Bankruptcy Prediction of Privately Held SMEs Using Feature Selection Methods

نویسندگان

چکیده

In this paper, we test alternative feature selection methods for bankruptcy prediction and illustrate their superiority versus popular models used in the literature. We these using a comprehensive dataset of more than one million financial statements from privately held Norwegian SMEs 2006-2017. Our are allowed to choose among 155 accounting-based input variables derived prior find that chosen by an embedded least absolute shrinkage operator (LASSO) method yield best in-sample fit, out-of-sample performance, stability. findings robust discrete hazard with either deep artificial neural network (DNN) or logistic regression (LR) estimation hold across different time periods. show simulation which mimics real-world competitive credit market LASSO predictors improves risk pricing decision making, resulting significantly higher bank profits.

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ژورنال

عنوان ژورنال: Social Science Research Network

سال: 2021

ISSN: ['1556-5068']

DOI: https://doi.org/10.2139/ssrn.3911490