Research on Performance Evaluation of Listed Companies in China Based on Factor Analysis and Cluster Analysis
- DOI
- 10.2991/wrarm-17.2017.18How to use a DOI?
- Keywords
- Performance Appraisal; Factor Analysis; Cluster Analysis
- Abstract
This paper choose 2841 listed companies financial data in 2016 from Shanghai Stock Exchange from the RESSET data and build performance evaluation index system with 10 financial indicators. This paper uses factor analysis method to reduce the dimension of the evaluation index in the sample of listed companies and classify the listed companies by cluster analysis method. The number of evaluation index is reduced to 4 from 10, namely: income level, profitability, operation ability, growth ability, the original amount of information from the 4 factors to carry up to 73.387%. According to the contribution rate of the common factors and factor rotation to get The comprehensive evaluation model of financial situation of listed companies in China and use profitability factor in the result of factor analysis as the goal of clustering on listed companies, the clustering result showed that the Chinese listed companies can be divided into 5 categories, grade AAA; grade AA; Grade A; grade B; grade C. This paper has found that there have significant differences in the level of profitability and operational level between grade AA and grade B, not in the level of income and growth ability.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Chenggang Li AU - Kang Pan PY - 2017/11 DA - 2017/11 TI - Research on Performance Evaluation of Listed Companies in China Based on Factor Analysis and Cluster Analysis BT - Proceedings of the Fifth Symposium of Risk Analysis and Risk Management in Western China (WRARM 2017) PB - Atlantis Press SP - 99 EP - 104 SN - 1951-6851 UR - https://doi.org/10.2991/wrarm-17.2017.18 DO - 10.2991/wrarm-17.2017.18 ID - Li2017/11 ER -