上市公司财务危机预测研究新探索
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摘要
自1990年12月19日和1991年7月3日上海证券交易所和深圳证券交易所分别成立,并提供股票交易服务以来,中国证券市场经历了快速发展的历程。在这快速发展过程中,因发生财务危机而被特别处理的上市公司数目也在逐渐增加。’为了能够保护利益相关者的权益、增强企业抵御风险的能力,对上市公司财务危机预测的研究成了财务理论界和实务界关注的焦点。企业财务危机预测研究作为企业经营的指示灯,不仅具有较高的学术价值,而且有着很大的现实意义。
企业财务危机的预测研究是通过筛选有信息含量的指标,借助一定的分析方法构建预测模型。其中,如何准确、合理的选择预测指标和方法是企业财务危机预测研究的关键,然而传统的指标筛选法(如T检验、逐步回归等)和预测模型存在一定缺陷,阻碍了企业财务危机预测研究的进一步深入。出于对这些问题的浓厚兴趣和对其应用价值的关注,本文在对国内外文献回顾和总结的基础上,通过构造企业远期偿债能力的度量指标ω,通过引入信号噪音差方法筛选预测指标,通过对朴素贝叶斯分析法的改进等几种新的现代技术方法,构建企业财务危机的线性与非线性预测模型,并通过比较模型预测准确率的高低,来对模型进行比较。
本文以制造业上市公司为代表,借助SAS统计软件和Data Miner数据挖掘软件,构造不同方法下的企业财务危机预测模型进行研究。本文通过考察传统的指标筛选方法(T检验和逐步回归),得出T检验的缺陷在于不能给出单个指标的具体预测价值,并且T检验筛选出的预测指标数量偏多,影响了模型构建的效果,增大模型的使用成本。逐步回归方法的缺陷在于估计所得的指标系数无法说明指标和预测结果的关系,仍然没能给出单个指标的预测信息量,本文引入的信号噪音差方法解决了这些问题。文章同时也利用由逐步回归筛选的指标和信号噪音差所选指标分别构建了多元线性判别分析模型和朴素贝叶斯模型,实证结果显示信号噪音差所选指标构造的模型预测精度高于逐步回归筛选指标所建模型,并且信号噪音差所选指标构造的决策树模型具有较高的预测能力,肯定了信号噪音差在指标筛选方面的优势。鉴于以往研究一般利用公司的横截面数据来构建模型,不同的研究样本和研究数据可能得出不同的预测变量,造成预测模型的外推能力不强,并且难以反映企业从财务正常到财务危机的渐进过程。本文通过分析企业陷入财务危机的原因,提出一个新的预测变量,并利用财务报表时间序列数据和贝叶斯推断方法估计该变量,由该变量所构建的单变量预测模型和多变量预测模型都取得较好的预测准确率,为财务危机的预测研究提供了参考。
Since December 19,1990 and July 3,1991,the Shanghai Stock Exchange and Shenzhen Stock Exchange set up to provide stock trading service,the Chinese security market has experienced rapid development course. In this rapid development process,the number of listed companies with special treatment has been increasing,because of financial crisis.In order to protect the interests of stakeholders, and enhance listed company's ability to resist risk of financial crisis,the research of listed company's financial crisis prediction model has been the focus of theory and practition community. As the indicator of business operation, the research of financial crisis not only has high academic value, but also has great practical significance.
Prediction of enterprise financial crisis is through the selection of indicators with information content, using certain methods to build prediction models.Among them how accurately choose the predictor and analysis method is the key to the research, however, the traditional indicators of screening methods (such as T tests,stepwise regression, etc.) and prediction models have some defects,hindering further development of enterprise financial crisis prediction.For the interest on these two issues and concern about the value of their applications,based on the review and sum up of the literature at home and abroad,this paper constructed a measure of corporate long—term solvency indicator,introducted signal-noise method and through the improvement of Naive Bayesian analysis,etc.Building linear and nonlinear prediction models,and comparing level of prediction accuracy of each model.
