11.association between default behaviors of sm es and the credit facets of smes owners


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11.association between default behaviors of sm es and the credit facets of smes owners

  1. 1. European Journal of Business and Management www.iiste.orgISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)Vol 4, No.1, 2012Association between Default Behaviors of SMEs and the Credit Facets of SMEs Owners Amalendu Bhunia Reader, Department of Commerce Fakir Chand College, Diamond Harbour South 24-Parganas – 743331 West Bengal, India *E-mail of corresponding author: bhunia.amalendu@gmail.comAbstractThis study attempts examines relationship between default behaviors of SMEs and the credit facets of theirowners. Identifying and measuring credit risk of SMEs should be different from that of large firms, forSMEs appear to be influenced by their owners more directly and significantly, so that a more appropriateand effective way of credit management of SMEs could be applied in practice. This study implement anempirical study of logistic regression analysis with repeat sampling data after segregating the owners’characteristics data into variables of basic aspects, credit capacity aspects and credit will aspects. The resultreveals that variables reflected credit capacity aspects share more significant relationship with the SMEs’credit default behavior. It indicates that credit will variables and personal credit history have closestrelationship with enterprises’ default probability and the proportion of overdue loans are the extremesignificant variables which are valuable indicators in default risk estimate model.Keywords: SMEs, owners’ credit aspects, credit risk1. IntroductionDifferent from larger companies, identification and measurement of SMEs credit risk notonly depend on financial information, which is owing to financial information’sincredibility and SMEs’ special risk characteristics. Compared with larger firms, SMEsshow personification obviously, it means their development progress largely depends onthe enterpriser, because, generally speaking, the owner of the enterprise is its founder aswell. It is widely believed that the founder’s education level, experience, sprit, ability ofmanagement, personal quality, and social network are significant and important factorsfor enterprise’s long term-development. As a result, unlike larger ones, the owners’ creditfeatures may be beneficial supplement for identifying and measuring SMEs credit risk.2. Review of LiteraturesIn the research field on SMEs Credit Risk, besides the SME’s own financial information and non-financialinformation, their owner’s personal information attracts scholars’ and practitioners’ close attention. For theability of settling debts largely depends on the cash flow of borrower, some scholars began with the studyon the relationship between enterprises’ performance and their owners’ features. Haliassos and Bertaut(1995) showed that the people with higher education degree increasingly enlarge the proportion of riskyinvestment, such as stock and bond, to the personal fortune. It means that better educated people are alsobetter at finding opportunity of business and analyzing the market. The competence is just theindispensable quality as an entrepreneur, so the owners’ education level exerts a significant influence ontheir enterprises’ performance. Moreover, for most SMEs are at the start-up stage, the research on relativityof entrepreneur’s feature and enterprise’s performance in embarking may offer a useful reference. Lussier(2001) confirmed the factors of management skill and the owner’s age should be taken into consideration inthe predicting models for enterprise’s success. The result shows SMEs’ innovation ability is the key factorfor SME’s growing, and the SME’s innovation rest with their owner’s creation (Alberto Marcati et al.,2008). However, some scholars found there was no relationship between entrepreneur’s characteristics andenterprise’s performance (Liu Zhenhua, 2007). Meanwhile, some scholars made study on how owners’personal information influences firms’ financing behavior and ability. Avery R.B. et al. (1998) providednew empirical evidence on the relationship between personal commitments and the allocation of small 55
