基于面板数据的上市公司财务困境预测
摘要
在市场经济条件下,企业的生产经营充满着风险。激烈的市场竞争带给企业的不仅仅是机遇,还有挑战。风险是客观存在的,如果事态发展的结果对企业不利,又得不到及时挽救,企业就不可避免地陷入困境。而企业一旦陷入困境,就会给投资者、债权人、经营者乃至国家等各利益主体造成非常不利的影响。如果企业能够及时发现其财务状况出现的异常,采取措施阻止财务状况的进一步恶化,对于投资者、债权人、企业以及政府管理部门都具有极其重要的现实意义。
本文在系统研究了国内外关于财务困境预测方面的理论和方法的基础上,以我国的上市公司为研究对象,采用规范研究与实证研究相结合的方法,构建了财务预警的理论框架,分析了企业财务困境形成的原因,总结了我国财务困境公司的分布特征,论证了行业因素对财务困境风险的具体影响,构建了财务困境预警的指标体系,并分别在财务状况二分类和多分类的基础上构建了财务困境预测模型,检验了各自的预测效果,形成了一套较为合理的财务困境预测体系。论文的主要研究工作如下：
1.分析了论文选题的背景,阐述了财务困境预测研究的理论和现实意义,并在对国内外关于财务困境预测的文献进行综述的基础上总结了目前研究中普遍存在的问题。
2.构建了财务困境预测的理论框架。首先提出了本文对财务困境概念的理论和实证界定,进而分析了导致企业财务困境形成的内、外部原因,剖析了财务预警的理论依据,最后讨论了财务困境预测的定性和定量方法,对每一种方法的基本思想进行了介绍,并对各自的优缺点进行了总结。
3.分别从行业、区域、资产规模和生存时间四个角度统计了困境公司的分布情况,统计结果表明在这四个方面困境公司的分布都表现出了一定的差异性。从行业分布来看,风险较高的是综合业、传播与文化产业、信息技术业和农林牧渔业,风险处于中等水平的是制造业、房地产业、批发和零售贸易、社会服务业,风险较低的是采掘业、电力、煤气及水的生产和供应业、建筑业和交通运输、仓储业；从区域分布来看,海南、山西、广西、青海、陕西属于高风险区域,甘肃、重庆、黑龙江、宁夏、河北、湖北、辽宁、天津、吉林、新疆、西藏、湖南属于中风险区域,深圳、四川、山东、云南、广东、上海、河南、北京、内蒙古、贵州、福建、江苏、江西、浙江、安徽属于低风险区域；从资产规模分布来看,2亿以下属于极高风险资产规模,2亿-10亿属于高风险规模,10亿-20亿属于中等风险规模,20亿以上属于低风险资产规模；从生存时间分布来看,上市1-2年属于初步稳定期,3-6年属于风险加剧期,7-10年属于高危期,11-15年属于风险降低期,15年以上属于稳定期。
4.分析了不同行业财务指标存在差异的理论依据,然后对2005-2009年10个行业的10个财务指标进行了Kruskal-Wallis H非参数检验,结果表明除了净利润增长率和净资产增长率在部分年份接受零假设以外,其余指标均拒绝零假设,即存在显著的行业差异；接着采用Kendall W协同系数进一步检验行业差异的稳定性,结果显示除了净利润增长率和净资产增长率在有些时间跨度上接受原假设外,其余的财务比率均在5%水平上拒绝原假设,即这些比率的行业差异具有一定的稳定性,而且从不同的时间跨度来检验均得到了一致的结果。最后,选择制造业中样本量最大的两个次级行业机械、设备、仪表行业和石油、化学、塑胶、塑料行业为研究对象,采用Cox模型对行业是否是财务困境风险的影响因素进行研究。将代表行业的虚拟变量纳入Cox模型的解释变量中,结果显示虚拟变量系数显著,说明处于不同行业的公司所面临的财务困境风险是不同的,本文中的石油、化学、塑胶、塑料行业的总体财务困境风险是机械、设备、仪表行业的1.857倍。
5.选择制造业中样本量较大的石油、化学、塑胶、塑料子行业的上市公司作为研究对象,对31个初选指标进行Mann-Whitney U检验,剔除了均值差异不显著的6个变量,用剩余的25个变量进行因子分析,然后将得到的9个因子作为解释变量来建立Panel Logit模型。实证结果表明,盈利因子、偿债因子、成长因子是减缓上市公司陷入财务困境的因素,审计意见的类型也是影响财务困境风险的重要因素。同时,利用Panel Logit模型可以反映企业陷入财务困境的可能性与各因子的数量关系,因为企业的财务状况由正常发展到困境是一个逐步演变的过程,导致这种演变的各种因素也是不断变化的,用横截面数据建立的静态模型不能反映这个动态变化的过程,而面板数据模型可以弥补这一不足。
6.将正常公司细分为健康公司和亚健康公司,总体样本划分为三类(健康样本、亚健康样本和困境样本),运用面板数据建立了SVM多分类模型。在变量的筛选方面,选择了基于平均影响值的M1V方法。实证结果表明,经过变量筛选以后建立的模型预测能力较好,能够以较少的特征变量实现较高的分类精度。一方面说明基于平均影响值的变量筛选方法是切实有效的,另一方面也说明支持向量机模型具有良好的泛化能力,即对于训练集数据和测试集数据都能得到较高的分类准确率。
本文运用统计学和人工智能的相关方法,采用面板数据对我国上市公司的财务困境风险进行了较为全面的研究,得出了很多有意义的结论。但是由于本人水平有限,论文的研究仍然存在着很多局限和有待深入的地方,这些问题还有待于在进一步的研究中不断克服和改进。
In the market economy, the production and operations is full of risks.Competition not only brings enterprises opportunities, more of challenge. In the intense market competition,some enterprises will fall into financial distress inevitably,even a crisis of survival.Once enterprises fall into financial distress,which will impact on investors, creditors,enterprises and the government.If we can predict the likelihood of financial distress correctly,that will be practical significance for protecting the interests of investors and creditors, Operators'preventing the financial crisis, Government departments' monitoring quality of listed companies and stock market risk.
