Predicting mortgage early delinquency with machine learning methods

Shunqin Chen, Zhengfeng Guo and Xinlei Zhao

European Journal of Operational Research, 2021, vol. 290, issue 1, 358-372

Abstract: This paper investigates the performance of thirteen methods for modelling and predicting mortgage early delinquency probabilities. These models include variants of logit models, some commonly used machine learning methods, and variants of ensemble models. We find that heterogenous ensemble methods lead other methods in the training, out-of-sample, and out-of-time datasets in terms of risk classification. Nonetheless, various predictive accuracy performance measures yield different rankings among the thirteen methods and no method consistently dominates in this performance dimension in the training, out-of-sample, and out-of-time data. Lastly, predictive accuracy is a major challenge facing all mortgage early delinquency models, even in the training data.

Keywords: Credit scoring model; Mortgage early delinquency; Machine learning; Gradient boosting; Random forest; Neural network, Ensemble (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:290:y:2021:i:1:p:358-372

DOI: 10.1016/j.ejor.2020.07.058

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