节点文献
中国上市公司财务预警实证研究
The Empirical Research of Financial Early Warning in Chinese Listed Companies
【作者】 褚玲仙;
【导师】 苏宁;
【作者基本信息】 北京林业大学 , 会计学, 2011, 硕士
【摘要】 随着市场竞争的日益激烈,越来越多的上市公司陷入了财务困境中,为了帮助上市公司及投资者、债权人、政府等相关利益者更好的区别公司是否存在发生财务困境的可能性,建立科学有效的财务预警机制有着重大意义。本文主要采用了实证分析方法,从深、沪A股上市公司中选取了2009年首次被ST的上市公司及其配对公司作为训练样本,在传统财务指标的基础上,引入了公司治理指标、股本结构指标、股票市场表现指标、审计报告意见类型指标和EVA因子,构建了一个信息相对全面的财务预警指标体系。运用训练样本公司2007-2008年的数据为基础,首先对除了EVA以外变量进行非参数检验,再将通过显著性检验的变量用因子分析法提取因子,并运用这些因子构建Logistic模型,同时引入EVA因子后再构建预警模型,然后选取2010年被首次ST的上市公司和配对公司作为检验样本,对构建的模型进行有效性检验。通过实证研究表明EVA能有效提高财务预警的能力。
【Abstract】 With the increasingly fierce market competition, more and more listed companies fall into financial distress, in order to help the listed companies and investors, creditors, government and other stakeholders to better distinguish whether the company is in financial difficulties, establishing a scientific and effective financial early-warning mechanism is of great significance.This paper uses empirical analysis, selects 2009 year ST listed companies for the first time and their matching companies as the training sample from the Shanghai and Shenzhen A-share listed companies, selects indicators on the basis of financial indicators, introduces the corporate governance indicators, share capital structure indicators, stock market performance indicators, audit report opinions type indicator, also joined the EVA factor, builds a relatively comprehensive financial information system of early-warning indicators. Based on data of the training sample companies for 2007-2008, firstly adopting non-parametric method to test the significance of the sample’s variables in addition to EVA, using the factor analysis extracts common factors from significant variables, then establishing Logistic models, while adding EVA factor to bulid models again. Then, selecting the 2010 year ST listed companies and their matching companies as the test samples, testing the effectiveness of the models.The empirical research shows that EVA can improve the ability of the financial early-warning.
【Key words】 Financial distress; The financial early-warning model; Economic Value Added; Logistic Model;