| Summary: | 碩士 === 中國文化大學 === 資訊管理學系碩士在職專班 === 101 === This study uses back-propagation neural network (BPNN) theory to forecast the next day closing index of TAIEX from January 2000 to December 2010. We use fundamental indicators and technical indicators as input variables. The learning sample is divided into business cycles, sliding windows, space windows.
The result shows that:
1. For the input parameters, using the 5-day William’s Indicator accompanies with the 5-day bias predicts better than using other parameters or using the indicators separately.
2. The predicting result can be more accurate, if we divide the period of sample data into business cycles, sliding windows, and space windows during the training phase.
3. Using business cycle as the input parameter for BPNN cannot improve the predicting performance, no matter whether using the stepwise regression method to reduce the number of input parameters or not.
4. The forecasting result is better when using longer training space window as the sample data is more stable.
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