Stock Time Series Forecasting Using Support Vector Machines Employing Analyst Recommendations
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.
KeywordsSupport Vector Machine Stock Price Input Attribute Time Series Forecast Closing Price
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- 1.Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998)Google Scholar
- 3.Fung, G.P.C., Yu, J.X., Lam, W.: Stock Prediction: Integrating Text Mining Approach using Real-Time News. In: Computational Intelligence for Financial Engineering, pp. 395–402 (2003)Google Scholar
- 4.Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. Technical Report, Department of Computer Science, National Taiwan University (2001)Google Scholar
- 5.Hsu, C.W., Chang, C.C.: A Practical Guide to Support Vector Classification. Department of Computer Science and Information Engineering. National Taiwan University (2003)Google Scholar