中国旅游上市公司财务危机预警研究
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摘要
经济全球化给中国企业带来前所未有的发展机遇,也带来了前所未有的挑战。中国旅游上市公司作为第三产业的重要组成部分,随着中国经济腾飞和第三产业的振兴,目前也处在一个高速发展的阶段。然而大部分旅游类上市公司高速发展的光环并不能掩盖部分公司因财务危机而导致的举步维艰,因此,中国旅游上市公司财务危机预警是一个非常值得研究的课题。企业兴衰转变是一个由量变到质变的过程,如果我们在财务危机潜伏期就能采用一定技术手段识别企业财务危机信号,并做出企业财务危机预警、制定相应的财务危机纠正措施,把问题解决在初始阶段,使企业走上健康的发展道路,就能使企业避免巨大经济损失。
目前我国财务危机预警研究与实践基本上都是以全部上市公司为样本、使用共性的预警模型,几乎没有考虑到行业的差别和企业的差别。实际上不同行业可能会产生不同企业特征,不同企业特征可能会产生不同的风险特征,因此企业财务危机预警模型在设计之初就要考虑具有行业烙印的企业的特征,只有这样,预警模型才能更具有针对性,才能更具有实用性和有效性。其次,多数学者是以一个年份的历史财务数据进行预警研究,较少的考虑数据的时效性和趋势。本文在全面分析财务危机预警理论的基础上研究了中国旅游业发展的概况、行业发展特征,基于投资者视角提出适合中国旅游业的财务风险预警指标体系。然后采用灰色系统方法中GM(1,1)对中国旅游上市公司财务数据进行预测,得到财务指标预测值。最后运用AHP-模糊综合评价法进行综合评价,进而做出预警判断。
Economic globalization has brought not only unprecedented development opportunities for China's enterprises, but also unprecedented challenges. As an important part of the third industry, China's listed tourism companies are in a high speed development stage along with the rapid growth of China's economy and the revitalization of the third industry. However, the aura of most of listed tourism companies which have got high-speed development, can't cover up other companies′difficulty caused financial distress, so it is worthy of researching on China's early warning of financial distress for China's listed Tourism Company. The process of worsening or degenerating of corporate health is a transition from quantitative change to qualitative change, if we can identify enterprise financial distress signal by a certain technology method when the company are in financial distress incubation period, make early warning of enterprise financial distress, and carry out corresponding financial distress corrective measures to solve the problems in the initial stage, get the enterprise on the road of the healthy development. It would avoid huge economic losses for the enterprise.
At present, research and practice of our country's early warning of enterprise financial distress basically regard all listed companies as a sample, using common early warning model, with no account taken of the difference of specific industries. Different industry may have a different enterprise characteristics, then,different enterprise characteristics may have different risk features, so the early warning model of enterprise financial distress is designed during the earliest periods,considering the characteristics of the enterprise brand industry, only in this way, can the early warning mode be more practical and effective. In addition, most scholars use a year's history financial data for the study of early warning, with no account taken of the timeliness and trend of the data. Based on the comprehensive analysis of the theory of early warning of enterprise financial distress, the paper research the survey of the development of the China's tourism industry and the industry development present situation, proposing the index system of early warning of enterprise financial distress for tourism based on investor perspective. GM (1, 1) of the grey system theory is used to forecast financial data of China's listed tourism companies. AHP-fuzzy comprehensive evaluation method is used for comprehensive evaluation, to make a Judgment on early warning.
