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基于因子分析法的我国农业类上市公司绩效评价研究 被引量:4

Performance Evaluation of Agricultural Listed Companies Based on Factor Analysis
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摘要 农业类上市公司是农业领域先进生产力的代表,其发展壮大对中国农业产业化发展有着举足轻重的作用。为研究我国农业类公司的经营绩效,以A股市场中33家上市公司为研究对象,选取各公司2016-2018年三年间财报数据为研究样本,从盈利能力、营运能力、偿债能力以及发展能力四个方面共选取9项指标,运用因子分析和K-means均值聚类分析法,对33家上市公司的经营绩效进行评价分析,计算得出各公司的经营绩效综合得分,并对其进行排名。结果表明各公司间经营绩效差异较大,行业发展存在极不均衡的现象,因此提出针对性建议:农业类企业应优化产品生产链,加大科研投入,打造优质农产品品牌,不断增强自身核心竞争力等。以此提高公司经营绩效,进而推动中国农业类企业的良性发展。 Agricultural listed companies are the representatives of advanced productive forces in the field of agriculture.Their development and growth play an important role in the development of agricultural industrialization in China.Operating performance is the key index to evaluate the operation of enterprises.In order to study the operating performance of agricultural companies in China,this paper takes 33 listed companies in A-share market as the research object,choosing the three-year earnings data of each company from 2016 to 2018 as the research sample,and selecting nine indicators from four aspects: profitability,operating ability,solvency and development ability.This paper uses factor analysis and K-means clustering analysis to evaluate the operating performance of 33 listed companies.The results show that there are great differences in business performance among companies,and the development of the industry is extremely unbalanced.For this reason,this paper proposes that agricultural enterprises should optimize the production chain of products,increase investment in scientific research,create high-quality brand of agricultural products,and constantly enhance their core competitiveness,so as to improve the company's operating performance and then promote the sound development of Chinese agricultural enterprises.
作者 常啸 苗宇 CHANG Xiao;MIAO Yu(Anhui University of Finance and Economics, Bengbu Anhui 233030, China)
机构地区 安徽财经大学
出处 《长沙大学学报》 2019年第4期13-17,共5页 Journal of Changsha University
基金 安徽省高校自然科学重点项目,编号:KJ2016A002
关键词 农业类上市公司 经营绩效 因子分析 K-means聚类分析 agricultural listed companies operating performance factor analysis K-means cluster analysis
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