횡령ㆍ배임 및 최대주주변경을 고려한 부실기업예측모형 연구
An empirical study on a firm’s fail prediction model by considering whether there are embezzlement, malpractice and the largest shareholder changes or not
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초록
본 연구는 코스닥 기업의 횡령 ∙ 배임 및 최대주주변경을 고려한 부실기업 예측 모형을 연구하였다. 모형개발을 위해 2009년부터 2012년까지 코스닥시장에서 상장폐지된 제조기업 83개사를 부실기업표본으로 선정하였고. 정상기업 표본은 같은 기간 코스닥시장에 상장되어 정상적인 영업활동을 하고 있으며 부실기업과 동일아이템 혹은 동종업종에 속한 83개사를 선정하여 총 166개사를 쌍대표본 추출법으로 구성하였다. 본 연구는 상기 표본기업의 상장폐지 직전 5년간 재무비율 80개를 선정하여 T-test를 실시하여 유의미한 변수 중에서 5년 연속 출현한 19개를 도출하였고 전진선택법을 이용하여 로지스틱 회귀분석 모형식을 추정하였다. 기존 연구에서는 상장폐지 직전 3년간 자료만을 분석하였으나 본 연구는 직전 5년간 자료를 분석하여 기업이 부실화되는 초기과정부터 어떤 유의미한 재무적 특성이 시차를 두고 부실화에 영향을 미치는 지를 연구했다는 점과 선행 연구에서 시도되지 않은 횡령ㆍ배임과 최대주주변경이라는 비재무적인 특성을 더미변수로써 고려된 부실기업예측모형을 구축하여 그 정보의 유용함을 실증적으로 분석한 점이 기존 선행연구들과 차별화 된다. 연구결과, 더미변수를 추가한 모형의 판별력은 T-1년에 95.2%, T-2년에 88.0%, T-3년에 81.3%, T-4년에 79.5%, T-5년에 74.7%로 나타났으며, 상장폐지 년도에 가까워지면서 판별력도 점차 올라갔으며 기존 선행연구의 결과보다도 대체로 높은 판별력을 보였다. 본 연구가 사전에 부실화될 가능성이 높은 기업을 찾아냄으로써 해당기업은 물론 투자자, 금융기관 및 기타 이해관계자들의 피해를 조금이나마 줄여 줄 수 있을 것이라고 기대된다.
Abstract
This study analyzed the failure prediction model of the firms listed on the KOSDAQ by considering whether there are embezzlement , malpractice and the largest shareholder changes or not. This study composed a total of 166 firms by using two-paired sampling method. For sample of failed firm, 83 manufacturing firms which delisted on KOSDAQ market for 4 years from 2009 to 2012 are selected. For sample of normal firm, 83 firms (with same item or same business as failed firm) that are listed on KOSDAQ market and perform normal business activities during the same period (from 2009 to 2012) are selected. This study selected 80 financial ratios for 5 years immediately preceding from delisting of sample firm above and conducted T-test to derive 19 of them which emerged for five consecutive years among significant variables and used forward selection to estimate logistic regression model. While the precedent studies only analyzed the data of three years immediately preceding the delisting, this study analyzes data of five years immediately preceding the delisting. This study is distinct from existing previous studies that it researches which significant financial characteristic influences the insolvency from the initial phase of insolvent firm with time lag and it also empirically analyzes the usefulness of data by building a firm’s fail prediction model which considered embezzlement/malpractice and the largest shareholder changes as dummy variable(non-financial characteristics). The accuracy of classification of the prediction model with dummy variable appeared 95.2% in year T-1, 88.0% in year T-2, 81.3% in year T-3, 79.5% in year T-4, and 74.7% in year T-5. It increased as year of delisting approaches and showed generally higher the accuracy of classification than the results of existing previous studies. This study expects to reduce the damage of not only the firm but also investors, financial institutions and other stakeholders by finding the firm with high potential to fail in advance.