This paper take manufacturing sector as example, using SAS statistical software arid the Data Miner software,constructed different financial crisis prediction models.-In this paper,by screening traditional indicator choosing methods (T test and stepwise regression),finding defects in T test are that it can not give a single indicator specific predictive value,and with huge number indicators,affecting the model building effect,increasing the cost of model using.The drawback of stepwise regression method derived from the indicators estimated coefficients can not account for the relationship between targets and indicators,meanwhile still unable to predict the amount of information given in a single indicator,this paper introduces the signal to noise ananlysis to solve these problems.The article also used indicators selected by the stepwise regression and signal to noise analysis constructing multivariate linear discriminant analysis model and a naive Bayesian model, empirical results show that the prediction accuracy of models using indicators selected by signal to noise analysis is higher than that of stepwise regression,and the decision tree model constructed by indicators selected by signal to noise analysis has a high predictive ability,confirmed the signal to noise analysis'advantage. Past studies have generally used the company's cross-sectional data to build models,different study samples and research data may get different predictor variables,resulting in poor extrapolation power of predictive models,and is difficult to reflect the gradual process for business changing to financial crisis.This paper analyzes the reasons for companies in financial crisis,proposing a new predictor variable,and the use of financial statements for time-series data and Bayesian inference methods to estimate the variable,the single-variable forecasting models and multi-variable forecasting models constructed by it achieving higher prediction accuracy,providing a reference for financial crisis prediction research.
企业财务危机的预测研究是通过筛选有信息含量的指标,借助一定的分析方法构建预测模型。其中,如何准确、合理的选择预测指标和方法是企业财务危机预测研究的关键,然而传统的指标筛选法(如T检验、逐步回归等)和预测模型存在一定缺陷,阻碍了企业财务危机预测研究的进一步深入。出于对这些问题的浓厚兴趣和对其应用价值的关注,本文在对国内外文献回顾和总结的基础上,通过构造企业远期偿债能力的度量指标ω,通过引入信号噪音差方法筛选预测指标,通过对朴素贝叶斯分析法的改进等几种新的现代技术方法,构建企业财务危机的线性与非线性预测模型,并通过比较模型预测准确率的高低,来对模型进行比较。
本文以制造业上市公司为代表,借助SAS统计软件和Data Miner数据挖掘软件,构造不同方法下的企业财务危机预测模型进行研究。本文通过考察传统的指标筛选方法(T检验和逐步回归),得出T检验的缺陷在于不能给出单个指标的具体预测价值,并且T检验筛选出的预测指标数量偏多,影响了模型构建的效果,增大模型的使用成本。逐步回归方法的缺陷在于估计所得的指标系数无法说明指标和预测结果的关系,仍然没能给出单个指标的预测信息量,本文引入的信号噪音差方法解决了这些问题。文章同时也利用由逐步回归筛选的指标和信号噪音差所选指标分别构建了多元线性判别分析模型和朴素贝叶斯模型,实证结果显示信号噪音差所选指标构造的模型预测精度高于逐步回归筛选指标所建模型,并且信号噪音差所选指标构造的决策树模型具有较高的预测能力,肯定了信号噪音差在指标筛选方面的优势。鉴于以往研究一般利用公司的横截面数据来构建模型,不同的研究样本和研究数据可能得出不同的预测变量,造成预测模型的外推能力不强,并且难以反映企业从财务正常到财务危机的渐进过程。本文通过分析企业陷入财务危机的原因,提出一个新的预测变量,并利用财务报表时间序列数据和贝叶斯推断方法估计该变量,由该变量所构建的单变量预测模型和多变量预测模型都取得较好的预测准确率,为财务危机的预测研究提供了参考。
Since December 19,1990 and July 3,1991,the Shanghai Stock Exchange and Shenzhen Stock Exchange set up to provide stock trading service,the Chinese security market has experienced rapid development course. In this rapid development process,the number of listed companies with special treatment has been increasing,because of financial crisis.In order to protect the interests of stakeholders, and enhance listed company's ability to resist risk of financial crisis,the research of listed company's financial crisis prediction model has been the focus of theory and practition community. As the indicator of business operation, the research of financial crisis not only has high academic value, but also has great practical significance.
