摘要
The SVMs for regression is used to forecast Shanghai stock composite index (SSCI). Implementing structural risk minimization principle, SVMs can overcome the over-fitting problem. The regression uses ε-insensitive loss function. The training of SVMs leads to a quadratic programming problem and it has a global unique solution. The experiment uses BP neural networks as benchmark for comparison. The results demonstrate that the prediction figure of SSCI can help to find timing for buy or sell, the forecasting variation of SVMs is smaller than that of BP, and the direction forecasting of SVMs is more accurate than that of BP.
The SVMs for regression is used to forecast Shanghai stock composite index (SSCI). Implementing structural risk minimization principle, SVMs can overcome the over-fitting problem. The regression uses ε-insensitive loss function. The training of SVMs leads to a quadratic programming problem and it has a global unique solution. The experiment uses BP neural networks as benchmark for comparison. The results demonstrate that the prediction figure of SSCI can help to find timing for buy or sell, the forecasting variation of SVMs is smaller than that of BP, and the direction forecasting of SVMs is more accurate than that of BP.
基金
SupportedbytheNationalNaturalScienceFoundationofChina (70 2 0 2 0 0 5)