  2. 2. European Journal of Business and Management www.iiste.orgISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)Vol 4, No.1, 2012business credit, and suggested that personal commitments are important for seeking certain types of loans.Personal commitments appear to be substitutes for business collateral, at least for lines of credit. Yan Jun(2008) asserted that the personal and family management affected the will of external equity financing, andmake most private SMEs mainly rely on internal financing. The network and diversity-orderly structure andtrust of business owners are key factors which affect the availably of financing.As a result, some enterprisers’ features considered as important impact on firms’ credit risk also emerged inthe theory and practice of identification and measurement for SMEs’ credit risk. In terms of the theory,Grunet et al. (2004) emphasizes the effect of the owners’ age and characteristics in evaluation on SMEs’credit risk. Zhou Qiaoyun (2004) chosen the enterprises’ quality and value as indicators to evaluate SMEscredit risk. Ma Jiujie et al. (2004) conducted spot investigation and made empirical analysis on variablesleading to default loans of SMEs, which located at county wide, with Logit model. The results showed that,owners’ features, especially their age, education degree and the percentage of stocks held by the owners,could influence firms’ credit risk considerably. Furthermore, Zhao Ziyi (2006) put forward that thevariables for credit scoring model should include firms and their owners’ feature, and latter may containwhether the owners were investors and had extensive social relationship(but it not been proved). As topractice, American Robert Morris Association (ARMA) and Fair Isaac & Company cooperate to developSMEs credit risk scoring product and absorb owners’ personal qualities into the business’ creditassessment, and testified that the owners’ personal indicators did have closer relationship with the firms’credit level than its operational variables.Accompanying the research on owners’ special features, parts of scholars sensed owners’ credit featuresmade effect on firms’ credit risk, but it had less study on this aspects. Wang Wenying (2004) thought thatSMEs rating should not only concern enterprises, but also take the enterpriser into account as well. Theirpersonal track record of tax duty, laws, social, commercial insurance, personal deposit and debt, and so on,may contribute to predict the SMEs credit risk. Zhang Xin (2005) took advantage of the organizationaldevelop theory and behavior decision theory ‘s frame to analyze the characters of SMEs’ organization andbehavior, proving that the enterprisers significantly impacted on the business, and SMEs’ credit risk wereinfluenced by both personal aspiration and firms operation. It also puts forward hypothesis of relativity forowners’ credit feature, enterprises’ operation and their state of default. Yang Huakun and Sun Liqiong(2006) thought Small companies’ loan were similar to their owners’ personal loan, the aspiration to repaydepended on its credit level. Based on introducing reasonably owners’ credit estimation, they set up bi-variate credit risk model combining enterprisers’ credit and financial indictor for small enterprises with lessthan 2 million loan facility.Limited by the Chinese less developed personal credit system, plenty of researches focused on SMEsenterprisers’ specific personal information. However, there is seldom study on owners’ credit features. Inorder to deeply find out SMEs credit risk’s regular pattern, we paid more attention to the mechanism thathow the SMEs credit risk are influenced by their owners’ credit characteristics, and conducted quantitativeanalysis on the relativity of small and medium enterprisers’ personal credit features and controlledenterprise s’ credit risk.3. Methodology3.1 Data SourceThe data analyzed in this paper was derived from the credit database of SMEs offered by Mintai Institute ofFinance and Banking, Central University of Finance and Economics. We chosen 204 SMEs’ defaultstatuses and their owners’ credit information, 38 of which are default firms and the other 166 are non-default firms. When constructing models below, we sampled elaborately according to the ratio of defaultobservations to good ones hitting on 1:2.3.2 Hypotheses and VariablesAccording to the framework of mechanism mentioned above, we segregate the owners’ characteristics datainto variables of basic features, credit capacity features and credit will features, and we draw out thehypotheses that these four aspects of the owner’s characteristics would play significant role in SMEs’ dailymanagement respectively. Meanwhile, in order to find out the closest personal information with firms’ 56
