Author/Authors :
Huang Chih-Hua نويسنده she is also a student in the PhD Program in Management at Da-Yeh University , Yang Feng-Hua نويسنده He is an Associate Professor at the Department of International Business Management at Da- Yeh University , Lee Chien-Pang نويسنده Associate Professor in the Department of Maritime Information and Technology at National Kaohsiung University of Science and Technolog
Abstract :
Stock indices forecasting has become a popular research issue in recent years.
Although many statistical time series models have been applied to stock indices forecasting,
they are limited to certain assumptions. Accordingly, the traditional statistical time series
models might not be suitable for forecasting real-life stock indices data. Hence, this
paper proposes a novel forecasting model to assist investors in determining a strategy for
investments in the stock market. The proposed model is called the modi ed support vector
regression model, which is composed of the correlation coecient method, sliding window
algorithm, and support vector regression model. The results show that the forecasting
accuracy of the proposed model is more stable than those of the existing models in terms
of average and standard deviation of the Root Mean Square Error (RMSE) and Mean
Absolute Percentage Error (MAPE). Accordingly, the proposed model would be used to
assist investors in determining a strategy for investing in stocks
