Asia-Pacific Journal of Business Venturing and Entrepreneurship (벤처창업연구)

The Korean Society of Business Venturing (한국벤처창업학회)
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A Comparative Study on Failure Pprediction Models for Small and Medium Manufacturing Company

중소제조기업의 부실예측모형 비교연구

  • Hwangbo, Yun (Graduate School of Global Entrepreneurship, Kookmin University) ;
  • Moon, Jong Geon (Graduate School of Information and Communication, Konkuk University)
  • 황보윤 (국민대 글로벌창업벤처대학원) ;
  • 문종건 (건국대 정보통신대학원)
  • Received : 2016.05.28
  • Accepted : 2016.06.21
  • Published : 2016.06.30
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Abstract

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

본 연구는 코스닥 시장에 상장 폐지된 중소제조기업의 재무자료를 이용하여 다변량 판별분석모형, 로지스틱회귀분석모형 그리고 인공신경망분석모형을 구축하고 이들의 예측력을 비교분석하였다. 표본기업은 2009년에서 2012년까지 상장 폐지된 83개의 부실기업과 83개의 정상기업 총166개사로 정하였다. 166개사 중에서 무작위로 부실기업50개사와 정상기업 50개사 총100개사를 선정하여 훈련용 표본(training data)으로 모형을 구축하는데 사용하였다. 나머지 66개사는 모형의 예측성과를 평가하기 위하여 검증용 표본(test data)으로 사용하였다. 과거 5년 동안의 재무비율 79개 자료로 T-test를 실시하여 5년 연속 유의미한 변수 9개를 선정하고 각각의 모형을 구축하였다. T-test 결과, 부실초기에는 주로 수익성지표들이 부실예측에 주요 변수로 나타났으며 부실 후반에 가면서 안정성지표와 현금흐름지표들이 추가로 유의미한 변수로 나타났다. 모형의 예측력을 비교해 보면 훈련용 표본의 경우, 로지스틱회귀분석모형이 가장 높은 분류 정확도를 보였고, 검증용 표본의 경우에는 인공신경망모형이 가장 높은 분류 정확도를 보였다. 본 연구는 첫째, 부실이 서서히 진행된다는 점을 감안하여 T-test를 실시하여 5년 연속 유의미한 변수로 모형을 구축하여 변수의 시계열적인 측면이 고려되었다는 점과, 둘째, 기존 선행 연구들이 정규성을 무시하고 판별분석모형을 구축하였으나, 본 연구가 정규성 여부를 검정하고 모형을 구축하였다는 점이 차별화된다. 본 연구에 따른 정책적 시사점은 부실기업의 징후는 본 논문에서처럼 대체로 재무제표에 나타나기 때문에 회사에 대한 공시서류의 신회성 확보가 중요하다. 따라서 이런 점에서 회계법인 혹은 세무기장 종사자들의 도덕적 해이을 억제할 수 있는 제도적 장치가 강화되어야 할 것이다.