目前我国财务危机预警研究与实践基本上都是以全部上市公司为样本、使用共性的预警模型,几乎没有考虑到行业的差别和企业的差别。实际上不同行业可能会产生不同企业特征,不同企业特征可能会产生不同的风险特征,因此企业财务危机预警模型在设计之初就要考虑具有行业烙印的企业的特征,只有这样,预警模型才能更具有针对性,才能更具有实用性和有效性。其次,多数学者是以一个年份的历史财务数据进行预警研究,较少的考虑数据的时效性和趋势。本文在全面分析财务危机预警理论的基础上研究了中国旅游业发展的概况、行业发展特征,基于投资者视角提出适合中国旅游业的财务风险预警指标体系。然后采用灰色系统方法中GM(1,1)对中国旅游上市公司财务数据进行预测,得到财务指标预测值。最后运用AHP-模糊综合评价法进行综合评价,进而做出预警判断。
Economic globalization has brought not only unprecedented development opportunities for China's enterprises, but also unprecedented challenges. As an important part of the third industry, China's listed tourism companies are in a high speed development stage along with the rapid growth of China's economy and the revitalization of the third industry. However, the aura of most of listed tourism companies which have got high-speed development, can't cover up other companies′difficulty caused financial distress, so it is worthy of researching on China's early warning of financial distress for China's listed Tourism Company. The process of worsening or degenerating of corporate health is a transition from quantitative change to qualitative change, if we can identify enterprise financial distress signal by a certain technology method when the company are in financial distress incubation period, make early warning of enterprise financial distress, and carry out corresponding financial distress corrective measures to solve the problems in the initial stage, get the enterprise on the road of the healthy development. It would avoid huge economic losses for the enterprise.
At present, research and practice of our country's early warning of enterprise financial distress basically regard all listed companies as a sample, using common early warning model, with no account taken of the difference of specific industries. Different industry may have a different enterprise characteristics, then,different enterprise characteristics may have different risk features, so the early warning model of enterprise financial distress is designed during the earliest periods,considering the characteristics of the enterprise brand industry, only in this way, can the early warning mode be more practical and effective. In addition, most scholars use a year's history financial data for the study of early warning, with no account taken of the timeliness and trend of the data. Based on the comprehensive analysis of the theory of early warning of enterprise financial distress, the paper research the survey of the development of the China's tourism industry and the industry development present situation, proposing the index system of early warning of enterprise financial distress for tourism based on investor perspective. GM (1, 1) of the grey system theory is used to forecast financial data of China's listed tourism companies. AHP-fuzzy comprehensive evaluation method is used for comprehensive evaluation, to make a Judgment on early warning.
