비상장 중소기업의 부실예측과 현금보유수준 (Cash Holdings and Loan Default Predictions for Unlisted SMEs in Korea)

서지정보

· 발행기관 : 한국중소기업학회
· 수록지 정보 : 중소기업연구 / 31권 / 4호 / 41 ~ 65페이지
· 저자명 : 나인철, 김성규

초록

유동성 확보가 중요한 비상장 중소기업의 신용위험 평가 내지 기업부실 예측에서 가장 본원적인 유동성 지표인 현금보유수준의 역할을 검토한 연구는 많지 않은데, 최근 Altman and Sabato(2007, A&S)는 바젤Ⅱ에 따른 고급내부등급법(A-IRB) 적용과 관련하여 중소기업을 위한 신용평가 모형의 개발이 중요함을 강조하며 그에 유용한 모형을 제시하고 있다. A&S 모형의 특징 중 하나는 중소기업 재무적 상황의 유동성 측면을 현금보유수준으로 포착하고 있는 것이다. 그리고 나인철․김성규(2008)는 국내 비상장 중소기업의 현금보유수준을 설명하는 요인을 검토하면서, 현금보유수준이 높은 기업이 우량하거나 혹은 경영환경변화에 신축적으로 잘 대응하는 기업의 특성을 나타내기에 비상장 중소기업의 지속경영가능성을 사전적으로 판단하는 분석을 할 때 그간 활용하지 않았던 현금보유수준의 증분 판별력을 추가로 검토할 필요성을 제기하고 있다. 본 연구는 이 두 연구 결과를 바탕으로 하여, 현금보유수준이 비상장 중소기업의 부실예측에 유용한 변수인가를 실증적으로 검토한다.
본 연구에서의 부실사건은 비상장 중소기업이 대출 원리금을 적시에 상환하지 못하여 발생하는 신용사고(loan defaults)로서 먼저, 이러한 부실사건의 발생을 예측함에 있어 부실확률과 음(-)의 관계를 가질 것으로 기대하는 현금보유수준을 포함한 다섯 개의 재무비율로 구성된 A&S 모형이 적용될 수 있는지 여부를 검토한다. 이때 A&S 모형의 성과를 국내외 중소기업 부실예측에 사용한, 현금보유수준 변수를 활용하지 않은 기존 모형들과 비교함으로써 A&S 모형의 잠재적 우수성을 살펴본다. 이러한 목적으로 본 연구에서는 Begley et al.(1996)의 중소기업 형 Z-score 모형, Ohlson(1980)의 모형, Beaver et al.(2005)의 모형 및 기존 신용평가 실무에서 사용한 바 있는 도산거리(distance-to-default) 분석을 선정한다. 이어서 A&S를 포함한 이들 모형 혹은 분석에서 제시하는 재무변수 19개를 모두 이용하여 새로이 모형을 추정하는 경우 그 최종모형 설명변수의 하나로서 현금보유수준이 선정되며 또한 부실확률과 음(-)의 관계를 나타내는지를 살펴본다. 이는 중소기업의 재무적 상황을 A&S와는 다소 다른 방식으로 분석하더라도 그 부실예측에 현금보유수준이 변별력을 가지는 변수로 포착되는지 여부를 검토하기 위함이다.
자료의 분석방법은 동태적 현상인 기업부실을 예측하는데 활용하는 생존분석방법의 하나로서, 특정 연도에 발생한 부실･건실 여부를 그 직전까지 확보되는 연차 재무제표에서 추출한 재무비율로 예측하고자 하는 본 연구의 상황에 적합하다고 판단되는 Clog-log(complementary log-log) 회귀분석이다. 표본은 31,592개 비상장 중소기업의 2001년～2006년 재무자료 98,816개에서 추출한 모형 추정용 표본(estimation sample) 29,644개와 추정된 모형의 예측성과를 검토한 확인용 표본(holdout sample) 20,751개인데, 각 표본에서의 부실비율이 5.5%가 되도록 층화임의추출하였다. 본 연구에서는 표본 최초연도인 2001년을 IMF에의 구제금융신청 이후의 금융시장 불안정이 충분히 해소된 시점이라고 보고, 그 이후 건실․부실판정 연도까지의 생존연도 경과를 연도더미로 포착하였다.
실증 분석 결과, A&S 모형과 19개 변수를 투입하여 추정한 최종모형 모두에서 현금보유수준은 부실확률과 유의적인 음(-)의 관계를 가지는 것으로 나타났고, 건설기업과 다른 기업을 구분하여 추정한 업종별 모형에서도 현금보유수준의 역할이 유사하게 검출되었다. 그리고 현금보유수준이 포함된 부실예측 모형의 추정용 표본과 확인용 표본에서의 성과측정치인 AUC(Area Under the ROC Curve)가 유사한 자료를 활용한 기존 연구에서의 예측성과에 비하여 양호한 수준인 것으로 판단되었다. 이러한 실증분석 결과를 종합할 때 비상장 중소기업의 현금보유수준은 그 부실예측에 유용한 변수라고 판단되기에, 중소기업의 유동성측면을 포착하는 변수임에도 그간 도외시한 현금보유수준을 보다 적극적으로 활용하는 연구와 실무개발을 수행할 필요성을 제기한다. 또한 본 연구가 가지는 주된 한계는 비재무자료를 활용하지 못한 것인데, 향후 기업 특성에 대한 제반 정성적 자료가 풍부히 반영한 상황에서도 과연 현금보유수준의 역할을 검출한 본 연구의 결과가 재현되는지를 검토하는 노력도 필요하다고 판단한다.

