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Abstract: In this paper we construct the consumer loan default predicting model through conducting the empirical analysis on the customers of unsecured consumer loan from a certain financial institution in Taiwan, and adopt the borrower’s demographic variables and money attitude as the real-timeaneous discriminant information. Furthermore, we construct respectively through four predicting methods, such as DA, LR, NN and DEA–DA, to compare the suitability of these four mentioned methods. The results show that DEA–DA and NN are possessed better predicting capability and they are the optimal predicting model that this study longing for. In addition, this study showed that the default loan predicting model will be possessed higher level of predicting capability after added money attitude. [Copyright &y& Elsevier]

Abstract: This paper proposes a hybrid methodology that exploits the unique strength of the seasonal autoregressive integrated moving average (SARIMA) model and the support vector machines (SVM) model in forecasting seasonal time series. The seasonal time series data of Taiwan’s machinery industry production values were used to examine the forecasting accuracy of the proposed hybrid model. The forecasting performance was compared among three models, i.e., the hybrid model, SARIMA models and the SVM models, respectively. Among these methods, the normalized mean square error (NMSE) and the mean absolute percentage error (MAPE) of the hybrid model were the lowest. The hybrid model was also able to forecast certain significant turning points of the test time series. [Copyright &y& Elsevier]