基于盈利质量的DANP变权财务预警模型
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摘要
高质量的盈利是企业健康持续发展的基石,财务危机是财务质量的综合表现.以和谐理论为基础,从盈利质量及其各维度的均衡性两方面分析财务危机发生的机理,并依此建立基于盈利质量金字塔的财务危机指标评价体系.考虑到评价指标间存在一定程度的相互影响的现实因素,同时为避免常权综合评价模型的属性值转移,建立决策实验室法的网络层级分析法(DANP)双层惩罚变权时间序列财务预警模型以判断企业财务状况.通过上市公司数据实证,构建财务预警综合评价值的临界区间为[0.6494,0.6547],其值越小财务危机发生的可能性越大,且预测准确率达到91.53%.在理论上对财务危机发生的机理研究具有增量贡献,在实践中对企业自身财务状况的监督具有现实指导意义.
High-quality earnings is the cornerstone of the healthy and sustainable development of the company and the financial crisis is a comprehensive performance of financial quality.Based on the theory of harmony,this paper analyzes the mechanism of financial crisis from the aspects of earnings quality and the balance of its each dimension and establishes the financial crisis' evaluation index system based on earnings quality pyramid.Considering the mutual influence between the evaluation indicators and the attribute value transfer of the constant weight comprehensive model,this paper establishes the decisionmaking and trial evaluation based analytic network process(DANP)double layer penalty variable weight time series financial early-warning model to judge the financial condition.Through the empirical data of listed companies,the critical interval of the financial early warning comprehensive value is [0.6494,0.6547].Its prediction accuracy rate reaches 91.53%.The smaller the value,the greater the likelihood of the financial crisis.It has an incremental contribution to the study of the mechanism of financial crisis in theory and it helps companies supervise their financial situation in practice.
High-quality earnings is the cornerstone of the healthy and sustainable development of the company and the financial crisis is a comprehensive performance of financial quality.Based on the theory of harmony,this paper analyzes the mechanism of financial crisis from the aspects of earnings quality and the balance of its each dimension and establishes the financial crisis' evaluation index system based on earnings quality pyramid.Considering the mutual influence between the evaluation indicators and the attribute value transfer of the constant weight comprehensive model,this paper establishes the decisionmaking and trial evaluation based analytic network process(DANP)double layer penalty variable weight time series financial early-warning model to judge the financial condition.Through the empirical data of listed companies,the critical interval of the financial early warning comprehensive value is [0.6494,0.6547].Its prediction accuracy rate reaches 91.53%.The smaller the value,the greater the likelihood of the financial crisis.It has an incremental contribution to the study of the mechanism of financial crisis in theory and it helps companies supervise their financial situation in practice.
引文
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[19]Laitinen E K,Suvas A.Financial distress prediction in an international context:Moderating effects of Hofstede's original cultural dimensions[J].Journal of Behavioral&Experimental Finance,2016,9:98-118.
[20]Bhandari S B,Johnson-Snyder A J.A generic model of predicting probability of success-distress of an organization:A logistic regression analysis[J].Journal of Applied Business Research,2018,34(1):169-182.
[21]Jabeur S B.Bankruptcy prediction using partial least squares logistic regression[J].Journal of Retailing&Consumer Services,2017,36:197-202.
[22]Zmijewski M E.Methodological issues related to the estimation of financial distress prediction models[J].Journal of Accounting Research,1984,22(1):59-82.
[23]Iturriaga F J L,Sanz I P.Bankruptcy visualization and prediction using neural networks:A study of U.S.commercial banks[J].Expert Systems with Applications,2015,42(6):2857-2869.
[24]杨淑娥,黄礼.基于BP神经网络的上市公司财务预警模型[J].系统工程理论与实践,2005,25(1):12-18.Yang S E,Huang L.Financial crisis warning model based on BP neural network[J].Systems Engineering—Theory&Practice,2005,25(1):12-18.
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[31]Mselmi N,Lahiani A,Hamza T.Financial distress prediction:The case of French small and medium-sized firms[J].International Review of Financial Analysis,2017,50:67-80.
[32]Fallahpour S,Lakvan E N,Zadeh M H.Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem[J].Journal of Retailing and Consumer Services,2017,34:159-167.
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