会议论文详细信息
International Scientific Conference "Digital Transformation on Manufacturing, Infrastructure and Service"
Optimizing trading company capital structure on the basis of using bankruptcy logistic models under conditions of economy digitalization
Pirogova, Oksana^1 ; Makarevich, Marina^1 ; Ilina, Olga^1 ; Ulanov, Vladimir^1
Peter the Great St.Petersburg Polytechnic University, Politechnicheskaya str., 29, Saint Petersburg
195251, Russia^1
关键词: Capital structure;    Financial distress;    Logistic models;    Logistic regression models;    Logistic regressions;    Number of factors;    Optimal level;    Risk levels;   
Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/497/1/012129/pdf
DOI  :  10.1088/1757-899X/497/1/012129
来源: IOP
PDF
【 摘 要 】
The publication gives consideration to the directions of using logistic models of bankruptcy of the companies in the algorithms of determining optimal capital structure on the basis of trade-off approach. A variant of determining company's financial distress costs has been offered proceeding from the bankruptcy risk assessment. The bankruptcy risk can be calculated on the basis of logistic regression models. Unlike the more widespread discriminate bankruptcy risk models, the bankruptcy risk models based on the logistic regression assess a probability of the bankruptcy risk occurrence. An algorithm of calculating the cost of financial distress has been offered proceeding from the risk level assessment. It will be possible to calculate the optimal level of company's debt burden on the basis of calculating this indicator in future. The suggested approach helps to take into account quite a great number of factors affecting the bankruptcy risk and use these data in the summarized form to determine the optimal level of the of company's debt burden.
【 预 览 】
附件列表
Files Size Format View
Optimizing trading company capital structure on the basis of using bankruptcy logistic models under conditions of economy digitalization 420KB PDF download
  文献评价指标  
  下载次数:9次 浏览次数:33次