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Stock Time Series Forecasting Using Support Vector Machines Employing Analyst Recommendations

  • Zhi-yong Zhang
  • Chuan Shi
  • Su-lan Zhang
  • Zhong-zhi Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

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.

Keywords

Support Vector Machine Stock Price Input Attribute Time Series Forecast Closing Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    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
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhi-yong Zhang
    • 1
    • 2
  • Chuan Shi
    • 1
    • 2
  • Su-lan Zhang
    • 1
    • 2
  • Zhong-zhi Shi
    • 1
  1. 1.Institute of Computing and TechnologyChinese Academy of SciencesBeijing
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina

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