Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Forecasting Approach Using Hybrid Model ASVR/NGARCH with Quantum Minimization
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
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This paper discusses the application of support vector machine (SVM) in stock price change trend forecasting. By reviewing prior research, thirteen technical indicators are defined as the input attributes of SVM. By training this model, we can forecast if the stock price would rise the next day. In order to make best use of market information, analyst recommendations about upgrading stocks are employed. So we put forward an improved method to evaluate if an upgrade classification of SVM is reliable. In our method, recommendation accuracy is first calculated according to historical advice. Then the more objective relative accuracy is deduced by considering the influence of total stock market index. Moreover, improved model is examined with the real data in Shanghai stock exchange market. Finally, we discuss some interesting hints to help readers understand this model more explicitly.