非财务指标因素对企业信用评级的影响研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
伴随着新巴塞尔协议的发布,同时对我国银行业的风险管理水平也提出了更高的要求,现有的信用评级模型主要是以企业的财务数据来预测企业的违约率,对于如何提高信用评级模型的预测能力,成为了普遍关注的问题。
     Berger et al(2002)学者通过研究发现,在对企业进行信用评级时,要把企业的财务因素和非财务因素进行综合考量,这样能够更准确的预测企业的违约率。通过阅读大量的文献,发现学者们在对企业进行信用评级时也考虑了非财务指标,但是仅仅是进行定性分析并没有进行具体的量化。
     本研究通过对上市公司中的98家企业的样本数据进行实证研究,以建筑业、旅游业、零售业、制造业、旅游业为研究对象,并选取公司特性因素、企业的市场地位、股权结构这三个非财务指标,通过对这三个非财务指标进行量化来考查其是如何反映企业的信用状况,同时指出三个非财务指标与预期违约率之间的关系,并采用KMV违约预测模型为工具。利用统计软件SPSS17.0对数据进行描述性统计分析、相关分析和回归分析,得出:优质的公司特性因素、合理的股权结构、企业的市场地位与预期违约率呈负相关关系,企业的信用评级与预期违约率呈负相关。
With the release of the new Basel II, while the level of risk management of China's banking industry has made a higher demand, the existing credit rating model mainly depended on financial data for forecasting corporate default rate, as to how to improve credit predictive power of rating models has become a common concern.
     Berger et al (2002) and scholars through the study found that when the credit rating of enterprises are proceeded, financial factors and non-financial factors should be taken into consideration, it can more accurately predict corporate default rates. Foreign banks have already paid more a attention to non-financial indicators in the rating model. However, the credit rating of Chinese commercial banks to consider non-financial indicators, and quantification of non-financial indicators still questioned.
     Empirical studies of firm characteristics factors of quality, reasonable ownership structure, and corporate market position of these three non-financial indicators of the impact on the corporate credit rating. In this study, construction, tourism, retail, manufacturing, tourism are selected, financial and non-financial factors as variables, and using KMV default prediction model as a tool. Then this model’s reliability and validity analysis are based on the assumption. After all scales can serve as an effective measurement tool, the study also described the data statistical analysis, found the data was representative to represent the overall objective. Therefore, this paper use spss as a tool of correlation and regression analysis, non-financial indicators and the role of credit rating mechanism between an analysis and explanation, and to scholars with related research on methods of credit rating models, and build a non-financial factors and corporate credit rating of the theoretical model, this characteristic elements from the company, ownership structure, business status of the three angles market analysis, hypotheses and empirical analysis on these hypotheses and discussions have finally come to the following conclusions:
     (1) The company features the quality and the expected default frequency factor was negatively correlated.
     Ability to represent the company's credit solvency of enterprises, and will, therefore the higher the credit capacity, greater ability to finance enterprises in the industry will accelerate the development of the enterprise are bound to expand the scale, the paper analyzes the data, the company characteristic factors and expected a negative correlation between default rates, which to some extent, shows characteristics of factors affecting the company's credit business. And the data analysis, the company characteristic factors impact on the credit ratings of some more.
     (2) Reasonable ownership structure and expected default frequency was negatively correlated.
     Obtained by the analysis of the text, ownership structure and a negative correlation between default rates, but no company characteristic factors impact on the expected default frequency of large, but to some extent influence the corporate credit ratings to the banks on a credit rating company O'clock This indicator should be considered.
     (3) the status of the enterprise market with the expected default rates negatively correlated.
     Regression analysis between the factors, we can see that the market position of enterprises the greatest impact on credit ratings but also the most obvious. This explains the status of industry changes through affecting the profitability of enterprises and then affect the credit rating, and company size to some extent affect the structure of assets and liabilities.
     This conclusion has practical significance as follows:
     (1) In order to avoid bad debt bad debt situation, and in the enterprise credit rating loans, commercial banks shall strengthen the consideration of non-financial indicators, financial indicators of enterprises and non-financial indicators of these two aspects of comprehensive consideration.
     With the release of the new Basel II, China's commercial banks must follow the requirements of the new agreement, the effectiveness of the internal rating of higher demand. In this paper, the data of listed companies’selected empirical study on non-financial indicators were selected and quantified, and to verify a company characteristic factor, ownership structure, market position and business credit rating of the enterprise. From this study found that China's commercial banks to make loans to enterprises to improve decision-making considerations on non-financial indicators, attention to the soft factors on the corporate credit rating of.
     (2) Enterprises are to occupy position in the industry, leading to their own businesses competitive, not only to be considered in the development of enterprise in the development of internal policies, but also pay attention to the external environment in which, as far as possible the two coordinated development aspects.
