주식유동성과 금융경제 지표를 결합한 스태킹앙상블 기반의 기업 부도예측에 관한 연구

초록

Purpose: This study aims to predict corporate defaults by implementing several state-or-the-art machine-learning techniques and assess whether a hybrid feature selection strategy applied to features consisting of both accounting-based and non-financial features enhances the predictive ability. Methods: We propose a stacking ensemble model with stock liquidity and financial conomic indicators augmented to accounting-based features to predict default risk. A hybrid approach to feature selection for eliminating less relevant features is also employed to further increase predictive accuracy. Results: Experimental results show that our proposed method yields the highest performance regardless of misclassificaion cost considerations, and the results are consisntent with a few recent findings that utilize stacking ensemble models. Conclusion: This study shows that predictive accuracy of corporate defualt predition models can be enhanced by both augmenting non-financial fuetures such as stock liquidity and financial conomic indicators to accounting-based features and using a hybrid approach to feature selecion. Keywords：Corporate default prediction, Data imbalance, Feature selection, Stacking ensembles

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