미관측 공통요인과 전이효과를 고려한 기업실패 예측 연구: 머신러닝과 전통적인 모형 비교
Corporate Failure Prediction With Frailty and Contagion: A Comparison of Traditional and Machine Learning Models
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초록
본 연구는 기업실패 예측모형의 표본 외 예측력을 정확도, 정보 내용, 경제적 가치 등 세 가지 측면에서 비교・분석한다. 1980년부터 2020년까지의 미국 기업 자료를 분석한 결과, 미관측 공통요인과 전이효과를 고려한 모형이 전통적인 로짓 모형보다 우수한 예측력을 보인다. 특히, 미관측 공통요인을 124개의 거시금융 시계열로 반영하고 변수 간의 비선형적 관계를 구조화한 Neural Network 모형이 가장 높은 AUC를 기록하며 상대적으로 우월한 예측 정보를 제공한다. 대출 시장 시뮬레이션에 따르면 동 예측모형의 사용이 금융기관의 건전성과 수익성을 크게 개선하는 것으로 나타난다.
Abstract
This study compares the out-of-sample predictive performance of different corporate failure models. Using data from U.S. firms from 1980 to 2020, we found that machine learning models, considering frailty and contagion, outperform the traditional Logit model. Specifically, the neural network with one layer of 32 nodes provides superior information about corporate defaults, yielding the highest AUC. Simulation in the corporate loan market suggests that a financial institution can improve both its soundness and profitability by adopting the advanced model.
참고문헌 (28)
- 엄하늘, "머신러닝 기반 기업부도위험 예측모델 검증 및 정책적 제언: 스태킹 앙상블 모델을 통한 개선을 중심으로", 지능정보연구, 26(2), pp.105~129 (2020), 한국지능정보시스템학회
- 이상헌, "저축은행 부실예측 모형의 머신러닝 기법으로의 전환과 예측력 개선의 주요 요인", 금융안정연구, 22(2), pp.87~135 (2021), 예금보험공사[DOI]
- Altman, E. I., "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", Journal of Finance, 23(4), pp.589~609 (1968)[DOI]
- Azizpour, S., "Exploring the sources of default clustering", Journal of Financial Economics, 129(1), pp.154~183 (2018)[DOI]
- Barboza, F., "Machine learning models and bankruptcy prediction", Expert Systems with Applications, 83, pp.405~417 (2017)[DOI]
- Begley, J., "Bankruptcy classification errors in the 1980s: An empirical analysis of Altman's and Ohlson's models", Review of accounting Studies, 1, pp.267~284 (1996)[DOI]
- Bharath, S. T., "Forecasting default with the Merton distance to default model", The Review of Financial Studies, 21(3), pp.1339~1369 (2008)[DOI]
- Bianchi, D., "Bond risk premiums with machine learning", The Review of Financial Studies, 34(2), pp.1046~1089 (2021)[DOI]
- Blöchlinger, A., "Economic benefit of powerful credit scoring", Journal of Banking & Finance, 30(3), pp.851~873 (2006)[DOI]
- Understanding the behavior of distressed stocks, "Boualam, Y.", Jacobs Levy Equity Management Center, ~ (2020)
- Campbell, J. Y., "In search of distress risk", The Journal of finance, 63(6), pp.2899~2939 (2008)[DOI]
- Chava, S., "Bankruptcy prediction with industry effects", Review of Finance, 8(4), pp.537~569 (2005)[DOI]
- Crosbie, P., "Modeling default risk: Modeling methodology" (2003), KMV corporation
- G. Cybenko, "Approximation by superpositions of a sigmoidal function", Mathematics of Control, Signals, and Systems, 5(4), pp.455~455 (1992), Springer Science and Business Media LLC[DOI]
- Duffie, D., "Frailty correlated default", The Journal of Finance, 64(5), pp.2089~2123 (2009)[DOI]
- Gu, S., "Empirical asset pricing via machine learning", The Review of Financial Studies, 33(5), pp.2223~2273 (2020)[DOI]
- Hillegeist, S. A., "Assessing the probability of bankruptcy", Review of accounting studies, 9, pp.5~34 (2004)[DOI]
- Hornik, K., "Multilayer feedforward networks are universal approximators", Neural Networks, 2, pp.359~366 (1989)[DOI]
- Jorion, P., "Good and bad credit contagion : Evidence from credit default swaps", Journal of Financial Economics, 84(3), pp.860~883 (2007)[DOI]
- Koopman, S. J., "Modeling frailty-correlated defaults using many macroeconomic covariates", Journal of Econometrics, 162(2), pp.312~325 (2011)[DOI]
- McCracken, M. W., "FRED-MD : A monthly database for macroeconomic research", Journal of Business & Economic Statistics, 34(4), pp.574~589 (2016)[DOI]
- Merton, R. C., "On the pricing of corporate debt : The risk structure of interest rates", The Journal of Finance, 29(2), pp.449~470 (1974)[DOI]
- Nanni, L., "An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring", Expert systems with applications, 36(2), pp.3028~3033 (2009)[DOI]
- James A. Ohlson, "Financial Ratios and the Probabilistic Prediction of Bankruptcy", Journal of Accounting Research, 18(1), pp.109~131 (1980), JSTOR[DOI]
- Shumway, T., "Forecasting bankruptcy more accurately : A simple hazard model", The journal of business, 74(1), pp.101~124 (2001)[DOI]
- Vassalou, M., "Default risk in equity returns", The journal of finance, 59(2), pp.831~868 (2004)[DOI]
- Wang, G., "A comparative assessment of ensemble learning for credit scoring", Expert systems with applications, 38(1), pp.223~230 (2011)[DOI]
- Wang, G., "An improved boosting based on feature selection for corporate bankruptcy prediction", Expert Systems with Applications, 41(5), pp.2353~2361 (2014)[DOI]
