Earticle

초록

영어

This paper construct the predicted model based on support vector machine (SVM) for the Shanghai Composite Index, acquired the model parameters using genetic algorithm optimization was carried out, combined with k-fold cross method. Experiments based on the start date to February 2011 total 4948 trading day data, 10 fold cross circulation experiments of GA optimization; get the most accurate model parameter of SVM. At last, the regression model is used to predict, and the relative error of regression prediction is 0.11, and the accuracy of regression prediction is higher. In conclusion, this model can be used to predict the Shanghai Composite Index.

목차

Abstract
 1. Introduction
 2. Algorithm Analysis
  2.1. The GA Algorithm Principle
  2.2. SVM Regression Prediction Algorithm
 3. GA-SVM Optimization of the Stock Market Forecast Model
 4. Empirical Analysis
  4.1. Index Selection
  4.2. Experiment Design
  4.3. Results Analysis
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Liang bang-long [ School of Economics and Management, Tongji University, Shanghai 200092, China ]
• Lin jie [ School of Economics and Management, Tongji University, Shanghai 200092, China ]
• Yuan Guanghui [ School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605