Based on the study of domestic and foreign theories and methods of financial distress prediction,the thesis selects chinese listed companies as objects of study and utilizes methods of normative research and empirical research's combination.,then the thesis establishes the theoretical framework of financial early warning and analyzes the reasons for financial distress.Furthermore,it summarizes the distribution of chinese failed companies and proves the industry differences is an important factor that affects financial distress prediction.Lastly,the thesis builds indicators of financial distress prediction and constructs the financial distress predicting models on the basis of binary classification and multi-classification respectivly and tests the model prediction,then a new system of financial distress prediction forms The main research is as followed.
1.The thesis analyzes the background of this topic and elaborates the theoretical and practical significance of the research on financial distress prediction. Then the common problems in the present study are summrized on the basis of reviewing a lot of foreign and Chinese literatures about financial distress prediction.
2.The thesis builds a theoretical framework of financial distress prediction. Firstly, the defining principle is proposed based on the research of foreign and Chinese defining methods of financial distress.Then the reasons for financial distress are analyzed and the theory of financial warning is explained.Lastly,the methods of financial distress prediction are discussed from the qualitative and quantitative points. For each method the basic idea is introduced and the advantages and disadvantages are summrized.
3.The statistical distribution of the failed companies are gathered from the industries, regions, assets and survival time points of view respectively. Results show that the distribution of companies is different in each area.From the industry distribution, comprehensive industry, communication and cultural industry,information technology industry and agriculture, forestry, animal husbandry and fishery industry are high-risk.Menufacturing industry,real estate industry,wholesale and retail trade industry and social services industry are medium-risk.Mining industry, Electricity, gas and water production and supply industry,building industry and transportation and warehousing industry are low-risk.From the regional distribution, Hainan, Shanxi, Guangxi, Qinghai, Shaanxi are high-risk areas. Gansu, Chongqing, Heilongjiang, Ningxia, Hebei, Hubei, Liaoning, Tianjin, Jilin, Xinjiang, Tibet, and Hunan are medium-risk areas. Shenzhen, Sichuan, Shandong, Yunnan, Guangdong, Shanghai, Henan, Beijing, Inner Mongolia, Guizhou, Fujian, Jiangsu, Jiangxi, Zhejiang and Anhui are low-risk areas.From the assets distribution,the scale below200million is very high-risk.200million-1billion is a high-risk scale.1billion-2billion is a medium-risk scale and the scale more than2billion is low-risk. From the survival time distribution,1-2years after listing is a initial stabilization period.3-6yeears is a period of increased risk.7-10years is a high-risk period.10-15years is a period of risk reduction and more than15years is a stable period.
