Application of support vector machines regression in prediction Shanghai stock composite index |
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Authors: | Email author" target="_blank">Wang?DongEmail author Wu?Wen-feng |
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Institution: | (1) Aetna School of Management, Shanghai Jiaotong University, 20052 Shanghai, China |
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Abstract: | 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 ɛ-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.
Founation item: Supported by the National Natural Science Foundation of China (70202005)
Biography: Wang Dong (1972-), male, Ph. D. candidate, research direction: financial engineering |
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Keywords: | stock market SVMs BP neural networks forecasting |
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