  3. 3. European Journal of Business and Management www.iiste.orgISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)Vol 4, No.1, 2012credit risk, this paper separate Credit will into four parts, credit history, overdue degree, recent creditperformance (Last 24 months) and self-inquiry frequency (Last 12 months).Credit capacity is also dividedinto four aspects, ability of acquiring credit, structure of personal credit, tension of liability and inquiryfrequency from others (Table I). Table-1: Variables of Owners’ Credit Features Category Subcategory Variable Age/Marriage/Gender/Habitation/Income/Job Basic features - title/Education Amount/proportion of normal loans Amount/proportion of overdue loans Amount/proportion of settled loans Credit history Number/proportion of normal credit cards Number/proportion of bad credit cards Number/proportion of terminated credit cards Average overdue duration of credit cards Maximum overdue duration of credit cards Overdue degree Average overdue duration of loans Credit will Maximum overdue duration of loans Normal repayment frequency of loans Settlement frequency of loans Recent credit performance Normal repayment frequency of credit cards (Last 24 months) Settlement frequency of credit cards Maximum number of repayments without reaching requirement Self-inquiry Self-inquiry frequency frequency Proportion of self-inquiry (Last 12 months) Number of loan accounts/Number of credit institution/Credit line of loans/Credit balance/Guarantee for others/Number of Ability of acquiring loans/Amount of loans/ Number of credit credit card accounts/Number of credit card issuers/Credit line of credit cards/Number of Credit capacity credit cards Number/proportion of housing loans Number/proportion of automobile loans Structure of personal Number/proportion of business loans credit Amount/proportion of housing loans Amount/proportion of automobile loans 57
  4. 4. European Journal of Business and Management www.iiste.orgISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)Vol 4, No.1, 2012 Amount/proportion of business loans Loan balance/Amount of overdraft/Credit balance to credit line ratio/Overdraft amount to credit line ratio/ Guarantee for others to Tension of liability credit line ratio/Number of loans settled in same year/Credit balance to income ratio/ Personal liability to income ratio/ Overdraft amount to income ratio Inquiry frequency/Inquiry frequency for dissension/ Inquiry frequency for credit card approval/ Inquiry Inquiry frequency frequency for credit loan approval/ Inquiry frequency from others for guarantee qualification/ Inquiry frequency for post- loan risk management4. Empirical ResultIn order to certify the relativity of personal credit information and firms’ default probability, the paperimplied binary logistic regression with forward stepwise method and likelihood ratio standard. For thecredibility of empirical results, we have constructed randomly-sampling models for twenty times,according to the ratio of 1:2 for default and non-default enterprises. Out of the twenty models’ results, wechosen the result whose entering variable and variable’s coefficient were stable as the final empirical result(Table-2). Table-2: Empirical Results of Owners’ Credit Features Samples All samples and variables B SE Wald Sig. Age -0.09 0.03 6.06 0.014 Variables entering the model Inquiry frequency for post- 0.33 0.12 7.89 0.005 loan risk management Inquiry frequency for credit 0.06 0.03 4.07 0.044 loan approval Accuracy/True positive rate 75.44%/47.37% Samples All samples (only basic and credit capacity variables) B SE Wald Sig. Age -0.06 0.03 4.82 0.028 Variables entering the model Inquiry frequency for post- 0.32 0.11 8.75 0.003 loan risk management Accuracy/True positive rate 73.81%/35.71% Samples All samples (only basic and credit will variables) B SE Wald Sig. Variables entering the model Age -0.08 0.03 7.71 0.006 Proportion of overdue loans 3.52 1.93 3.33 0.07 58
  5. 5. European Journal of Business and Management www.iiste.orgISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)Vol 4, No.1, 2012 Accuracy/True positive rate 70.75%/20.41%As the empirical results showed, the correlation between owners’ age and enterprises’ default probability isnegative, and inquiry frequency for post-loan risk management and credit loan approval are positive tofirms’ default probability. It means that the higher owners’ age is, the lower their enterprises’ defaultprobability will be, and it is also greater when the inquiry frequency for post-loan risk management andcredit loan approval are larger. Accordingly, as to the enterprises’ credit information, there is somedifference between credit capacity and credit will in the correlation degree with SMEs’ default behavior.For instance, compared with credit will features, the credit capacity indicators have closer