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Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning

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  • Lixia Liu
  • Xueli Zhan

Abstract

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 (R 2 ) 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.

Suggested Citation

  • Lixia Liu & Xueli Zhan, 2019. "Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-11, July.
    • Handle: RePEc:hin:complx:9190273
    DOI: 10.1155/2019/9190273
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    1. Yong Sun & Hui Liu & Jiwei Liu & Mingyu Sun & Qun Li, 2023. "Analysis of Factors Influencing the Corporate Performance of Listed Companies in China’s Agriculture and Forestry Sector Based on a Panel Threshold Model," Sustainability, MDPI, vol. 15(2), pages 1-21, January.

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