引文
[1]邓琳. Logistic模型在上市公司动态财务预警中的应用研究[D].西南财经大学硕士论文.2008:5-8
[2]谢季坚,刘承平.模糊数学方法及其应用[M].武昌:华中科技大学出版社2006:90-121
[3] Altman.E.I, Financial Ratios. Discriminate Analysis and the prediction of Corporate Bankruptcy. Journal of Finance[J].1968(9):587-601
[4] Frostor G. Financial Statement Analysis Edition. Englewood CliffS: NJ: Prenticehouse Ins,1986:121-126
[5] Beaver W.H. Marker Price. Financial Ratio and the Prediction of Failure. Journal of Accounting Research.[J].1968
[6] Edmister R.Financial ratios as discriminant Predictors of small business failure [J]. Journal of Finance,1972(27):1477-1493
[7] Taffler,Richard,TISShaw. Going,going,gone- four factors which predict. Howard Accountancy [J], 1977:56-66
[8] Aharony Jones, Swary.Analysis a new model for identifying bankruptcy risk of corporation. Journal of Banking and Finance,1980:31-43
[9] Charles Mossman,Geoffrey Bell, L Swartz, Harry Turtle . An empirical comparision of bankruptcy models. The Financial Review,1998,33:35-54
[10] H.Fryman,E.I.Altman,D.Kao. Introducing Recursive Partitioning for Financial Classification:The Case of Financial Distress[J].The Journa1 of Finance, l 985,40:256-278
[11] Feng Yu Lin,Sally Mc Clean. A date mining approach to the prediction of corporate failure [J].Knowledge-Based Systems,2001,14:165-179
[12] William Hopwood, James McKeown and Jane Mutchler.A Test of the Incremental Explanatory Power of Opinions Qualified for Consistency and Uncertainty[J].The Accounting Review,1989.64(1):38-45
[13] Marin, Daniel.Early warning of bank failure:a logit regression approach[J].Journal of Banking and Finance,1977,l,:236-268
[14] Gentry J.A.,Newbold P, Whitford D.T.Classifying bankrupt firms with funds Flow components. Journal of Accounting Research,1985,23(l):132-157
[15] Lapedes A,Farber R.Nonlinear signal processing using neural networks:prediction and system modeling[R]. Los Almos,Los Almos National Laboratory,1987
[16] Gilbert L.R.,Krishn agopa lt ernative accounting measures predictors of failure. Accounting Review, 1968(43):24~35l M., SchwartzK.B. predicting bankruptcy for firms In financial distress. Journal of Business Finance and Accounting,1990,17:141-182
[17]王玲.医药行业上市公司财务风险预警模型研究[D].西安电子科技大学硕士论文.2011
[18]张建.运用财务比率分析评价公司财务状况——内蒙华电公司财务状况分析[D].对外经济贸易大学硕士论文.2006:23-28
[19]张友棠.财务预警系统管理研究[M].北京:中国人民大学出版,2004:23-43
[20]杨玉娥.我国制造业上市公司财务预警分析[D].西北大学. 2009:28-31
[21]宋志霆.我国上市公司财务危机预警模型的研究[D].西安建筑科技大学. 2009:26-41
[22]柳炳祥,盛昭翰.基于粗神经网络的企业财务危机预警方法[J].中国科学,2002,(8)
[23]谭久均.财务指标与违约距离相融合的上市公司财务预警模型[J].系统工程,2005,9:1l1—117
