Stock Indices Forecasting Using a Support Vector Machine
碩士 === 國立交通大學 === 科技管理研究所 === 92 === This thesis deals with the application of a novel neural network technique, Support Vector Machine (SVM), in stock indices movement prediction. The purpose of this thesis is to demonstrate and verify the predictability of stock index direction using SVM, to devel...
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| Format: | Others |
| Language: | en_US |
| Published: |
2004
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| Online Access: | http://ndltd.ncl.edu.tw/handle/89862312651459498965 |
| Summary: | 碩士 === 國立交通大學 === 科技管理研究所 === 92 === This thesis deals with the application of a novel neural network technique, Support Vector Machine (SVM), in stock indices movement prediction. The purpose of this thesis is to demonstrate and verify the predictability of stock index direction using SVM, to develop effective trading strategies and to test the relative performance. A real future contract (Taiwan Stock Exchange Capitalization Weighted Stock Index) collected from Taiwan Futures Exchange is used as the data set. The series of relative difference in percentage of price (RDP) is adopted as the input variables to describe the patters of market movement. Results indicate that the technique is capable of returning results that are superior to those attained by buy-and-hold strategy.
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