참고문헌 (27)
- Altman, E. I., "Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy", Journal of Finance, 23(4), pp.589~609 (1968)
- Altman, E. I., "Evluation of a Company as a Going Concern", Journal of Accounting, 138(6), pp.50~57 (1974)
- Beaver, W. H., "Financial Ratios as Predictors of Failure", Journal of Accounting Research, 4(3), pp.71~111 (1966)
- Bhandari, S. B., "Predicting Business Failure Using Cash Flow Statement Based Measures", Managerial Finance, 39(7), pp.667~676 (2013)
- Cho, K. H., "An empirical research on the effects of corporate bankruptcy forecasting by audit report and audit quality", 박사, pp.2012
- 최태성, "부실기업예측모형의 판별력 비교", 한국경영과학회지, 27(2), pp.1~13 (2002), 한국경영과학회
- Chun, H., "An empirical study on the financial distresses and changes in the largest shareholder in the information technology industry", 석사, pp.2009
- "2013 Global Manufacturing Competitiveness Index" (2013)[URL]
- "Retrieval and Transfer System in Financial Supervisory Service"[URL]
- Deakin, E. B., "A Discriminant Analysis of Predictors of Business Failure", Journal of Accounting Research, 10(1), pp.167~179 (1972)
- "A paper on the result of operating actual investigation in KOSDAQ 2012, Disclosure System Division" (2013)[URL]
- Foster, G., "Financial Statement Analysis" (1986), Prentice Hall
- Hwang, S. H., "A Study on Corporate Failure Prediction", Korean Accounting Review, 12(7), pp.57~78 (1991)
- Jeon, S. B., "Reality and Theory on Business Failure" (2000), Dasan Publishing Company
- Jo, Q. G., "A study on the prediction models of coporate-failure", 석사, pp.2005
- 전현우, "상장폐지기업의 부실예측모형에 관한 연구 -거래소시장을 중심으로-", 국제회계연구, 38(38), pp.331~362 (2011), 한국국제회계학회
- Kim, C. K., "The Business Failure Prediction Model of Small and Medium-sized Firms", Journal of Social Science Research, 17, pp.111~132 (1998), Paichai University
- Kim, G. C., "A study on corporate failure predictions by using audit opinions and accounting firm's characteristics", 박사, pp.2011
- 김종운, "벤처기업의 외부협력이 경영성과에 미치는 영향", 벤처창업연구, 7(1), pp.215~224 (2012), 한국벤처창업학회
- 권경섭, "국가연구개발기관 기술사업화 종합지원사업 성공요인에 관한 탐색적 연구 -세라믹히든챔피언사업을 중심으로-", 벤처창업연구, 7(1), pp.225~232 (2012), 한국벤처창업학회
- Lee, K, C., "A Comparative Study on the Bankruptcy Prediction Power of Statistical Model and AI Models : MDA, Inductive Learning, Neural Network", Journal of the Korean OR and MS Society, 18(2), pp.57~81 (1993)
- Nam, Joo. Ha., "The Cause of Business's Failure and the Analysis of the Bankruptcy Prediction Model", Journal of Money & Finance, 12(2), pp.77~107 (1998)
- 오희장, "도산예측에서 신용등급정보의 유용성", 경제연구, 23(2), pp.173~208 (2005), 한국경제통상학회
- Ohlson. J. A., "Financial Ratios and the Probabilistic Prediction of Bankruptcy", Journal of Accounting Research, 18(1), pp.109~131 (1980)
- Park, H. J., "An empirical study on the failure prediction model of the firms listed on the kosdaq", 박사, pp.2008
- Weston, J. F., "Instructor’s Manual to A company Managerial Finance", pp.961~ (1981), The Dryden Press
- Zmijewski, M. E., "Methodological Issues Related to the Estimation of Financial Distress Prediction Models", Journal of Accounting Research, 22, pp.59~82 (1984)