房地产企业财务危机预警实证研究
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摘要
房地产企业财务危机预警管理能力在企业经营活动风险控制中占据重要位置。建立在我国上市公司财务数据具有高相关性和高维性等特点之上,提出了基于主成分分析与Logistic分析相结合的危机预警方法。运用逻辑回归分析方法,得到影响房地产企业财务能力的6个主成分综合因子,构建科学合理的房地产企业财务危机预警决策模型。实证研究结果表明:该模型能够最大限度减少变量信息丢失情况下实现多变量的降维处理,同时解决原始数据各变量之间彼此高度相关的不足,对于预测企业经营失效与否具有很好的识别预警和推广能力,为我国上市房地产企业制定后期发展战略决策提供有效参考。
As an important force to promote China's economic and social development,the real estate industry has developed rapidly,and the capability of financial crisis early warning management capability plays an important role in the risk control of business operations. Therefore,based on the high correlation and high dimensionality of financial data of listed companies in China,this paper proposes a crisis early warning method based on principal component analysis and logistic analysis. Before using logistic regression analysis,principal component analysis is used to achieve the purpose of dimension reduction processing while retaining the original financial data information to the utmost. Using logistic regression analysis method,the six principal component comprehensive factors affecting the financial ability of real estate enterprises are obtained,and a scientific and reasonable financial crisis early warning decision model for real estate enterprises is constructed. The empirical results show that the model can achieve multivariate dimensionality reduction processing while minimizing the loss of variable information,and at the same time solve the problem that the variables of the original data are highly correlated with each other,and it is very good for predicting the failure of enterprise operation. The identification,early warning and promotion capabilities provide an effective reference for the formulation of late-stage development strategies for listed real estate companies in China.
As an important force to promote China's economic and social development,the real estate industry has developed rapidly,and the capability of financial crisis early warning management capability plays an important role in the risk control of business operations. Therefore,based on the high correlation and high dimensionality of financial data of listed companies in China,this paper proposes a crisis early warning method based on principal component analysis and logistic analysis. Before using logistic regression analysis,principal component analysis is used to achieve the purpose of dimension reduction processing while retaining the original financial data information to the utmost. Using logistic regression analysis method,the six principal component comprehensive factors affecting the financial ability of real estate enterprises are obtained,and a scientific and reasonable financial crisis early warning decision model for real estate enterprises is constructed. The empirical results show that the model can achieve multivariate dimensionality reduction processing while minimizing the loss of variable information,and at the same time solve the problem that the variables of the original data are highly correlated with each other,and it is very good for predicting the failure of enterprise operation. The identification,early warning and promotion capabilities provide an effective reference for the formulation of late-stage development strategies for listed real estate companies in China.
引文
[1]郑晓云,李建华.房地产上市公司财务预警实证研究[J].会计之友,2015(9):72-76.
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[4]杜俊娟.我国房地产企业财务风险的预警与防范[J].会计之友,2013(10):44-47.
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[6]闫炳琪,赵月瑶,张辉.基于上市公司财务数据的企业信用风险预测Logistic模型研究[J].中国传媒大学学报(自然科学版),2016(4):36-47.
[7]吴芃,吴应宇,仲伟俊.基于单变量分析的我国上市公司两阶段财务预警模型实证研究——以纺织行业为例[J].东南大学学报(哲学社会科学版),2006(2):18-23.
[8] Li Z,Crook J,Andreeva G.Chinese companies distress prediction:an application of data envelopment analysis[J].Journal of the Operational Research Society,2014,65(3):466-479.
[9]王茜,张洪.基于信用风险模型的我国房地产上市公司财务预警[J].经济研究导刊,2016(3):100-102.
[10] Hilscher J,Wilson M I.Credit Ratings and Credit Risk:Is One Measure Enough?[J].Social Science Electronic Publishing,2016.
[11]孔梅英.我国知识型员工流失研究综述[J].企业活力,2010(3):52-55.
[12] Ohlson J A. Financial ratios and the probabilistic prediction of bankruptcy[J]. Journal of accounting research,1980,18(1):109-131.
[13] Smaranda C.Scoring Functions and Bankruptcy Prediction Models-Case Study for Romanian Companies[J].Procedia Economics&Finance,2014,10(1):217-226.
[14]王广强,张学伟.房地产企业财务危机预警模型研究——基于主成分分析和Fisher判别法[J].商,2014(2):118.
[15]余新培,卢清文.现代财务管理理论发展简述[J].审计与理财,2004(3):12-14.
[16]吴忠涛,张琅,张裕华.经济转型时期的科技型企业创新效率比较研究[J].当代经济科学,2018(3):57-65.
[2]夏群立.房地产企业工程的项目精细化管理分析[J].住宅与房地产,2018(9):10.
[3]吴君民,王蓓雯.新常态下房地产上市公司财务预警分析[J].会计师,2016(15):78-79.
[4]杜俊娟.我国房地产企业财务风险的预警与防范[J].会计之友,2013(10):44-47.
[5]杜华峰.对财务管理目标与企业财务核心能力的分析[J].财会学习,2016(8):56-58.
[6]闫炳琪,赵月瑶,张辉.基于上市公司财务数据的企业信用风险预测Logistic模型研究[J].中国传媒大学学报(自然科学版),2016(4):36-47.
[7]吴芃,吴应宇,仲伟俊.基于单变量分析的我国上市公司两阶段财务预警模型实证研究——以纺织行业为例[J].东南大学学报(哲学社会科学版),2006(2):18-23.
[8] Li Z,Crook J,Andreeva G.Chinese companies distress prediction:an application of data envelopment analysis[J].Journal of the Operational Research Society,2014,65(3):466-479.
[9]王茜,张洪.基于信用风险模型的我国房地产上市公司财务预警[J].经济研究导刊,2016(3):100-102.
[10] Hilscher J,Wilson M I.Credit Ratings and Credit Risk:Is One Measure Enough?[J].Social Science Electronic Publishing,2016.
[11]孔梅英.我国知识型员工流失研究综述[J].企业活力,2010(3):52-55.
[12] Ohlson J A. Financial ratios and the probabilistic prediction of bankruptcy[J]. Journal of accounting research,1980,18(1):109-131.
[13] Smaranda C.Scoring Functions and Bankruptcy Prediction Models-Case Study for Romanian Companies[J].Procedia Economics&Finance,2014,10(1):217-226.
[14]王广强,张学伟.房地产企业财务危机预警模型研究——基于主成分分析和Fisher判别法[J].商,2014(2):118.
[15]余新培,卢清文.现代财务管理理论发展简述[J].审计与理财,2004(3):12-14.
[16]吴忠涛,张琅,张裕华.经济转型时期的科技型企业创新效率比较研究[J].当代经济科学,2018(3):57-65.