Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Predicting Default of Construction Companies Using Bayesian Probabilistic Approach

베이지안 확률적 접근법을 이용한 건설업체 부도 예측에 관한 연구

  • Hong, Sungmoon (Department of Architectural Engineering, Hanyang University) ;
  • Hwang, Jaeyeon (Department of Architectural Engineering, Hanyang University) ;
  • Kwon, Taewhan (Department of Architectural Engineering, Hanyang University) ;
  • Kim, Juhyung (Department of Architectural Engineering, Hanyang University) ;
  • Kim, Jaejun (Department of Architectural Engineering, Hanyang University)
  • Received : 2016.04.29
  • Accepted : 2016.08.16
  • Published : 2016.09.30
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Abstract

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

주수급자 역할을 하는 건설기업의 부실화는 발주자에게 공사계약 미이행에 따른 피해를 초래할 수 있고, 전문건설업체 및 자재공급업체의 재무건전성에 악영향을 줄 수 있다. 건설업은 프로젝트를 수주하고 진도에 따라 기성을 받는 현금흐름의 재무적 특성이 존재하고, 사업 진행 중의 부실화는 투입한 자금의 손실로 이어질 수 있으므로 건설업체의 부실화 예측은 중요하다. 국내 건설업체의 부실화 예측은 90년도 초 미국에서 개발된 KMV (Kealhofer McQuown and Vasicek)사의 KMV모형으로 수행되는 경우도 있지만, 이 모형은 일반적인 기업 및 은행의 신용위험 평가에 개발되어져 건설기업 예측력에는 부족함이 있다. 또한, KMV값의 부도확률 예측력에 대해서는 분석대상의 기업수 및 데이터의 부족으로 의문점이 지속적으로 제기되고 있다. 따라서 이러한 의문점을 해결하기 위해 기존 부도예측확률모형에 베이지안 확률적 접근법(Bayesian Probabilistic Approach)을 접목하고자 한다. 베이즈 통계학의 사전확률(Prior Probability)만 적절하게 예측가능하다면 적은 정보라도 증거에 대한 조건부 획득으로 신뢰성 있는 사후확률(Posterior Probability)을 예측할 수 있기 때문이다. 이에 본 연구에서는 기존 부도예측확률모형에 베이지안 확률적 접근법을 활용하여 예상부도확률(Expected Default Frequency, EDF)을 측정하고, 기존 모형의 예상부도확률과 비교하여 정확성을 예측하고자 한다.

Keywords

References

  1. Altman, E. I. (1968). "Finacial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy." Journal of Finance, 23(4), pp. 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
  2. Black F., and Scholes M. (1973). "Pricing of option and corporate liabilities." Journal of Political Economy, 81, pp. 637-659. https://doi.org/10.1086/260062
  3. Beaver, W. (1966). Financial Ratios as Predictors of Failures. Empirical Research in Accounting : Selected Studies.
  4. Cho, S. and Ryu, I. (2007). "Accounting Information and Prediction of Corporate Failure During a Recession." Journal of Business Research, 22(1), pp. 1-32. https://doi.org/10.1016/0148-2963(91)90008-L
  5. Choi, I, Yoo, S, and Kim, J. (2013). "Measuring Default Risk of Construction Company Using KMV-model." Journal of the Architectural Institute of Korea Structure & Construction, 29(2), pp. 67-74.
  6. Collin-Dufresne, P. and Goldstein, R.S. (2001). "Do Credit Spreads Reflect Stationary Leverage Ratios?" Journal of Finance, 56(5), pp. 1929-1957. https://doi.org/10.1111/0022-1082.00395
  7. Gelman, A. ,Curlin, J.B.,Stern, H.S. and Rubin, D.B. (2000). Bayesian Data Analysis. Champman & Hall, New York.
  8. Jarrow, R.A. and Turnbull, S.M. (1995). "Pricing Derivatives on Financial Securities Subject to Credit Risk." Journal of Finance, 50(1), pp. 53-85. https://doi.org/10.1111/j.1540-6261.1995.tb05167.x
  9. Kang, M. and Lee, J. (2009). "Survival Analysis of Small and Medium Size Construction Enterprises Using Cox Proportional Hazards Model." Journal of Korea Real Estate Analysts Association, 15(2), pp. 41-57.
  10. Kim, J. (2009). "Development and Application of a Default Prediction Model Using a Bayesian Probabilistic Approach." M.A. degree, Hanyang University.
  11. Kim, J. H and Nam, J. S (2014). "Impacts of Macroeconomic Fluctuations on Insolvency: Case of Korean Construction Companies." Journal of Management in Engineering. pp. 1-9
  12. Kook, C. and Jeong, w. (2002). "Based on Stock Price Information = A Study on Forecasting Corporate Default." Asian Review of Financial Research, 15(1), pp. 217-249.
  13. Leland, H.E. and Toft, K.B. (1996). "Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads." Journal of Finance, 51(3), pp. 987-1019. https://doi.org/10.1111/j.1540-6261.1996.tb02714.x
  14. Longstaff, F.A. and Schwartz, E.S. (1995). "A Simple Approach to Valuing Risky Fixed and Floating Rate Debt." Journal of Finance, 50(3), pp. 789-819. https://doi.org/10.1111/j.1540-6261.1995.tb04037.x
  15. Merton, R.C. (1973). "Theory of rational option pricing." Bell Journal of Economics and management Science 4, pp. 141-183. https://doi.org/10.2307/3003143
  16. Merton, R.C. (1974). "On the Pricing of Corporate Debt : The Risk Structure of Interest Rates." Journal of Finance. 29(2), pp. 449-470.
  17. Ohlson, J.S. (1980). "Financial Ratios and Probabilistic Prediction of Bankruptcy." Journal of Accounting Research, 18(1), pp. 109-131. https://doi.org/10.2307/2490395
  18. Park, K. and Kim, H. (2011). "A study on the professional construction company bankruptcy prediction model." Korea Research Institute of Construction Policy.
  19. Sim, J. (2009). Credit risk modeling (structural model and reduced model). M.A. degree, Yonsei University.
  20. Simonian, J. (2011). "A Bayesian Approach to Building Robust Structural Credit Default Models." Applied Economics Letters, 18, pp. 1397-1400. https://doi.org/10.1080/13504851.2010.539528
  21. Zmijewski, M.E. (1984). "Methodological Issues Related to the Estimation of Financial Distress Prediction Models." Journal of Accounting Research, 22(1), pp. 59-82. https://doi.org/10.2307/2490859
  22. Han, J. (2013). "Analysis of Factors Influencing the Construction Business Management Success. Korean." Journal of Construction Engineering and Management, 14(4), pp. 46-54. https://doi.org/10.6106/KJCEM.2013.14.4.046