[24]曹明.基于数据挖掘的财务预警模型设计与实现[J].计算机应用,2006,26(1 0):242l-2424
[25]陈静.上市公司财务恶化预测的实证分析.会计研究. 1999, (4):27-35
[26]刘红霞.以主成分分析法构建企业财务危机预警模型.中央财经大学学报2004, (4):194-202
[27]周敏.基于系统模糊优选和神经网络模型的企业财务危机预警.管理科学学报,2003, (6):85-91
[28]企业财务危机预警指标解析.财会研究.2006, (4): 48-52
[29]万希宁,苏秋根.关于上市公司财务失败预警的实证分析.财务与会计导刊.2003, (12):79-82
[30]柳炳祥,盛昭翰.基于粗神经网络的企业财务危机顶警方法.中国工程科学.2002, (8):58-62
[31]梁正宁.旅游上市公司的资产重组及应注意的若干问题.旅游学刊[J],2000(5).21-25
[32]戴国庆,舒芸芸.旅游上市公司净资产收益率分析.上海大学学报[J],2000(l).76-80
[33]赵锦爱,徐涛.旅游上市公司利润质量分析.旅游科学[J],2003(2).21-25
[34]刘海英,刘志远,旅游上市公司增长机会与资本结构关系实证研究.山东大学学报[J],2007(3).84-92
[35]李旻暾.基于供应链管理的供应商模糊综合评价[J].价值工程,2003(6):39-42
[36]朱颢.供应链管理中供应商选择的灰色综合评价法[J].天津理工学院学报,2004,20(2)
[37]徐小艳,杨君岐,刑战雷.供应商评价的模糊综合评判模型研究及应用[J].陕西科技大学学报:自然科学版,2004,22(6):133-140
[38]孙蛟,曾凡婷.层次分析法AHP在供应商评价中的应用[J].物流技术,2004(9):45-48
[39]米辉辉,李永臣.企业集团业绩的模糊综合评价,财会月刊[J],2007(1),38-40
[40]赵树宽,陆晓芳.国外典型企业效绩评价,工业技术经济[J],
[41]杨金丹.中国旅游上市公司治理结构与经营绩效的相关性研究[D].安徽农业大学硕士论文.2009:5-8
[42]王静.中国上市公司财务危机预警模型的构建与实证研究[D].湖南师范大学硕士论文.2008:9-13
[43]王丽芳.我国上市公司财务危机阶段性预警模型的构建[D].东北大学硕士论文.2006:
[44]曾俊丽.我国上市公司财务危机预警研究[D].江苏大学硕士论文.2007:23-31
[45]高丽亚.我国上市公司动态财务危机预警研究——基于时序立体数据方法的模型构建[D].西南财经大学硕士论文.2006:47-54
[46]柳晓霞.中国制造业上市公司财务困境阶段性预警研究[D].广东工业大学硕士论文.2007:11-17
[47]陈国坤.房地产企业财务危机预警研究[D].华中科技大学硕士论文.2006:29-34
[48]张丽娜.AHP—模糊综合评价法在生态工业园区评价中的应用[D].大连理工大学硕士论文.2006:21-27
[49]姜红珍.企业财务危机预警的研究[D].南京理工大学硕士论文.2003:18-22
[50]马立群.基于小波神经网络的财务预警模型研究与实践[D].上海财经大学硕士论文.2007:18-19
[51]邵磊.跨界突发性大气环境风险源分级评价研究[D].大连理工大学硕士论文.2009:
[52]徐进华.基于灰色理论的数据挖掘及其模型研究[D].北京交通大学硕士论文.2009:6-8
[53]余学武.基于BP神经网络的企业贷款风险评估研究[D].中山大学硕士论文.2010:7-9
[54]嘉林.我国钢铁企业社会责任与企业财务绩效关系研究[D].西南大学硕士论文.2009:
[55]王玲.医药行业上市公司财务风险预警模型研究[D].西安电子科技大学硕士论文.2011:
[2]谢季坚,刘承平.模糊数学方法及其应用[M].武昌:华中科技大学出版社2006:90-121
[3] Altman.E.I, Financial Ratios. Discriminate Analysis and the prediction of Corporate Bankruptcy. Journal of Finance[J].1968(9):587-601
[4] Frostor G. Financial Statement Analysis Edition. Englewood CliffS: NJ: Prenticehouse Ins,1986:121-126
[5] Beaver W.H. Marker Price. Financial Ratio and the Prediction of Failure. Journal of Accounting Research.[J].1968
[6] Edmister R.Financial ratios as discriminant Predictors of small business failure [J]. Journal of Finance,1972(27):1477-1493
[7] Taffler,Richard,TISShaw. Going,going,gone- four factors which predict. Howard Accountancy [J], 1977:56-66
[8] Aharony Jones, Swary.Analysis a new model for identifying bankruptcy risk of corporation. Journal of Banking and Finance,1980:31-43
[9] Charles Mossman,Geoffrey Bell, L Swartz, Harry Turtle . An empirical comparision of bankruptcy models. The Financial Review,1998,33:35-54
[10] H.Fryman,E.I.Altman,D.Kao. Introducing Recursive Partitioning for Financial Classification:The Case of Financial Distress[J].The Journa1 of Finance, l 985,40:256-278
