摘要
运用K-近邻、朴素贝叶斯、决策树、支持向量机这4种数据挖掘算法,基于2015-04-01—2016-03-31A股市场所有股票日交易数据,计算10个具有代表性的传统技术分析指标,抽取合适样本,结合实际投资需求,构建4个股票强涨跌分类器。对样本数据进行测试,结果表明K-近邻具有较高的分类正确率,支持向量机具有较高的击中率。综合来看,K-近邻和支持向量机更适合于实际投资。
Four data mining algorithms are employed to build models, including K-nearest neighbors, naive Bayes, decision tree and supported vector machine. Based on all a-share market stocks＇ day trading data which is from April 1, 2015 to March 31, 2016, 10 representative traditional technical analysis parameters were calculated. By selecting appropriate samples, four classifiers were constructed combining with real investment demand and sample data was tested for predicting stock’s ups and downs. Results show K-nearest neighbors classifier has higher classification accuracy and supported vector machine has higher sensitivity. On the whole, K- nearest neighbors and supported vector machine are more suitable for real investment.
作者
张文俊
张永进
ZHANG Wenjun ZHANG Yongjin(School of Mathematics ＆ Physics, Anhui University ofTechnology, Ma＇anshan 243032, Chin)
出处
《安徽工业大学学报（自然科学版）》
CAS
2017年第1期97-102,共6页
Journal of Anhui University of Technology（Natural Science）
关键词
数据挖掘
股票分类
量化投资
分类器
data mining
stock classification
quantitative investment
classifier