영어초록

This paper addresses whether the level of cash holdings(CASH, hereafter) is useful in predicting loan defaults for unlisted SMEs in Korea. While the necessity of incorporating liquidity measures in predicting a business failure is emphasized in almost all the models developed for that purpose, the prime measure of liquidity, CASH, is rarely considered both in academe and in practice. Research findings of two papers are instrumental in launching this study. One is Altman and Sabato(2007, A&S hereafter), which replace working capital ratio with CASH as a liquidity proxy in their model to assess credit risk for U.S. SMEs. The other is Na and Kim(2008, written in Korean), which suggest that Korean SMEs with higher level of cash holdings tend to possess characteristics of operationally and financially more sound firms.
The main claim of this paper is that CASH would have a robust negative relation with the probability of loan default, which is the incidence of business failure in this study. To see if the claim comes across as an empirical regularity, this paper initially examines the sign and the significance level of CASH in the model (model 1 in Tables 5～7) that utilizes five financial ratios including CASH as proposed in A&S.
Since there is a possibility that CASH would not be selected in a model which starts from a different set of financial ratios, this paper collects variables from diverse sources of established provenance. This is the approach taken in Xu and Zhang(2009). While they predict business failures of Japanese listed firms with variables suggested in Altman(1968), Ohlson(1980), and distance-to-default literature, this paper predicts business failures of Korean unlisted SMEs with variables from five sources：Altman's model as modified for SMEs by Begley et al.(1996), Ohlson(1980), Beaver et al.(2005) which employed Shumway(2001)'s estimation method, distance-to-default practice, and finally A&S. A total of 19 variables are collected from these sources. The estimation is proceeded in two phases. Firstly, the final version of the estimated model whose classificatory performance is at least comparable to A&S's should include CASH as one of its predictors. Secondly, CASH in that model (model 6 in Tables 5～7) should display significantly negative coefficient.
With limited ability to capitalize on the direct provision of funds from capital market, SMEs continuously solicit loans from financial institutions to stay in business and to support their growth activities. They can only secure new loans by maintaining good credit standings. When a firm defaults any part of its current loan portfolio, opportunity for tapping funds will vanish away and viability as a sustainable entity will shortly be challenged. Loan defaults can occur at any point in time during the year. Time lapsed from a specified starting point -survival time- is continuous by nature. The probability for a firm to survive up till it defaults a loan may best be estimated by the proportional hazards regression or the Cox regression. In the case of listed firms, this probability measured on a timely basis will be estimated by variables capturing up-to-date information from capital market where their securities are traded, as well as by financial ratios.
Risk analyses of unlisted SMEs, however, can not benefit from continuous supply of information from capital market. They generally rely on annual financial statements. A much confronted setting in practice is the one in which a loan officer predicts a business failure within an upcoming year with currently available set of financial ratios for the firm. Survival time is reduced to an observation of failure event for the year in concern. When continuous durations are grouped into occurrences of failure events during regularly spaced intervals of time, their probabilities can properly be assessed by the complementary log-log transformations of the event probabilities according to Kalbfleisch and Prentice(2002). In our complementary log-log regression, the response variable is expressed, quite literally, as log[-log(1-p)], where p stands for the probability that a failure occurs in a specific year. When the set of explanatory variables includes year dummies, regressing the response variable on this set is tantamount to estimating parameters of the proportional hazards regression(Allison(1995, 1999), Beck et al.(1998)). This regression is referred to as Clog-log regression in the literature.