4.The thesis analyzes the theoretical basis for the differences of financial indicators in different industries and makes the non-parametric Kruskal-Wallis H test for10financial indicators during2005-2009from10industries.Results show that all the indicators reject the null hypothesis except net profit growth rate and net assets growth rate's accepting the null hypothesis in some years,which means there are significant differences in defferent industry.In order to test the stability of differences in industry,we made the Kendall W test. The results show that all the financial ratios reject the null hypothesis in the5%level except net profit growth rate and net assets growth rate's accepting the nulll hypothesis in some time span.That means these financial ratios have considerable stability in the industry differences and the consistent test results are obtained from different time span.Lastly, the thesis selects the two biggest subordinate industries:machinery, equipmentandinstrument industry and oil,chemical, plastic industry in manufacturing for study.The Cox models are built to test industry difference is a factor that affects financial risk and a dummy variable on behalf of industry differences is used in the Cox model with mixed samples.. Empirical results show that the dummy variable coefficient is significant, which indicates that companies in different industries are facing different financial risk.In this research, the risk of financial distress in the oil,chemical, plastic industry is1.857times in the machinery, equipment and instrument industry.
5. The thesis selects the oil,chemical, plastic industry in manufacturing for study,which is a bigger subordinate industry.Firstly, the Mann-Whitney U test is made for31primary indicators and6viables whose mean difference are not significant are excluded.Then factor analysis is made for the remaining25variables and we select the9factors from factor analysis as explanatory variables to build the Panel Logit model. Empirical results show that profitability factor, debt factor and growth factor are factors that can prevent the listed companies from falling into financial distress and the type of audit opinion is also an important factor.At the same time, the Panel Logit model can describe the relationship between the probability of financial distress and the factors. Financial distress is an asymptotic process and the factors that affect the enterprises'financial situation are constantly changing. So the static econometric models based on cross-sectional financial data can't reflect thedynamic process,while the Panel Logit model can just make up the shortfall.
6.The thesis devides the normal companies into two categories:financial healthy companies and financial sub-healthy companies,so the listed companies are classified into three categories:financial healthy companies, financial sub-healthy companies and financial distress companies.Then the SVM multi-classifier model is constructed with panel data. In variable selection, the method of Mean Impact Value is used. The empirical results indicate that the financial distress early warning model after variable selction has good forecast ability, which can give a higher classification accuracy by a fewer characteristics variables.On the one hand,it means that the MIV method is feasible and effective on variable selection.On the other hand,it means that SVM has high generalizing ability,which can get a high classification accuracy not only for the training set data but also the test set data.
The thesis makes a more comprehensive research on the financial distress prediction of chinese listed companies with panel data and the methods of statistic and artificial intelligence and gets lots of meaningful conclusions.But because of my limited ability,this thesis still has its limitation and it still needs to be further researched in some places,which I will overcome and improve in my future research.
