文献类型：期刊文献

英文题名：Research on support vector regression in the stock market forecasting

作者：Cai, Chun[1]; Ma, Qinghua[1]; Lv, Shuqiang[1]

第一作者：蔡春

通讯作者：Cai, C.

机构：[1] Dept. of Information and Science, College of Arts and Science, Beijing Union University, Beijing 100191, China

第一机构：北京联合大学应用文理学院

年份：2012

卷号：148 AISC

期号：VOL. 1

起止页码：607-612

外文期刊名：Advances in Intelligent and Soft Computing

收录：EI(收录号：20123115296467)

语种：英文

外文关键词：Investments - Commerce - Electronic trading - Regression analysis - Financial markets - Vectors - Forecasting - Vector spaces

摘要：As the stock market has high noise, nonlinearity, uncertainty characteristics as well as traditional neural network forecasting method deficiencies of the problem, this paper presents a support vector regression (SVR) and financial time series methods to forecast future stock market; follows the value investment philosophy, reduces dimensional space by support vector machines the input vector mapped to a high dimensional space, and non-linear problem into a linear problem, establish the value of the equity investments of prediction models under the principle of structural risk minimization. For an empirical test of the model to get the stock opened at 0.02% error of 3.66%, closing error of 0.00% to 3.41%, the results showed that SVR prediction in the stock market index has certain advantages. ? 2012 Springer-Verlag GmbH.