纺织行业上市公司财务危机预警研究
发布时间：2019-06-18 17:14
【摘要】：2008年美国次贷危机引发了全球性金融危机,造成了大批银行、企业纷纷破产倒闭,损失惨重。国际社会由此反思,如果这些企业的管理层以及投资者能够对企业进行有效的危机管理,那么这些企业在这场全球金融危机中就完全可能避免损失或少受一些损失。目前,经济环境复杂多变,要想对企业进行危机管理,就需要对企业进行财务危机预警。 财务危机预警就是根据财务危机企业的特征,对企业进行危机预警,所以寻找到能够反映财务危机企业特征的变量组合就显得尤为重要,由于我国上市公司没有法定破产实例,本文将财务危机企业定义为“无法按时偿还债务或支付费用的企业”,由于资信评级与企业坏账比例和财务比率之间存在着密切的联系,本文使用资信评级界定纺织行业上市公司财务危机企业,参照我国工业信用评级指标体系构建财务危机预警指标体系,对2010年至2012年纺织行业上市公司进行财务危机预警,通过BP神经网络的学习过程,构建财务危机预警模型,通过BP神经网络的识别过程,完成财务危机预警。 研究结果显示,最终构建的变量组合能够在一定程度上反映财务危机企业的特征,能够为企业管理者及投资者提供所需的企业危机管理的情报信息,具有一定的应用价值。
[Abstract]:In 2008, the subprime mortgage crisis in the United States triggered a global financial crisis, resulting in a large number of banks, enterprises have gone bankrupt, heavy losses. As a result, the international community reflects that if the management and investors of these enterprises can effectively manage the crisis of enterprises, then these enterprises are likely to avoid losses or suffer less losses in this global financial crisis. At present, the economic environment is complex and changeable, in order to carry on the crisis management to the enterprise, it is necessary to carry on the financial crisis early warning to the enterprise. Financial crisis early warning is to carry out crisis early warning according to the characteristics of financial crisis enterprises, so it is particularly important to find a combination of variables that can reflect the characteristics of financial crisis enterprises. Because there are no legal bankruptcy examples of listed companies in our country, this paper defines financial crisis enterprises as "enterprises that can not repay their debts or pay expenses on time". Because there is a close relationship between credit rating and bad debt ratio and financial ratio of enterprises, this paper uses credit rating to define the financial crisis enterprises of listed companies in textile industry, constructs the financial crisis early warning index system according to the industrial credit rating index system of our country, carries on the financial crisis early warning to the listed companies of textile industry from 2010 to 2012, and constructs the financial crisis early warning model through the learning process of BP neural network. Through the identification process of BP neural network, the financial crisis early warning is completed. The results show that the final combination of variables can reflect the characteristics of financial crisis enterprises to a certain extent, and can provide enterprise managers and investors with the required information of enterprise crisis management, and has certain application value.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.81;F406.7
本文编号：2501650
[Abstract]:In 2008, the subprime mortgage crisis in the United States triggered a global financial crisis, resulting in a large number of banks, enterprises have gone bankrupt, heavy losses. As a result, the international community reflects that if the management and investors of these enterprises can effectively manage the crisis of enterprises, then these enterprises are likely to avoid losses or suffer less losses in this global financial crisis. At present, the economic environment is complex and changeable, in order to carry on the crisis management to the enterprise, it is necessary to carry on the financial crisis early warning to the enterprise. Financial crisis early warning is to carry out crisis early warning according to the characteristics of financial crisis enterprises, so it is particularly important to find a combination of variables that can reflect the characteristics of financial crisis enterprises. Because there are no legal bankruptcy examples of listed companies in our country, this paper defines financial crisis enterprises as "enterprises that can not repay their debts or pay expenses on time". Because there is a close relationship between credit rating and bad debt ratio and financial ratio of enterprises, this paper uses credit rating to define the financial crisis enterprises of listed companies in textile industry, constructs the financial crisis early warning index system according to the industrial credit rating index system of our country, carries on the financial crisis early warning to the listed companies of textile industry from 2010 to 2012, and constructs the financial crisis early warning model through the learning process of BP neural network. Through the identification process of BP neural network, the financial crisis early warning is completed. The results show that the final combination of variables can reflect the characteristics of financial crisis enterprises to a certain extent, and can provide enterprise managers and investors with the required information of enterprise crisis management, and has certain application value.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.81;F406.7
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