Keywords

References

  1. Altman, E. I.(1968), Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy, Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
  2. Altman, E. I., Eom, Y. H. & Kim, D. W.(1994), Distress Classification of Korean Firms, New York University, working paper series, FD-94-2, 1-38.
  3. Altman, E. I. & Hotchkiss, E.(2005), Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt, 3rd Edition, Joniley & Sons Inc., Hoboken, New Jersey, 3-6.
  4. Altman, E. I. & McGough, T. P.(1974), Evaluation of a Company as a Going Concern, Journal of Accounting, 138(6), 50-57.
  5. Back, B., Laitinen, T. & Sere, K.(1996), Neural Networks and Genetic Algorithms For Bankruptcy Predictions, Expert Systems With Applications, 11(4), 407-413 https://doi.org/10.1016/S0957-4174(96)00055-3
  6. Bae, K. S. & Jung, S. H.(2011), An empirical study on the prediction model of the delisted firms, Tax Accounting Research. 30. 125-140.
  7. Beaver, W. H.(1966), Financial Ratios as Predictors of Failure, Journal of Accounting Research, 4(3), 71-111.
  8. Bhandari, S. B. & Iyer, R.(2013), Predicting Business Failure Using Cash Flow Statement Based Measures, Managerial Finance, 39(7), 667-676. https://doi.org/10.1108/03074351311323455
  9. Chae, S. I.(2008), Social Science Search Methodology. Seoul : B&M Books press.
  10. Choi, T. S., Kim, H. K. & Kim, S. H.(2002), A Comparison of the Discrimination of Business Failure prediction Models, Journal of the Korean OR and MS Society, 27(2), 1-13.
  11. Craig, A., Aleda, V., Bharath, G., Atanu, C., Jack, M., Tim, H. & Michelle, D.(2013), 2013 Global Manufacturing Competitiveness Index, NY, Deloitte Touche Tomatsu Limited and U.S. Council on Competitiveness, Retrieved Jan 1, 2014 from https://www.deloitte.com/assets/Dcom-Mexico/Local%20Assets/Documents/mx(en-mx)Global_Manufacturing_Competitiveness2013.pdf
  12. Deakin, E. B.(1972), A Discriminant Analysis of Predictors of Business Failure, Journal of Accounting Research, 10(1), 167-179. https://doi.org/10.2307/2490225
  13. Han, H. N.(2006), A Comparative Study on the Predictability of Bankruptcy Model : Artificial Neural Networks Model and Logistics Regression Model", Management includes, Management Research Center of Inha University, 11(1), 215-236.
  14. Heo, M. H. & Lee, Y. K.(2008), Data Mining Modeling and Case, Seoul : Hannare Press.
  15. Hwang, S. H.(1991), A Study on Corporate Failure Prediction, Korean Accounting Review, 12(7), 57-78.
  16. Issah, M.(2012), Predicting Corporate Failure of UK's Listed Companies Comparing Multiple Discriminant Ananlysis and Logistic Regression, Lap LaMBERT Academic Publishing, 32-68.
  17. Jang, Y. K.(2012), Business Analysis, Seoul: Trading Management Sa Press.
  18. Jeong, Y. S.(2003), A Neural Network Model for Bankruptcy Prediction, Doctoral dissertation, Kyung Hee University.
  19. Jeon, S. B. & Kim, M. C.(2000), Reality and Theory on Business Failure, Seoul: Dasan Publishing Company.
  20. Jo, Q. G.(2005), A study on the prediction models of corporate failure, Master's thesis, Dong-Eui University.
  21. Jo, Y. J., Heo, J. & Choi, I. K.(1999), Data Mining Neural Network Analysis, Seoul : SPAA academy Press.
  22. Jun, H. W., Chung, Y. H. & Shin, D. H.(2011), A Study on the Failure Prediction Model of Delisting Firms, Korea International Accounting Review, 38(8), 331-362.
  23. Jung, K. W.(2014), A Study on the default prediction model of SMES after supporting the credit guarantee, Masters's thesis, Hanyang University.
  24. Kang, H. C., Choi, H. S. & Han, S. T.(2010), Introduction to SPSS Data Analysis. Paju: Jayou Academy.
  25. Kim, C. K. & Kim, S. H.(1998), The Business Failure Prediction Model of Small and Medium-sized Firms, Paichai University, Journal of Social Science Research 17, 111-132.
  26. Kim, G. C.(2011), A study on corporate failure predictions by using audit opinions and accounting firm's characteristics, Doctoral dissertation, Soongsil University.
  27. Kim, M. J. & Kang, D. K.(2010), An Empirical Analysis of Boosting Neural Networks for Bankruptcy Prediction, Journal of the Korea Institute of marine Information and Communication Engineering, 14(1),63-69. https://doi.org/10.6109/jkiice.2010.14.1.063
  28. Kim, S. Y.(2006), Prediction of Hotel Bankruptcy Using Multi-variate Discriminant Analysis, Logistic Regression and Artificial Neural Network, Journal of tourism sciences, 30(2), 53-75.
  29. Kim, W. K. & Ban, S. S.(1990), A Study on Muti-variate Firm's Fail Prediction Model: Focus on MDA, Logit and Non-parametric Model, Journal of Management Research. 1. 5-36.
  30. Kueon, S. I.(2012), Corporate Credit Analysis, Seoul: Korea banking Institute press.
  31. Kwon, K. S., Kim, B. J. & Ha, K. S.(2012), An Exploratory Study of Key Success Factors in Public R&D Institutions' Business Supporting Service, Asia-Pacific Journal of Business Venturing and Entrepreneurship, 7(1). 225-232. https://doi.org/10.16972/apjbve.7.1.201203.225
  32. Lee, K, C.(1993), 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), 57-81.
  33. Moon, J. G. & Hwangbo, Y.(2014), An Empirical study on a Firm's Fail Prediction Model by Considering whethere there are Embezzlement, Malpractice and the Largest Shareholder Changes or not, Asia-Pacific Journal of Business Venturing and Entrepreneursip, 9(1), 119-132.
  34. Nam, J. H.(1998), The Cause of Business's Failure and the Analysis of the Bankruptcy Prediction Model, Journal of Money & Finance 12(2), 77-107.
  35. Odom, M & Sharda, R.(1990), A neural networks model for bankruptcy prediction, Proceedings of the IEEE internation Conference on Neural Networks, 2, 163-168.
  36. Oh, H. J.(2005), Usefulness of Credit Rating in the Bankruptcy Prediction Model, Journal of Economic Studies, 23(2), 173-208.
  37. Ohlson. J. A.(1980), Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
  38. Park, H. J.(2008), An empirical study on the failure prediction model of the firms listed on the kosdaq, Doctoral dissertation, Paichai University.
  39. Park, S. E.(2002), Multi-variate Discriminant Analysis and Logistic Regression Analysis, Seoul: Minyoung Sa Press.
  40. Razali, N. M. & Wah, Y. B.(2011), Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests, Journal of Statistical Modeling and Analytics, 2(1), 21-33.
  41. Sarah, A. & Shahriar, M.(2011), Compare failure prediction nodels based on feature selection technique: Empirical case from Iran, Procedia Computer Science, 3, 568-573. https://doi.org/10.1016/j.procs.2010.12.094