Prediction of enterprise financial crisis is through the selection of indicators with information content, using certain methods to build prediction models.Among them how accurately choose the predictor and analysis method is the key to the research, however, the traditional indicators of screening methods (such as T tests,stepwise regression, etc.) and prediction models have some defects,hindering further development of enterprise financial crisis prediction.For the interest on these two issues and concern about the value of their applications,based on the review and sum up of the literature at home and abroad,this paper constructed a measure of corporate long—term solvency indicator,introducted signal-noise method and through the improvement of Naive Bayesian analysis,etc.Building linear and nonlinear prediction models,and comparing level of prediction accuracy of each model.
This paper take manufacturing sector as example, using SAS statistical software arid the Data Miner software,constructed different financial crisis prediction models.-In this paper,by screening traditional indicator choosing methods (T test and stepwise regression),finding defects in T test are that it can not give a single indicator specific predictive value,and with huge number indicators,affecting the model building effect,increasing the cost of model using.The drawback of stepwise regression method derived from the indicators estimated coefficients can not account for the relationship between targets and indicators,meanwhile still unable to predict the amount of information given in a single indicator,this paper introduces the signal to noise ananlysis to solve these problems.The article also used indicators selected by the stepwise regression and signal to noise analysis constructing multivariate linear discriminant analysis model and a naive Bayesian model, empirical results show that the prediction accuracy of models using indicators selected by signal to noise analysis is higher than that of stepwise regression,and the decision tree model constructed by indicators selected by signal to noise analysis has a high predictive ability,confirmed the signal to noise analysis'advantage. Past studies have generally used the company's cross-sectional data to build models,different study samples and research data may get different predictor variables,resulting in poor extrapolation power of predictive models,and is difficult to reflect the gradual process for business changing to financial crisis.This paper analyzes the reasons for companies in financial crisis,proposing a new predictor variable,and the use of financial statements for time-series data and Bayesian inference methods to estimate the variable,the single-variable forecasting models and multi-variable forecasting models constructed by it achieving higher prediction accuracy,providing a reference for financial crisis prediction research.
引文
[1]陈晓,陈治鸿.中国上市公司的财务困境预测[J].中国会计与财务研究,2000：第3期.
[2]陈静,上市公司财务恶化预测的实证分析[J].会计研究,1999：4月.
[3]谷祺,于东智.公司治理、董事会行为与经营绩效[J].财经问题研究,2001：第1期.
[4]洪光宏.以经营效率观点建立台湾信息电子业财务危机预警模型[D].台湾逢甲大学未发表论文,2006.
[5]李秉成,扬七中,刘芬芳.上市公司财务困境比率征兆研究[J].财会通讯,2004：第3期.
[6]刘习勇,试论现金流量制财务指标体系的构建[J].甘肃省经济管理干部学院学报：2001：第4期
[7]刘红霞,上市公司投资风险的判别分析[J].投资研究,2004：第9期
[8]潘玉叶,台湾企业财务危机形成历程中财务信息变迁研究[J].台北市银月刊,1992：第23卷第7期
[9]彭寿康,上市公司远期财务危机预测研究[J].数理统计与管理,2005：第4期
[10]彭寿康,会计信息的数据挖掘方法与银行信贷风险预测[J].商业经济与管理,2008：第3期
[11]邵建利.SAS应用统计实验[M].上海财经大学出版社,2006：10月
[12]管鸿禧.我国中小企业竞争力研究[M].中国经济出版社,2007年5月第1版,204-206
[13]吴世农,傅荣.我国上市公司经营失败风险的判定分析——BP神经网络模型和Fisher多类线性判定模型[J].东南学术,2002：第2期
[14]吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].经济研究,2001：第1期
[15]王莉莉,刘同明等.基于决策树方法的银行客户信用评估[J].江南大学学报,2007：第6期
[16]伍忠贤,财务危机的侦测与预防[J].能力杂志,2001：第539卷
[17]鲜文铎,金延华.基于Probit模型的上市公司财务失败实证研究[J].经济与管理,2007：第10期
[18]杨华,上市公司财务危机预警模型效果比较研究[J].中国计量学院学报,2006：第1期
[19]叶鸿祥.台湾上市信息电子产业危机预警之研究：DEA与Logit模型之应用[D].台湾南华大学未出版论文,2006.
