Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning
Agricultural enterprises play a significant role in China’s economic development. However, compared with other enterprises, agricultural enterprises are facing serious financial problems. Financing difficulty is essentially a question of financing efficiency. Based on the DEA method, this paper eval...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9190273 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551955697762304 |
|---|---|
| author | Lixia Liu Xueli Zhan |
| author_facet | Lixia Liu Xueli Zhan |
| author_sort | Lixia Liu |
| collection | DOAJ |
| description | Agricultural enterprises play a significant role in China’s economic development. However, compared with other enterprises, agricultural enterprises are facing serious financial problems. Financing difficulty is essentially a question of financing efficiency. Based on the DEA method, this paper evaluates the financing efficiency of 39 agricultural listed companies in China from 2013 to 2017. The results suggest that the financing efficiency is generally low, and the Total Factor Productivity of agricultural enterprises’ financing has a tendency to decrease first and then increase. The influencing factors of financing efficiency are analyzed using the Tobit regression model and the random forest regression model. And we find the following: (1) The random forest regression model significantly outperformed the Tobit regression model, with determination coefficients (R2) greater than 0.9 in full sample sets. (2) Total liability, financial expenses, return on total assets, and inventory turnover rate are important factors affecting financing efficiency of agricultural listed companies. (3) Return on total assets and inventory turnover rate promote the financing efficiency, while total liability and financial expenses reduce financing efficiency. Finally, the paper makes some suggestions for the financing of agricultural enterprises. |
| format | Article |
| id | doaj-art-7154c4ea3ea54f5183844cbb2deee02d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7154c4ea3ea54f5183844cbb2deee02d2025-02-03T06:00:02ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/91902739190273Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine LearningLixia Liu0Xueli Zhan1School of Economics, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Economics, Beijing Wuzi University, Beijing 101149, ChinaAgricultural enterprises play a significant role in China’s economic development. However, compared with other enterprises, agricultural enterprises are facing serious financial problems. Financing difficulty is essentially a question of financing efficiency. Based on the DEA method, this paper evaluates the financing efficiency of 39 agricultural listed companies in China from 2013 to 2017. The results suggest that the financing efficiency is generally low, and the Total Factor Productivity of agricultural enterprises’ financing has a tendency to decrease first and then increase. The influencing factors of financing efficiency are analyzed using the Tobit regression model and the random forest regression model. And we find the following: (1) The random forest regression model significantly outperformed the Tobit regression model, with determination coefficients (R2) greater than 0.9 in full sample sets. (2) Total liability, financial expenses, return on total assets, and inventory turnover rate are important factors affecting financing efficiency of agricultural listed companies. (3) Return on total assets and inventory turnover rate promote the financing efficiency, while total liability and financial expenses reduce financing efficiency. Finally, the paper makes some suggestions for the financing of agricultural enterprises.http://dx.doi.org/10.1155/2019/9190273 |
| spellingShingle | Lixia Liu Xueli Zhan Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning Complexity |
| title | Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning |
| title_full | Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning |
| title_fullStr | Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning |
| title_full_unstemmed | Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning |
| title_short | Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning |
| title_sort | analysis of financing efficiency of chinese agricultural listed companies based on machine learning |
| url | http://dx.doi.org/10.1155/2019/9190273 |
| work_keys_str_mv | AT lixialiu analysisoffinancingefficiencyofchineseagriculturallistedcompaniesbasedonmachinelearning AT xuelizhan analysisoffinancingefficiencyofchineseagriculturallistedcompaniesbasedonmachinelearning |