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基于多因子选股的半监督核聚类算法改进研究 被引量：7

Improvement of Semi-supervised Kernel Clustering Algorithm Based on Multi-factor Stock Selection
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摘要 研究了一种附有引力影响因子的半监督K-means核函数聚类算法,并将该方法应用于多因子选股模型中。研究表明,相比传统的聚类模型,改进的模型具有较强的泛化能力,模型在处理样本线性不可分、样本分布非球状簇等问题上具有明显的优势,能选出较优的股票组合。 This paper studies a kind of semi-supervised K-means kernel function clustering algorithm with gravitational influence factors,and applies it to the multi-factor stock selection model.The empirical evidence shows that:Compared with the traditional clustering model,this improved model has a strong generalization ability,and it has obvious advantages in dealing with the non-linear and non-spherical of sample distribution,and can select a better stock combination.
作者 李文星 李俊琪
机构地区 暨南大学经济学院
出处 《统计与信息论坛》 CSSCI 北大核心 2018年第3期30-36,共7页 Journal of Statistics and Information
关键词 股票选择 核函数 半监督K-means聚类算法 stock selection kernel function semi-supervised K-means clustering
分类号 O212 [理学—概率论与数理统计] F832.5 [经济管理—金融学]
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