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上市公司投资模式分类及业绩表现研究

Research on investment pattern classification and performance of listed companies
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摘要 我国上市公司的投资问题已经成为投资者和研究者关注的热点问题。本文选取沪深A股2014家上市公司数据为研究样本,以投资分类为指标进行聚类分析,使用财务业绩指标进行主成分分析,联系实际对聚类分析结果与主成分分析综合得分进行对比,探究不同投资模式下,财务因子综合得分情况,最终得到适合进行投资领域研究的590家公司。研究结果表明:投资模式可分为6类;而主成分分析结果显示,偏重均衡投资模式、股权投资模式、债权投资模式的公司经营状况均良好,而偏重固定资产投资模式的企业财务状况相对较差。基于以上方法得到的结果对进一步研究投资与公司治理的关系有一定的指导意义,对预测公司发展前景有一定的参考价值。 The investment problem of China s listed companies has become a hot topic for investors and researchers. This paper selects the data of 2014 listed companies of Shanghai and Shenzhen A-shares as research samples,analyzes the investment classification as indicators,uses the financial performance indicators for principal component analysis, and compares the cluster analysis results with the principal component analysis comprehensive scores. Exploring the comprehensive scores of financial factors under different investment patterns,and finally the results were 590 companies suitable for research in the investment field. The results show that the clustering divides the samples into six categories,and the results of principal component analysis are good,which proves that the classification results obtained by clustering are good. The results of principal component analysis show that the companies that emphasize balanced investment pattern,equity investment pattern and debt investment pattern are all in good operating condition,while those that emphasize fixed asset investment pattern are in poor financial condition. The results obtained based on the above methods have certain guiding significance for further research on the relationship between investment and corporate governance,and have certain reference value for predicting the development prospect of the company.
作者 单爽 周石鹏 SHAN Shuang;ZHOU Shi-peng(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处 《科技与管理》 2019年第5期89-94,共6页 Science-Technology and Management
关键词 上市公司 投资模式 聚类分析 主成分分析 listed company investment pattern cluster analysis principal component analysis
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