| Summary: | 碩士 === 國立暨南國際大學 === 資訊管理學系 === 96 === This study proposed two forecasting models, which are Fuzzy GP/SC and Fuzzy Piecewise MOGP/SC. Fuzzy GP/SC is used to deal with crisp observations, and can be applied in small observations and provide decision makers the best-possible and worst-possible situations. The experiments found that Fuzzy GP/SC is better than Fuzzy ARIMA (Tseng et al., 2001) with minimizing Mean Absolute Deviations (MAD). Fuzzy Piecewise MOGP/SC is used to deal with fuzzy observations, and can be applied with small observations and get smaller MAD than Fuzzy ARIMA does because it can solves problem of outliers instead of deleting all the outliers.
This study used predicted return instead of arithmetic mean for Multiple Criteria Decision Making (MCDM) to conduct portfolio selection. GARCH model was used to calculate the risk for standard deviations. Moreover, there are two forecasting models used to forecast predicted return, which are GP/SC model and ARIMA model. MCDM is consisted of four criteria, which are predicted return, predicted risk, β value, and skewness. The experiments found that the GPSC-GARCH model outperformed the MVBS (Cho, 2007), GPSC-STD, and ARMA-GARCH.
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