With the development of machine learning technologies, predicting stock movements by analyzing news articles has been studied actively. Most of the existing studies utilize only the datasets of target companies, and some studies use datasets of the relevant companies in the Global Industry Classification Standard (GICS) sectors. However, we show that GICS has a limitation in finding relevance regarding stock prediction because heterogeneity exists in the GICS sectors. To solve this limitation, we suggest a methodology that reflects heterogeneity and searches for homogeneous groups of companies which have high relevance. Stock price movements are predicted using the K-means clustering and multiple kernel learning technique which integrates information from the target company and its homogeneous cluster. We experiment using three-year data from the Republic of Korea and compare the results of the proposed method with those of existing methods. The results show that the proposed method shows higher predictability than existing methods in the majority of cases. The results also imply that the necessity of cluster analysis depends on the heterogeneity in the sector, and it is essential to perform cluster analysis with a larger number of clusters as the heterogeneity increases.

随着机器学习技术的发展，已经积极研究了通过分析新闻文章来预测股票走势。现有的大多数研究仅使用目标公司的数据集，而一些研究则使用全球行业分类标准（GICS）部门中相关公司的数据集。但是，我们表明，由于GICS部门中存在异质性，因此GICS在找到与库存预测相关的信息方面存在局限性。为解决此限制，我们建议一种方法，该方法可反映异质性并搜索具有高度相关性的同类公司。使用K-means聚类和多核学习技术预测股票价格的走势，该技术集成了来自目标公司及其同类集群的信息。我们使用来自大韩民国的三年数据进行了实验，并将该方法的结果与现有方法的结果进行了比较。结果表明，在大多数情况下，所提出的方法具有比现有方法更高的可预测性。结果还暗示，聚类分析的必要性取决于部门中的异质性，并且随着异质性的增加，必须对大量聚类进行聚类分析。