| 基于K-means聚类和相对估值法的选股研究 |
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| 引用本文： | 刘晨旸.基于K-means聚类和相对估值法的选股研究[J].长春金融高等专科学校学报,2021(1):34-42. |
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| 作者姓名： | 刘晨旸 |
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| 作者单位： | 山西财经大学 金融学院,山西 太原 030012 |
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| 基金项目： | 山西省社会经济统计科研重点课题 |
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| 摘 要： | 当前机器学习热度不减,得益于机器学习在多领域的广泛应用,许多经典问题有了新的解决思路。针对量化选股问题,将价值投资中的公司相对估值法与机器学习中的K-means聚类算法相结合,构建一种简便易操作的选股策略,并选取2015—2019年数据进行实证分析。结果表明2015—2019年策略组合的回报率均优于基准指数(上证50、沪深300)的回报率。
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| 关 键 词： | K-means 聚类分析 选股策略 |
Research on Stock Selection Based on K-means Clustering and Relative Valuation |
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| LIU Chen-yang.Research on Stock Selection Based on K-means Clustering and Relative Valuation[J].Journal of Changchun Finance College,2021(1):34-42. |
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| Authors: | LIU Chen-yang |
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| Institution: | (School of Finance,Shanxi University of Finance and Economics,Taiyuan 030000,China) |
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| Abstract: | At present,machine learning is still popular.Thanks to the wide application of machine learning in many fields,many classic problems have new solution.Aiming at the problem of quantitative stock selection,this paper combines the company relative valuation method in value investment with K-means clustering algorithm in machine learning to construct a simple and easy to operate stock selection strategy,and selects the data from 2015 to 2019 for empirical analysis.The results show that the returns of the strategic portfolio from 2015 to 2019 are better than those of the benchmark indexes(SSE 50 and CSI 300). |
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| Keywords: | K-means clustering analysis stock selection strategy |
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