Data is extracted from the database maintained for and used in rendering guarantees by Korea Credit Guarantee Fund (KODIT). Quasi-population of this study is 98,816 observations for the years 2001 through 2006 from 31,592 unlisted SMEs with total assets of at least ￦1 billion and annual sales of at most ￦60 billion(approximately US$50 million equivalents). Their fiscal years end at the end of December and their industries encompass manufacturing, construction, wholesale, retail, or services other than of financial nature. Roughly 30% of the quasi-population (29,644 firm/years) is selected as an estimation sample and 30% again of the remainder in the quasi-population (20,751 firm/years) is selected as a holdout sample, while stratified random sampling technique is applied to keep default ratio in each sample at 5.5%, the most current five-year average figure for newly issued guarantees by KODIT to the unlisted SMEs.
When the estimation sample is partitioned into two groups, i.e. default firm/years and non-default or solvent firm/years, CASH in the former group is significantly lower than that in the latter group with a t-statistic of -12.18. Further, the classification result of univariate logistic regression is about 56% concordant with the actual default status. These results reported in <Table 4> hint at potential importance of CASH in distinguishing delinquent accounts from active ones.
The five variables including CASH identified in A&S are all significant and all their coefficients have the expected signs in a Clog-log regression applied to the estimation sample. Specifically, a higher level of CASH is found to be associated with a lower probability of default. For comparison purpose, concordant % is reported in <Table 5>. A&S (model 1) obtains 72.4%, which is an improvement of about 4% points over the result reported in a contemporary study which employs the same database. Model 6 is the final version of stepwise Clog-log regressions. The result reported in <Table 5> shows that a total of seven financial ratios are selected to be effective in estimating loan defaults. All of them are correctly specified in terms of the signs of the coefficients and their levels of significance. While model 1 and model 6 are different in their fine details, they share certain common features. On such feature to be noticed is the consistently significant role of CASH in estimating defaults for unlisted SMEs. A preferred measure of discriminatory power under the Basel II regime is AUC(area under the ROC curve) which combines the model's records of Hit Rates and False Alarm Rates. The figures for AUC seem to suggest that model 6(75.1%) performs slightly better than model 1(73.1%).
When the models 1 and 6 are estimated with industry dummies as exhorted in Chava and Jarrow(2004), the dummy variable for construction firm/years registers significance. Noticing this result, separate estimations of each model are performed for construction sub-sample and for non-construction sub-sample. Results of industry-specific regressions shown in Table 6 also support the importance of CASH in estimating default probabilities.
The estimated coefficients from each model are applied to the holdout sample with 20,751 firm/years and to its industry-specific sub-samples. Validation results are satisfactorily in concert with the estimation results. <Table 7> supports the claim that CASH exerts systematic influence in predicting defaults. All these pieces of empirical evidence seem to collaborate in evincing the usefulness of CASH in predicting loan defaults of unlisted SMEs in Korea.
This study does not claim that CASH should be regarded as one of the predictors for business failures for all classes of firms. It is instead an attempt to recognize, at least in assessing the credit risk for unlisted SMEs, the importance of CASH, which had not been attended to until A&S ascertained its proper role.
One of the caveats of this study is the lack of qualitative characteristics represented in the models examined. An interesting remedial extension would be the study which endeavors to see if the role of CASH in predicting business failure of SMEs is replicated in a setting where financial variables are competing with reasonable proxies for qualitative characteristics linked to business sustainability.

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