[20]张延波,彭淑雄.财务风险监测与危机预警[J].北京工商大学学报(社会科学版),2002： 第5期
[21]周兵,张军.上市公司财务状况恶化的预警模型分析[J].华东经济管理,2002：第8期
[22]张大成,周丽娟,黄筱雯.经营效率与企业危机相关性研究[J].信用资讯月刊,2004：2月
[23]张玲,杜庆宣.基于分类回归树算法的上市公司会计信息失真识别研究[J].财经理论与实践,2009：第3期
[24]朱顺泉,上市公司财务困境预警判别分析模型的建立及应用研究[J].中国管理信息,2006年：第3期
[25]Amiyatosh Purnanandam.:Financial distress and corporate risk management Theory and evidence. Journal of Financial Economics,2007 (3):706-739.
[26]Altman E, J. Hartzell, and M. Peck. Emerging markets corporate bonds:a scoring system[M].Salomon Brothers Inc,NewYork.1995.
[27]Beaver, w. H. Financial Ration as predictors of Failure[J]. Journal of Accounting Research, 1966:71-102
[28]Car mic hael, D. R,1972.The Auditor's Reporting Obligation, Auditing Research Monograph No.1 (New York:AICPA),1994.
[29]Deakin, E. B. A Discriminant Analysis of Prodictors of Failure.Journal of accounting Research,vol,1972:9, PP.167-179
[30]Fama, E.F., Jensen, M.C.Separation of Ownership Control [J] Journal of Law andEconomics, 1983:01-325
[31]H. Li, J. Sun.Ranking-Order Case-Based Reasoning for Financial Distress Prediction.Knowledge-Based Systems, doi:10.1016/j.knosys.2008.03.047.
[32]Marc Blum.Failing Company Discriminant Analysis[J].Journal of AccountingResearch, 1974:Spring
[33]Myers, S. and Majluf, N,Corporate Financing and Investment Decisions When Firms Have Information that Investors Do Not Have[J].Journal of Financial Economics,13,.1984: 187-221
[34]StephenA. Ross & Jeffey FJaffe.Corporate Finance.2nd. Homewood, IL.1990:PP.420-424
[35]Sun, J.,Li, H..Listed companies'financial distress prediction based on Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications,doi:10.1016/j.eswa.07.045.2007.
[36]Wu D, et al. Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis. Socio-Economic Planning Sciences.doi:10.1016/j.seps.2006.11.002.
[37]Yongsheng Ding.Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications.,2007 (4):3081-3089.
[2]陈静,上市公司财务恶化预测的实证分析[J].会计研究,1999：4月.
[3]谷祺,于东智.公司治理、董事会行为与经营绩效[J].财经问题研究,2001：第1期.
[4]洪光宏.以经营效率观点建立台湾信息电子业财务危机预警模型[D].台湾逢甲大学未发表论文,2006.
[5]李秉成,扬七中,刘芬芳.上市公司财务困境比率征兆研究[J].财会通讯,2004：第3期.
