008)
Lin, Hsio-Yi
;
Chen, An-Pin
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
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 ANNs\' nonlinear features and interval values instead of the shortcoming of ANNs\' 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.
Exchange rates forecasting using a hybrid fuzzy and neural network model
(2007)
Chen, An-Pin
;
Lin, Hsio-Yi
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Artificial neural networks (ANNs) are promising approaches for financial time series prediction and have been widely applied to handle finance problems because of its nonlinear structures. However, ANNs have some limitations in evaluating the output nodes as a result of single-point values. This study proposed a hybrid model, called Fuzzy BPN, consisting of backpropagation neural network (BPN) and fuzzy membership function for taking advantage of nonlinear features and interval values instead of the shortcoming of single-point estimation. In addition, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN and the empirical results show that Fuzzy BPN provides a useful alternative to exchange rate forecasting.