Logistic Lasso를 이용한 에너지․환경산업 기업부도 예측
Analyzing Bankruptcy Prediction in Energy and Environment Industries Using Logistic Lasso
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공공성이 높은 에너지산업과 환경산업(이하 에너지환경산업)에서 기업부도의사전 예측은 갑작스러운 공급 충격의 가능성을 예측하고 대비할 수 있게 하며이를 통해 경제 전반의 충격과 국민 후생 저하를 완화시킬 수 있다는 점에서중요하다. 본 연구는 에너지환경산업에 대한 기업부도 예측모형을 분석한다. 추정모형으로는 로짓모형을 이용하고 부도예측 선행연구에서 자주 이용되는 51개재무 변수를 초기 설명변수로 고려하였다. 그리고 이들 설명변수에 에너지환경산업 더미변수를 곱하여 추가한 경우와 추가하지 않은 경우의 예측력을 비교하였다. 한편 설명변수 수가 많기 때문에 예상되는 모형의 과다적합(over-fitting)과다중공선성 문제를 완화하기 위해 모형축소방법인 lasso(Tibshirani, 1996)를적용하여 로짓 모형과 예측력을 비교하였다. 분석결과, 에너지환경산업 더미변수를고려한 로짓 모형에 lasso를 적용한 logistic lasso모형의 예측력이 가장 우수한것으로 나타났다.
Abstract
Since energy and environment industries have much publicity,predicting the industries’ corporate bankruptcy in advance is importantin that it can predict and prepare for the possibility of sudden supplyshocks, thereby alleviating the overall economic shock anddeteriorating public welfare. This study analyzes prediction of corporatebankruptcy in energy and environment industries. The logit model isused as basic estimation model, in which 51 financial variablesfrequently used in the previous studies are considered as initialexplanatory variables. And we compare the predictive power betweentwo models, one with and another without the variables multiplied bythe industries' dummy variable as additional variables. On the otherhand, Lasso(Tibshirani, 1996), a model shrinkage method, is applied toalleviate the problem of over-fitting and multicollinearity due to manyexplanatory variables used in the model. The results showed that thelogistic lasso model including the dummy variables of the energyenvironment industry was the best in all predictive measures.
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