[11] Feng Yu Lin,Sally Mc Clean. A date mining approach to the prediction of corporate failure [J].Knowledge-Based Systems,2001,14:165-179
[12] William Hopwood, James McKeown and Jane Mutchler.A Test of the Incremental Explanatory Power of Opinions Qualified for Consistency and Uncertainty[J].The Accounting Review,1989.64(1):38-45
[13] Marin, Daniel.Early warning of bank failure:a logit regression approach[J].Journal of Banking and Finance,1977,l,:236-268
[14] Gentry J.A.,Newbold P, Whitford D.T.Classifying bankrupt firms with funds Flow components. Journal of Accounting Research,1985,23(l):132-157
[15] Lapedes A,Farber R.Nonlinear signal processing using neural networks:prediction and system modeling[R]. Los Almos,Los Almos National Laboratory,1987
[16] Gilbert L.R.,Krishn agopa lt ernative accounting measures predictors of failure. Accounting Review, 1968(43):24~35l M., SchwartzK.B. predicting bankruptcy for firms In financial distress. Journal of Business Finance and Accounting,1990,17:141-182
[17]王玲.医药行业上市公司财务风险预警模型研究[D].西安电子科技大学硕士论文.2011
[18]张建.运用财务比率分析评价公司财务状况——内蒙华电公司财务状况分析[D].对外经济贸易大学硕士论文.2006:23-28
[19]张友棠.财务预警系统管理研究[M].北京:中国人民大学出版,2004:23-43
[20]杨玉娥.我国制造业上市公司财务预警分析[D].西北大学. 2009:28-31
[21]宋志霆.我国上市公司财务危机预警模型的研究[D].西安建筑科技大学. 2009:26-41
[22]柳炳祥,盛昭翰.基于粗神经网络的企业财务危机预警方法[J].中国科学,2002,(8)
[23]谭久均.财务指标与违约距离相融合的上市公司财务预警模型[J].系统工程,2005,9:1l1—117
[24]曹明.基于数据挖掘的财务预警模型设计与实现[J].计算机应用,2006,26(1 0):242l-2424
[25]陈静.上市公司财务恶化预测的实证分析.会计研究. 1999, (4):27-35
[26]刘红霞.以主成分分析法构建企业财务危机预警模型.中央财经大学学报2004, (4):194-202
[27]周敏.基于系统模糊优选和神经网络模型的企业财务危机预警.管理科学学报,2003, (6):85-91
[28]企业财务危机预警指标解析.财会研究.2006, (4): 48-52
[29]万希宁,苏秋根.关于上市公司财务失败预警的实证分析.财务与会计导刊.2003, (12):79-82
[30]柳炳祥,盛昭翰.基于粗神经网络的企业财务危机顶警方法.中国工程科学.2002, (8):58-62
[31]梁正宁.旅游上市公司的资产重组及应注意的若干问题.旅游学刊[J],2000(5).21-25
[32]戴国庆,舒芸芸.旅游上市公司净资产收益率分析.上海大学学报[J],2000(l).76-80
[33]赵锦爱,徐涛.旅游上市公司利润质量分析.旅游科学[J],2003(2).21-25
[34]刘海英,刘志远,旅游上市公司增长机会与资本结构关系实证研究.山东大学学报[J],2007(3).84-92
[35]李旻暾.基于供应链管理的供应商模糊综合评价[J].价值工程,2003(6):39-42
[36]朱颢.供应链管理中供应商选择的灰色综合评价法[J].天津理工学院学报,2004,20(2)
[37]徐小艳,杨君岐,刑战雷.供应商评价的模糊综合评判模型研究及应用[J].陕西科技大学学报:自然科学版,2004,22(6):133-140
[38]孙蛟,曾凡婷.层次分析法AHP在供应商评价中的应用[J].物流技术,2004(9):45-48
[39]米辉辉,李永臣.企业集团业绩的模糊综合评价,财会月刊[J],2007(1),38-40
[40]赵树宽,陆晓芳.国外典型企业效绩评价,工业技术经济[J],
[41]杨金丹.中国旅游上市公司治理结构与经营绩效的相关性研究[D].安徽农业大学硕士论文.2009:5-8
[42]王静.中国上市公司财务危机预警模型的构建与实证研究[D].湖南师范大学硕士论文.2008:9-13
[43]王丽芳.我国上市公司财务危机阶段性预警模型的构建[D].东北大学硕士论文.2006:
[44]曾俊丽.我国上市公司财务危机预警研究[D].江苏大学硕士论文.2007:23-31
[45]高丽亚.我国上市公司动态财务危机预警研究——基于时序立体数据方法的模型构建[D].西南财经大学硕士论文.2006:47-54
[46]柳晓霞.中国制造业上市公司财务困境阶段性预警研究[D].广东工业大学硕士论文.2007:11-17
[47]陈国坤.房地产企业财务危机预警研究[D].华中科技大学硕士论文.2006:29-34
[48]张丽娜.AHP—模糊综合评价法在生态工业园区评价中的应用[D].大连理工大学硕士论文.2006:21-27
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