relationship withSMEs’ credit risk.Additionally, despite in the same category, different variables have some difference in the significance ofrelativity with SMEs’ credit risk. In terms of owners’ credit will variables, personal credit history andoverdue degree are more important, while self-inquiry frequency (last 12 months) is not significant tofirms’ default behavior. As to owners’ credit capability variables, the indicators of inquiry frequency fromothers are more significant than the variables of structure of personal credit, ability of acquiring credit andtension of liability. It means that appraisement from other intermediaries is important reference for SMEs’credit risk prediction. Moreover, although less than the variables of inquiry frequency from others inrelativity, the variables of structure of personal credit, ability of acquiring credit and tension of liability arealso related to firm credit risk, such as number of credit institution, number of loan accounts and number ofhousing loans. The above findings supply suggestions for taking advantage of personal credit informationin modeling SMEs’ credit risk.5. ConclusionIn order to certify owners’ influence on SMEs’ credit risk and supply a new perspective for indentifyingSMEs’ credit risk, this paper resorted to data from Chinese SMEs, and studied on the relativity betweenowners’ basic information and credit features with firms’ default behaviors.As empirical results showed, as to personal credit information, there is some difference between creditcapacity and credit will in the correlation degree with SMEs’ default behavior. Compared with theindicators of credit will features, the credit capacity indicators share closer relationship with SMEs’ creditrisk. Specifically, out of credit will variables, personal credit history have closest relationship withenterprises’ default probability. For credit capacity indicators, inquiry frequency for post-loan riskmanagement and credit loan approval are most significant risk indicators which should be paid speciallyattention to. It means that appraisement from other intermediaries is important reference for SMEs’ creditrisk estimate.Furthermore, the meaning of this paper perhaps is that, despite the SMEs’ financial information usually isnot available, small and medium enterpriser’s basic information and personal credit data could also beregarded as the useful predictors for the default probability. Thus, this finding may offer a new reliableperspective and valuable references for financial intermediaries’ credit risk management for loans of SMEs.ReferencesAvery R.B., Bostic R. W., Samolyk K. A. (1998), “The Role of Personal Wealth in SmallBusiness Finance”, Journal of Banking and Finance, Vol.22, pp. 1019-1061Alberto Marcati, Gianluigi Guido, Alessandro M. Peluso. (2008), “The Role of SMEEntrepreneurs’ Innovativeness and Personality in the Adoption of Innovations”. ResearchPolicy, Vol.37, pp. 1579-1590Grunet, J., Norden L, Weber M. (2004), “The Role of Non-Financial Factors in InternalCredit Ratings”, Journal of Banking and Finance, Vol 29(2), pp. 89-110Liu Zhenhua. (2007), “Research on the Impacts of Entrepreneur Traits on New VenturePerformance”, Jilin University. 59
  6. 6. European Journal of Business and Management www.iiste.orgISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)Vol 4, No.1, 2012Lussier R.N., Pfeifer S. (2001), “A Cross National Prediction Model for BusinessSuccess”, Journal of Small Business Management, Vol. 39(3), pp. 228-239.Ma Jiujie. (2004), “Empirical analysis on default behavior of SMEs in county wide”,Management World, Vol. 5, pp. 58-67Michael Haliassos. (1995), “Carol C. Bertaut. Why do so Few Hold Stocks?”, TheEconomic Journal, Vol.105 (432), pp. 1110-1129Wang Wenying, Pan Hua. (2004), “A discussion on two aspects and scoring model ofSMEs’ credit. Inquiry Into Economic Problems”, Vol.12, pp. 61-62Yang Huakun, Sun Liqiong. (2006), “The survey and analysis on SMEs’ financingsituation in Chengdu”, Research of Financial and Accounting, Vol. 2, pp. 60-61Yan Jun. (2008), “Research on Financing Behavior of Private Small and Medium-SizedEnterprises Owner in China”, Huazhong Agriculture University.Zhang Xin. (2005), “Study on Credit Risk 2-phase-evaluation Model of SMEs”,Chongqing University.Zhao Ziyi, Zou Kang. (2006), “Credit score model and the loan of SMEs”, Finance andAccounting Monthly, Vol. 2, pp. 34-35Zhou Qiaoyun. (2004), “An approach on scoring of SMEs credit risk, based on the modelof relationship lending”, Journal of Henan College of Financial Management Cadres,Vol. 5, pp. 52-54. 60
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