本文在系统研究了国内外关于财务困境预测方面的理论和方法的基础上,以我国的上市公司为研究对象,采用规范研究与实证研究相结合的方法,构建了财务预警的理论框架,分析了企业财务困境形成的原因,总结了我国财务困境公司的分布特征,论证了行业因素对财务困境风险的具体影响,构建了财务困境预警的指标体系,并分别在财务状况二分类和多分类的基础上构建了财务困境预测模型,检验了各自的预测效果,形成了一套较为合理的财务困境预测体系。论文的主要研究工作如下：
1.分析了论文选题的背景,阐述了财务困境预测研究的理论和现实意义,并在对国内外关于财务困境预测的文献进行综述的基础上总结了目前研究中普遍存在的问题。
2.构建了财务困境预测的理论框架。首先提出了本文对财务困境概念的理论和实证界定,进而分析了导致企业财务困境形成的内、外部原因,剖析了财务预警的理论依据,最后讨论了财务困境预测的定性和定量方法,对每一种方法的基本思想进行了介绍,并对各自的优缺点进行了总结。
3.分别从行业、区域、资产规模和生存时间四个角度统计了困境公司的分布情况,统计结果表明在这四个方面困境公司的分布都表现出了一定的差异性。从行业分布来看,风险较高的是综合业、传播与文化产业、信息技术业和农林牧渔业,风险处于中等水平的是制造业、房地产业、批发和零售贸易、社会服务业,风险较低的是采掘业、电力、煤气及水的生产和供应业、建筑业和交通运输、仓储业；从区域分布来看,海南、山西、广西、青海、陕西属于高风险区域,甘肃、重庆、黑龙江、宁夏、河北、湖北、辽宁、天津、吉林、新疆、西藏、湖南属于中风险区域,深圳、四川、山东、云南、广东、上海、河南、北京、内蒙古、贵州、福建、江苏、江西、浙江、安徽属于低风险区域；从资产规模分布来看,2亿以下属于极高风险资产规模,2亿-10亿属于高风险规模,10亿-20亿属于中等风险规模,20亿以上属于低风险资产规模；从生存时间分布来看,上市1-2年属于初步稳定期,3-6年属于风险加剧期,7-10年属于高危期,11-15年属于风险降低期,15年以上属于稳定期。
4.分析了不同行业财务指标存在差异的理论依据,然后对2005-2009年10个行业的10个财务指标进行了Kruskal-Wallis H非参数检验,结果表明除了净利润增长率和净资产增长率在部分年份接受零假设以外,其余指标均拒绝零假设,即存在显著的行业差异；接着采用Kendall W协同系数进一步检验行业差异的稳定性,结果显示除了净利润增长率和净资产增长率在有些时间跨度上接受原假设外,其余的财务比率均在5%水平上拒绝原假设,即这些比率的行业差异具有一定的稳定性,而且从不同的时间跨度来检验均得到了一致的结果。最后,选择制造业中样本量最大的两个次级行业机械、设备、仪表行业和石油、化学、塑胶、塑料行业为研究对象,采用Cox模型对行业是否是财务困境风险的影响因素进行研究。将代表行业的虚拟变量纳入Cox模型的解释变量中,结果显示虚拟变量系数显著,说明处于不同行业的公司所面临的财务困境风险是不同的,本文中的石油、化学、塑胶、塑料行业的总体财务困境风险是机械、设备、仪表行业的1.857倍。
5.选择制造业中样本量较大的石油、化学、塑胶、塑料子行业的上市公司作为研究对象,对31个初选指标进行Mann-Whitney U检验,剔除了均值差异不显著的6个变量,用剩余的25个变量进行因子分析,然后将得到的9个因子作为解释变量来建立Panel Logit模型。实证结果表明,盈利因子、偿债因子、成长因子是减缓上市公司陷入财务困境的因素,审计意见的类型也是影响财务困境风险的重要因素。同时,利用Panel Logit模型可以反映企业陷入财务困境的可能性与各因子的数量关系,因为企业的财务状况由正常发展到困境是一个逐步演变的过程,导致这种演变的各种因素也是不断变化的,用横截面数据建立的静态模型不能反映这个动态变化的过程,而面板数据模型可以弥补这一不足。
6.将正常公司细分为健康公司和亚健康公司,总体样本划分为三类(健康样本、亚健康样本和困境样本),运用面板数据建立了SVM多分类模型。在变量的筛选方面,选择了基于平均影响值的M1V方法。实证结果表明,经过变量筛选以后建立的模型预测能力较好,能够以较少的特征变量实现较高的分类精度。一方面说明基于平均影响值的变量筛选方法是切实有效的,另一方面也说明支持向量机模型具有良好的泛化能力,即对于训练集数据和测试集数据都能得到较高的分类准确率。
本文运用统计学和人工智能的相关方法,采用面板数据对我国上市公司的财务困境风险进行了较为全面的研究,得出了很多有意义的结论。但是由于本人水平有限,论文的研究仍然存在着很多局限和有待深入的地方,这些问题还有待于在进一步的研究中不断克服和改进。
In the market economy, the production and operations is full of risks.Competition not only brings enterprises opportunities, more of challenge. In the intense market competition,some enterprises will fall into financial distress inevitably,even a crisis of survival.Once enterprises fall into financial distress,which will impact on investors, creditors,enterprises and the government.If we can predict the likelihood of financial distress correctly,that will be practical significance for protecting the interests of investors and creditors, Operators'preventing the financial crisis, Government departments' monitoring quality of listed companies and stock market risk.
Based on the study of domestic and foreign theories and methods of financial distress prediction,the thesis selects chinese listed companies as objects of study and utilizes methods of normative research and empirical research's combination.,then the thesis establishes the theoretical framework of financial early warning and analyzes the reasons for financial distress.Furthermore,it summarizes the distribution of chinese failed companies and proves the industry differences is an important factor that affects financial distress prediction.Lastly,the thesis builds indicators of financial distress prediction and constructs the financial distress predicting models on the basis of binary classification and multi-classification respectivly and tests the model prediction,then a new system of financial distress prediction forms The main research is as followed.
1.The thesis analyzes the background of this topic and elaborates the theoretical and practical significance of the research on financial distress prediction. Then the common problems in the present study are summrized on the basis of reviewing a lot of foreign and Chinese literatures about financial distress prediction.
