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中国管理科学

中国管理科学 ›› 2013, Vol. 21 ›› Issue (3): 35-39.

• 论文 • 上一篇    下一篇

基于支持向量机的中国股指期货回归预测研究

赛英1, 张凤廷1, 张涛2   

  1. 1. 山东财经大学管理科学与工程学院, 山东 济南 250014;
    2. 山东财经大学会计学院, 山东 济南 250014
  • 收稿日期:2012-07-30 修回日期:2013-01-20 出版日期:2013-06-30 发布日期:2013-06-20
  • 基金资助:
    国家自然科学基金资助项目(70840018)

Research of Chinese Stock Index Futures Regression Prediction Based on Support Vector Machines

SAI Ying1, ZHANG Feng-ting1, ZHANG Tao2   

  1. 1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China;
    2. School of Accounting, Shandong University of Finance and Economics, Jinan 250014, China
  • Received:2012-07-30 Revised:2013-01-20 Online:2013-06-30 Published:2013-06-20

PDF (PC)

1900

被引次数

71

摘要： 本文针对股指期货预测的特点,选择对股指期货指数有重要影响的相关指标,首次提出用支持向量机(SVM)方法对其进行回归预测,并用遗传算法(GA)和粒子群算法(PSO)分别优化四种不同核函数的支持向量机,构建了八种不同的中国股指期货回归预测方案,用实证研究的方法对这八种方案的准确性和时效性进行了比较。实验结果表明粒子群算法优化的线性核函数支持向量机作为中国股指期货回归预测的模型,具有更好的预测效果。

关键词: 中国股指期货, 支持向量机, 遗传算法, 粒子群算法, 回归预测

Abstract: According to the characteristics of the stock index futures prediction, the indicators that have great influence on the development trend of stock index futures are selected and the support vector machines are firstly used to the regression prediction of stock index futures. Besides, genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are employed to optimize the support vector machine (SVM) with four different kernel functions and eight different programs are attained. By comparing the accuracy and the time complexity of all the programs, the empirical study shows that the linear kernel function SVM optimized by PSO is the best model for regression prediction of Chinese stock index futures.

Key words: Chinese stock index futures, support vector machine (SVM), genetic algorithm (GA), particle swarm optimization algorithm (PSO), regression prediction

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