     Rapid economic development today, companies want to be raised based on the competitiveness of enterprises, the reasonable control of company size, so that enterprises achieve the most optimal resource allocation, we must make reasonable the ownership structure, while also concerned about the business contribution to society, attention to the legitimacy of business transactions project, which not only affect enterprise performance, enterprise status in the industry, is a business enterprise can become the basis for Evergreen, but also affect the bank's overall credit rating of enterprises, but also affected business financing ability, is not conducive to expansion of business scale, the improvement of the competitiveness of enterprises. Therefore, the enterprises are to long-term development of an invincible position in the competition, to non-financial indicators and financial indicators into consideration, be hard and soft side.
     Verified the assumption, based on in-depth study of this paper are to be strengthened, made follow-up recommendations of the study are as follows:
     First, the paper uses empirical research KMV model to the data used by the company 2005-2009 - years of data, if the reduced time as a scale, use half a year or quarterly data, the results may be of some stability. As the database construction in China has yet to be perfect, and KMV model requires the support of at least five years of data, so that further studies need to be in the future.
     Second, this paper research firm characteristic factors, ownership structure, market position of enterprises on the validity of credit ratings, but these indicators of how the role of financial indicators did not study.
     Finally, this article only considerate listed companies, but more and more attention are attracted by SMEs in the current development situation,, while the SMEs operate with more flexibility and risk resistance are weak, non-financial factors much more affect SMEs, so small and medium enterprises will be a future research direction.
引文
[1]Anil K.Makhija and James M.Patton The impact of film ownership structure on voluntary disclosure empirical evidence from Czech annual reports[J].Journal of Business,2004(77):89-105.
    [2]American Accounting Association Financial Accounting Standards Committee (AAA FASC).Recommendations on disclosure of non financial performance measures [J].Accounting Horizons, 2002(16):353-362.
    [3]Anderson E·C·and D·Lelmnnn, Customer satis faction Mark share and profitability Findings from Sweden [J].Journal o Marketing,2000(3):53-66.
    [4]ALTMAN E,EISENBEIS R A , SINKEY J. Applications of classification techniques in business,Banking and Finance [M].JAI press,Greenwih,CT,1981.
    [5]Crosbie, J. P.,“Modeling Default Risk,”KMV: San Francisco, California, U.S.A.1999
    [6] ENGLISH, W.B., NELSON, W.R. Bank risk rating of business loans. In: Proceedings of the 35th Annual Conference on Bank Structure and Competition, 1999.5.
    [7]Friedman H.altmen E,Kao D.Inteoducing Recursive Partitioning for Financial Classification. The Case of Financial Distress[J].Banking Finance,1985 (1):269-291.
    [8] Hopfield J J, Tank D W.Neural computation of decisions in optimization problem[J].Biological Cybernetics,1985(5):123—145.
    [9]Inner C·D·and C·Larcker, Innovations in perfomnance Measurement Trends and research Implications [J], Management Accounting Research, 1998(6):205-238.
    [10]Ittner,C.D.,Larcker,D.F.and M.V.Rajan. The choice of performance measures in annual bonus contracts[J].The Accounting Review,1997(72):231-255.
    [11]Johnson G. Scholes K.and Exploring Corporate Strategy[M].Prentice Hall,Inc,1993.
    [12]J.Grunert et al. The Role of Non-Financial Factors in Internal Credit Ratings, Journal of Barking & Finance 29 (2005) 509-531
    [13]Lundy, M.Cluster .Analysis in Credit Scoring and Credit Control [M].New York:Oxford University Press,1993.
    [14]MESSIER W.F.HANSEN J V. Inducing rules for expert system development example using default and bankruptcy data [J]. Management Science,1988,34 (12):1403-1415.
    [15]OdomM D,Sharda R A .Neural network for bankruptcy predition[J]. International Joint Conference on Neural Networks,1990(6):163—168.
    [16]PRAHALD,C.K,GARY HAMEL .The Core Competence of the Corporation[J].Harvard Business Review,1990 Vol,68(3):79-91.
    [17]Sean W.G.Robb,Louise E.Single and Marilyn T.Zarzeski.Nonfinancial disclosures across Anglo-American countries[J].Journal of International Accounting,Auditing&Taxation,2001(10):71-83.
    [18]TREACY, W.F. and CAREY . Credit Risk Rating Systems at Large US Banks. Journal of Banking and Finance 24, 2000(05):167-201
    [19]陈华雄,林成德,叶武.基于神经网络的企业信用等级评估[J].系统工程学报,2002(12):117-119.
    [20]崔小岩.上市公司信用风险预警模型研究[D].江苏:江苏大学,2007.
    [21]程鹏,吴冲锋等.信用风险度量和管理方法研究[J].管理工程学报,2002 (1):70-73.