2.The thesis builds a theoretical framework of financial distress prediction. Firstly, the defining principle is proposed based on the research of foreign and Chinese defining methods of financial distress.Then the reasons for financial distress are analyzed and the theory of financial warning is explained.Lastly,the methods of financial distress prediction are discussed from the qualitative and quantitative points. For each method the basic idea is introduced and the advantages and disadvantages are summrized.
3.The statistical distribution of the failed companies are gathered from the industries, regions, assets and survival time points of view respectively. Results show that the distribution of companies is different in each area.From the industry distribution, comprehensive industry, communication and cultural industry,information technology industry and agriculture, forestry, animal husbandry and fishery industry are high-risk.Menufacturing industry,real estate industry,wholesale and retail trade industry and social services industry are medium-risk.Mining industry, Electricity, gas and water production and supply industry,building industry and transportation and warehousing industry are low-risk.From the regional distribution, Hainan, Shanxi, Guangxi, Qinghai, Shaanxi are high-risk areas. Gansu, Chongqing, Heilongjiang, Ningxia, Hebei, Hubei, Liaoning, Tianjin, Jilin, Xinjiang, Tibet, and Hunan are medium-risk areas. Shenzhen, Sichuan, Shandong, Yunnan, Guangdong, Shanghai, Henan, Beijing, Inner Mongolia, Guizhou, Fujian, Jiangsu, Jiangxi, Zhejiang and Anhui are low-risk areas.From the assets distribution,the scale below200million is very high-risk.200million-1billion is a high-risk scale.1billion-2billion is a medium-risk scale and the scale more than2billion is low-risk. From the survival time distribution,1-2years after listing is a initial stabilization period.3-6yeears is a period of increased risk.7-10years is a high-risk period.10-15years is a period of risk reduction and more than15years is a stable period.
4.The thesis analyzes the theoretical basis for the differences of financial indicators in different industries and makes the non-parametric Kruskal-Wallis H test for10financial indicators during2005-2009from10industries.Results show that all the indicators reject the null hypothesis except net profit growth rate and net assets growth rate's accepting the null hypothesis in some years,which means there are significant differences in defferent industry.In order to test the stability of differences in industry,we made the Kendall W test. The results show that all the financial ratios reject the null hypothesis in the5%level except net profit growth rate and net assets growth rate's accepting the nulll hypothesis in some time span.That means these financial ratios have considerable stability in the industry differences and the consistent test results are obtained from different time span.Lastly, the thesis selects the two biggest subordinate industries:machinery, equipmentandinstrument industry and oil,chemical, plastic industry in manufacturing for study.The Cox models are built to test industry difference is a factor that affects financial risk and a dummy variable on behalf of industry differences is used in the Cox model with mixed samples.. Empirical results show that the dummy variable coefficient is significant, which indicates that companies in different industries are facing different financial risk.In this research, the risk of financial distress in the oil,chemical, plastic industry is1.857times in the machinery, equipment and instrument industry.
5. The thesis selects the oil,chemical, plastic industry in manufacturing for study,which is a bigger subordinate industry.Firstly, the Mann-Whitney U test is made for31primary indicators and6viables whose mean difference are not significant are excluded.Then factor analysis is made for the remaining25variables and we select the9factors from factor analysis as explanatory variables to build the Panel Logit model. Empirical results show that profitability factor, debt factor and growth factor are factors that can prevent the listed companies from falling into financial distress and the type of audit opinion is also an important factor.At the same time, the Panel Logit model can describe the relationship between the probability of financial distress and the factors. Financial distress is an asymptotic process and the factors that affect the enterprises'financial situation are constantly changing. So the static econometric models based on cross-sectional financial data can't reflect thedynamic process,while the Panel Logit model can just make up the shortfall.
6.The thesis devides the normal companies into two categories:financial healthy companies and financial sub-healthy companies,so the listed companies are classified into three categories:financial healthy companies, financial sub-healthy companies and financial distress companies.Then the SVM multi-classifier model is constructed with panel data. In variable selection, the method of Mean Impact Value is used. The empirical results indicate that the financial distress early warning model after variable selction has good forecast ability, which can give a higher classification accuracy by a fewer characteristics variables.On the one hand,it means that the MIV method is feasible and effective on variable selection.On the other hand,it means that SVM has high generalizing ability,which can get a high classification accuracy not only for the training set data but also the test set data.
The thesis makes a more comprehensive research on the financial distress prediction of chinese listed companies with panel data and the methods of statistic and artificial intelligence and gets lots of meaningful conclusions.But because of my limited ability,this thesis still has its limitation and it still needs to be further researched in some places,which I will overcome and improve in my future research.
引文
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