[6]刘习勇,试论现金流量制财务指标体系的构建[J].甘肃省经济管理干部学院学报：2001：第4期
[7]刘红霞,上市公司投资风险的判别分析[J].投资研究,2004：第9期
[8]潘玉叶,台湾企业财务危机形成历程中财务信息变迁研究[J].台北市银月刊,1992：第23卷第7期
[9]彭寿康,上市公司远期财务危机预测研究[J].数理统计与管理,2005：第4期
[10]彭寿康,会计信息的数据挖掘方法与银行信贷风险预测[J].商业经济与管理,2008：第3期
[11]邵建利.SAS应用统计实验[M].上海财经大学出版社,2006：10月
[12]管鸿禧.我国中小企业竞争力研究[M].中国经济出版社,2007年5月第1版,204-206
[13]吴世农,傅荣.我国上市公司经营失败风险的判定分析——BP神经网络模型和Fisher多类线性判定模型[J].东南学术,2002：第2期
[14]吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].经济研究,2001：第1期
[15]王莉莉,刘同明等.基于决策树方法的银行客户信用评估[J].江南大学学报,2007：第6期
[16]伍忠贤,财务危机的侦测与预防[J].能力杂志,2001：第539卷
[17]鲜文铎,金延华.基于Probit模型的上市公司财务失败实证研究[J].经济与管理,2007：第10期
[18]杨华,上市公司财务危机预警模型效果比较研究[J].中国计量学院学报,2006：第1期
[19]叶鸿祥.台湾上市信息电子产业危机预警之研究：DEA与Logit模型之应用[D].台湾南华大学未出版论文,2006.
[20]张延波,彭淑雄.财务风险监测与危机预警[J].北京工商大学学报(社会科学版),2002： 第5期
[21]周兵,张军.上市公司财务状况恶化的预警模型分析[J].华东经济管理,2002：第8期
[22]张大成,周丽娟,黄筱雯.经营效率与企业危机相关性研究[J].信用资讯月刊,2004：2月
[23]张玲,杜庆宣.基于分类回归树算法的上市公司会计信息失真识别研究[J].财经理论与实践,2009：第3期
[24]朱顺泉,上市公司财务困境预警判别分析模型的建立及应用研究[J].中国管理信息,2006年：第3期
[25]Amiyatosh Purnanandam.:Financial distress and corporate risk management Theory and evidence. Journal of Financial Economics,2007 (3):706-739.
[26]Altman E, J. Hartzell, and M. Peck. Emerging markets corporate bonds:a scoring system[M].Salomon Brothers Inc,NewYork.1995.
[27]Beaver, w. H. Financial Ration as predictors of Failure[J]. Journal of Accounting Research, 1966:71-102
[28]Car mic hael, D. R,1972.The Auditor's Reporting Obligation, Auditing Research Monograph No.1 (New York:AICPA),1994.
[29]Deakin, E. B. A Discriminant Analysis of Prodictors of Failure.Journal of accounting Research,vol,1972:9, PP.167-179
[30]Fama, E.F., Jensen, M.C.Separation of Ownership Control [J] Journal of Law andEconomics, 1983:01-325
[31]H. Li, J. Sun.Ranking-Order Case-Based Reasoning for Financial Distress Prediction.Knowledge-Based Systems, doi:10.1016/j.knosys.2008.03.047.
[32]Marc Blum.Failing Company Discriminant Analysis[J].Journal of AccountingResearch, 1974:Spring
[33]Myers, S. and Majluf, N,Corporate Financing and Investment Decisions When Firms Have Information that Investors Do Not Have[J].Journal of Financial Economics,13,.1984: 187-221
[34]StephenA. Ross & Jeffey FJaffe.Corporate Finance.2nd. Homewood, IL.1990:PP.420-424
[35]Sun, J.,Li, H..Listed companies'financial distress prediction based on Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications,doi:10.1016/j.eswa.07.045.2007.
[36]Wu D, et al. Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis. Socio-Economic Planning Sciences.doi:10.1016/j.seps.2006.11.002.
[37]Yongsheng Ding.Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications.,2007 (4):3081-3089.