Evaluation Analysis of Credit Risk for Listed Real Estate Companies Based on Logistic Model
- DOI
- 10.2991/icemse-19.2019.120How to use a DOI?
- Keywords
- principal component analysis, Logistic model, credit risk, listed real estate company
- Abstract
With the aim to measure the credit risk of real estate companies, this study took the default probability of companies as a criterion and developed the evaluation model of credit risk. The sample included forty real estate companies listed in China’s stock exchanges whose data has been accessible from 2014 to 2018. we used applied Kolmogorov-Smirnov test to judge the distribution of selected fifteen indexes, then, applied independent-samples T test and Mann-Whitney U test to select indexes with significance, after that, principal component analysis was used to condense fifteen indicators into three principal component factors, finally, a Logistic regression model was built with those three principal component factors. Results show that the correctness of the prediction model is more than 85%; indexes representing profitability, operating capacity and growth capacity of the company have a significant impact on the credit risk. The Logistic model can make a more accurate judgment on the credit risk of real estate companies.
- Copyright
- © 2019, 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 - Dongpeng Huang PY - 2019/09 DA - 2019/09 TI - Evaluation Analysis of Credit Risk for Listed Real Estate Companies Based on Logistic Model BT - Proceedings of the 2019 3rd International Conference on Education, Management Science and Economics (ICEMSE 2019) PB - Atlantis Press SP - 518 EP - 522 SN - 2352-5428 UR - https://doi.org/10.2991/icemse-19.2019.120 DO - 10.2991/icemse-19.2019.120 ID - Huang2019/09 ER -