The Research Results of Professor Peng Fangping's Team Were Published in Management World

In recent years, as the problem of "difficult and expensive financing" for Chinese enterprises has become increasingly prominent, rigid payment in financial market has attracted widespread attention. However, can breaking the rigid payment effectively reduce the financing cost of enterprises? The answer to this question remains controversial both theoretically and practically. Based on this, the team of Professor Peng Fangping from the School of Business, Sun Yat-sen University used the method of Double Machine Learning (DML) to answer the above question. The research combines the data of bond market and listed companies, applies the method of DML, and uses K-Fold ArCo artificial counterfactual model and other machine learning models for robustness analysis to examine the impact of breaking rigid payment on the market interest of China's bond. The study found that breaking the rigid payment does not reduce the financing cost of enterprises, but causes the increase of financing cost in overall market. The reason is that breaking the rigid payment does not effectively reduce the risk-free interest rate, but the incident leads to the increase in the overall risk premium, which pushes up the financing cost of enterprises instead.

The research results were published in the economic management journal "Management World", Vol. 4, 2022, under the title "Can Breaking Rigid Payment Reduce the Financing Costs of Enterprises?". The School of Business, Sun Yat-sen University is the first corresponding unit, doctoral student Wang Ruting is the first author, and Professor Peng Fangping is the corresponding author. Other collaborators are Dr. Li Wei from Norwegian University of Science and Technology and graduate student Wang Chunli from the School of Business. This research was funded by the National Natural Science Foundation of China on "Over-indebtedness, Financial Stress and Real Economy Downturn: Research on Theory, Evidence and Countermeasures" (fund no. 71673312).