    [22]常丽娟,张俊瑞.财务信用评价与管理研究[M].大连:东北经大学出版社, 2006.
    [23]曹德芳,夏好琴.基于股权结构的财务危机预警模型构建[J].南开管理评论,2005(6):29-31.
    [24]陈良华,孙健.公司治理与财务危机:来自上海股票市场的证据[J].东南大学学报,2005(5):98-103.
    [25]陈华敏.非财务指标的绩效后果研究[D].厦门:厦门大学,2006年.
    [26]曹德芳,夏好琴.基于股权结构的财务危机预警模型构建[J].南开管理评论,2005(6):72-77.
    [27]曹伟.银行信贷决策中的经营风险分析[J].金融理论与实践,2002(9):178-189.
    [28]陈新,邢俊霞.浅析企业管理者行为对企业会计信息的影响[J].中国科技信息, 2007(1):153.
    [29]邓晓岚.非财务视角下中国上市公司经营困境评价模型及实证研究[D].湖北:华中科技大学,2006.
    [30]段素芳.商业银行信贷风险管理中的非财务因素分析[D].北京:对外经贸大学,2006.
    [31]范柏乃,朱文斌.中小企业信用评价指标的理论遴选与实证分析[J].科研管理,2003(11):87-90.
    [32]范巧玲.我国商业银行信用风险评级体系研究[D].秦皇岛:燕山大学,2007.
    [33]葛兆强.国际评级机构的银行信用评级原理、方法及其局限[J].华南金融研究,2001(2):30.
    [34]宫剑.民营企业信用评级研究[D].吉林大学,2008.
    [35]葛志鸿,刘广宇.浅谈公司治理结构对企业财务管理的影响[J].财会通讯理财版,2008(4)
    [36]郭斌等.我国企业财务危机预警模型研究一以财务与非财务因素构建[J].金融研究,2006(2):78-81.
    [37]黄智猛,张则斌,吴冲锋.基于违约风险的企业融资结构和成本[J].系统工程理论方法应用,2000(1):23-27.
    [38]黄明祥,许光华.KMV模型在台湾金融机构信用风险管理机制有效性之研究[J].财金论文丛刊,2005(10): 29-50.
    [39]姜灵敏.基于模糊综合评判的贷款风险非财务因素分析模型[J].统计与信息论坛,2003(5):39-42.
    [40]刘利军,徐琳.模糊数学在上市公司信用评价中的运用[J].财苑.业务与技术,2004(3):38-39.
    [41]刘铮铮.基于层次分析法的商业银行信用评级模型研究[D].深圳:西北工业大学,2006.
    [42]李羿,商业银行现代信用风险度量模型评估与应用研究[D].北京:对外经济贸易大学,2007.
    [43]李伟,朱卫东.基于证据理论的商业银行非财务信用风险评价研究[J].河南金融管理干部学院学报,2007(5):69-72.
    [44]李宗怡.美国大银行内部信用风险评级体系及其借鉴[J].国外财经,2000(2):128-130.
    [45]李力.何为信用评级[J].学术论丛,2009(4):16-17.
    [46]李力群.信贷决策中的非财务因素分析[J].金融理论与实践,2004(6):82-85.
    [47]李水平.信贷企业信用评价中的非财务因素分析[J].湖南税务高等专科学校学报,2007(1):160-162.
    [48]刘新建,范巧玲.层位评价理论在企业信用评级中的应用[J].山西师范大学学报(自然科学版),2006(9):103-105.
    [49]李伟,朱卫东.基于证据理论的商业银行非财务信用风险评价研究[J].河南金融管理干部学院学报,2007(1):73-78.
    [50]欧志伟,萧维.中国资信评级制度方略[M].上海:上海财经大学出版社,2005.
    [51]彭彤丽.商业银行对交通运输中小企业信用评估中非财务指标的设计[J].湖南工业职业技术学报:人文社会科学版,2009(6):74-75.
    [52]R.D.巴泽尔.战略与绩效-PIMS原理「M].北京:清华出版社,2000.
    [53]石新武.资信评估的理论和方法[M].北京:经济管理出版社,2002(6):223-240.
    [54]邵少敏,吴沧澜,林伟.独立董事和董事会结构、股权结构研究:以浙江省上市公司为例[J].世界经济,2004(2):75-77.
    [55]陶烁,杨晓光.商业银行内部信用评级比较研究中外管理导报
    [56]王克敏,罗艳梅.中国上市公司对外担保与财务困境研究[J].吉林大学学报(社科版),2006(05):106-113.
    [57]王春峰,万海晖,张维.基于神经网络技术的商业银行信用风险评估[J].系统工程理论与实践,1999(9):24—32.
    [58]武剑,我国商业银行内部评级体系(IBR)的建立和应用[J].新金融,2002(7):33-36.
