.png){kind=icon}
返回
Forecasting nonperforming loans using machine learning
.png){kind=icon}
PRE
AI
DOI：10.1002/for.2977.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nonperforming loans play a critical role in financial institutions' overall performance and can be controlled by forecasting the probable nonperforming loans. This paper employs a series of machine learning techniques to forecast bank nonperforming loans on emerging countries' financial institutions. Using quarterly cross-sectional data of 322 banks from 15 emerging countries, this study finds that advanced machine learning-based models outperform simple linear techniques in forecasting bank nonperforming loans. Among all 14 linear and nonlinear models, the random forest model outperforms other models. It achieves a 76.10% accuracy in forecasting nonperforming loans. The result is robust in different performance metrics. The variable importance analysis reveals that bank diversification is the most critical determinant for future nonperforming loans of a bank. Additionally, this study revealed that macroeconomic factors are less prominent in predicting nonperforming loans compared with bank-specific factors.
Keyword:
bagged CART
banking
forecasting
machine learning
nonperforming loans (NPLs)
期刊
IF:
2.7
论文数:
2.3K
被引数:
3.0K
机构
引用论文
Decreased limits of stability in response to postural perturbations in subjects with low back pain
PREAI
分享收藏Machine learning approach to drivers of bank lending: evidence from an emerging economy银行贷款驱动因素的机器学习方法: 来自新兴经济体的证据.png)
FINANCIAL INNOVATION
OAAI
FINANCIAL INNOVATIONIF7.2
OAAI分享收藏The role of investor sentiment in forecasting housing returns in China: A machine learning approachOAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容