원문정보
초록
영어
This study proposes design concepts for a comprehensive home financial learning environment that individual investors can use as a reference in establishing web-based learning and investment platforms. This study also introduces a hybrid approach that demonstrates a data mining function of the financial learning environment. Known as Fuzzy BPN, this approach is comprised of backpropagation neural network (BPN) and fuzzy membership function. This membership function takes advantage of the nonlinear features of artificial neural networks (ANNs) and the interval values as a means of overcoming the inadequacy of single-point estimation of ANNs. Based from these characteristics, a dynamic and intelligent time-series forecasting system will be developed for practical financial predictions. In addition to this, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN. The empirical results of the study show that Fuzzy BPN provides an alternative data mining tool for financial learning environment to investment forecasting.
목차
1. Introduction of Home Financial Investment
1.1. IHFILE System Framework
1.2. Web-Based Learning Environment
1.3. Module Design
1.4. The Virtual Trading Center
1.5. Financial Market Scenario Generator
2. Preliminary Description of Dynamic Financial Time-series PredictiveModel for Exchange Rate forecast
3. Artificial Neural Network and GARCH Model
3.1. Artificial Neural Network Model
3.2. GARCH Model
4. The Hybrid Methodology and Research Design
4.1. Fuzzy BPNs
4.2. Data and Experimental Design
5. Empirical Results
5.1 BPNs Model
5.2. GARCH Model
5.3. Forecasting Performance
6. Conclusion
7. References