    [59]吴江涛.基于灰关联法的我国上市公司信用风险评价研究[J].商场现代化,2007(9):137-139.
    [60]邬润扬.资信评级方法[M].中国方正出版社,2005.
    [61]吴晶妹.资信评估[M].北京:中国审计出版社,2001.
    [62]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(6)46-55.
    [63]王汉荣.企业资信等级模糊综合评判与商业信贷风险跟踪预警监测办法[J].沙洲职业工学院学报,2000(6):216-218.
    [64]肖北溟.国有商业银行信贷评级模型的构建及实证检验[J].金融论坛,2004(4):61-62.
    [65]徐志春,王宗军,薄纯林.引入非财务因素的中小企业信用风险预警模型实证研究[D].武汉:华中科技大学,2008.
    [66]徐晓东,陈小悦.第一大股东对公司治理、企业业绩的影响分析[J].经济研究,2003(2):63-66.
    [67]徐志春.引入非财务因素的中小企业信用风险预警模型实证研究[J].金融理论与实践,2008(6):111-113.
    [68]徐沉.企业绩效评价中的非财务指标研究—以房地产开发企业为例[D].浙江:浙江工商大学,2008.
    [69]徐光华.改进沃尔比重评分法之探讨[J].南京经济学报,1999(6):67-69.
    [70]徐光华,吴明鸣.基于EVA的行业财务预警模型研究——以沪市IT上市公司为例[J].经济管理,2006(24):63-68.
    [71]徐志春,王宗军,薄纯林.引入非财务因素的中小企业信用风险预警模型实证研究[J].金融理论与实践,2008(3):3-6.
    [72]于东智,池国华.董事会规模、稳定性与公司绩效:理论与经验分析[J].经济研究,2004(4):120-122.
    [73]杨兵.基于非财务指标及数据挖掘方法的财务危机预测研究[D].海南:华南热带农业大学,2005.
    [74]于新花.借款企业信用评级的非财务因素分析—管理控制能力分析[J].安徽广播电视大学学报,2004(2):89-92.
    [75]余高潮.北京企业景气指数保持平稳[J].数据,2006(5):58-59.
    [76]于东智,池国华.董事会规模、稳定性与公司绩效:理论与经验分析[J].经济研究,2004(4):49-50.
    [77]严富国.银行内部信用评级应用研究[D].西安:西安理工大学,2004.
    [78]叶庆祥等.基于资本市场理论的上市公司信用风险度量研究[J].经济学家,2005(2):112-117.
    [79]严富国.银行内部信用评级应用研究[D].西安:西安理工大学,2004.
    [80]杨涛,张晓峰,陈晶晶.我国上市公司财务信用评价指标体系构建初探[J].中国管理信息化,2009(9):128-130.
    [81]杨兵,基于非财务指标及数据挖掘方法的财务危机预测研究[D].华南热带农业大学,2005.
    [82]杨涛,张晓峰,陈晶晶.我国上市公司财务信用评价指标体系构建初探[J].中国管理信息化,2009(9):112-117.
    [83]于新花.借款企业信用评级的非财务因素分析—管理控制能力分析[J].安徽广播电视大学学报,2004(2):62-63.
    [84]詹原瑞,王文静,孙彤.关于零售市场信用风险建模的问题讨论[J].经济经纬,2005(4):64-66.
    [85]郑春明.开展金融业资信评估防范系统性金融风险[J].上海金融,2003(12):29.
    [86]张美灵,欧志伟.信用评估理论与实务[M].上海:复旦大学版社, 2004.
    [87]朱荣恩.资信评级[M].中国时代经济出版社,2006.
    [88]周庆文.我国银行信用评级[J].金融信息参考,2001(l1):9.
    [89]张青庚.建立基于企业价值的信用评级体系[J].中国国际商业信用网,2006(8):52-54.
    [90]张鸣,程涛.审计意见在财务预警中的信息含量[J].财会通讯,2004(12):47-48.
    [91]张川,潘飞,John Robinson.非财务指标与企业财务业绩相关吗—一项基于中国国有企业的实证研究[J].中国工业经济,2006(11):99-107.
    [92]张川,潘飞.非财务指标采用的业绩后果实证研究[J].会计研究,2008(3):59-62.
    [93]周颖,毛定祥.银行信用风险评估中的非财务分析[J].上海大学学报,2003(3):52-55.
    [94]张洽.我国工业企业资信评估体系确定的非财务指标研究[J].现代经济,2007(6):153-156.
    [95]赵庆森.商业银行信贷风险与行业分析[J].中国金融出版社,2004(6):79-81.
    [96]张凤英.我国商业银行信用风险管理研究[D].北京:首都经济贸易大学, 2005.
    [97]张青庚.建立基于企业价值的信用评级体系[J].中国国际商业信用网,2003(6):91-93.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700