Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices. In the proposed model, stepwise regression is first adopted, and multivariate adaptive regression splines and kernel ridge regression are then used to select the key features. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. The collected stock prices are employed to verify the proposed model under accuracy. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors.

Since Charnes, Cooper, and Rhodes introduced data envelopment analysis (DEA) in 1978, later called the DEA-CCR model, many studies applied this technique to different fields. Based on the original CCR model, many modified DEA models were developed by researchers. Since 1999, Seiford and Zhu presented a two-stage DEA model. Later, these models were widely used in many studies. However, the relationship between the efficiency scores that are obtained from the original CCR model and the two-stage DEA model remains unknown. To fill this gap, this study proposed a theoretical relationship between the efficiency scores that are calculated from the two-stage DEA model and those that are obtained from the original CCR model. How the sets of nonsymmetrical weights affected the efficiency scores were also investigated. Theorems regarding the relationship were developed, and then the model was utilized to evaluate the two-stage efficiency scores of the insurance companies (non-life) and bank branches. The results show that using a two-stage DEA model can get more information about operational efficiency than the traditional CCR model does. The findings from this study about the two-stage DEA technique can provide significant reasons for using this model to evaluate performance efficiency.

Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.