Severe Weather and Peer-to-Peer Farmers’ Loan Default Predictions: Evidence from Machine Learning Analysis

Wei Gao; Ming Ju; Tongyang Yang

Severe Weather and Peer-to-Peer Farmers’ Loan Default Predictions: Evidence from Machine Learning Analysis

Wei Gao
, Ming Ju
, Tongyang Yang

Economics and Finance

Widener University - Main Campus

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Finance Research Letters
Volume	58
State	Published - Dec 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger
SDG 8 Decent Work and Economic Growth

Disciplines

Business

Cite this

@article{96403e0c5c764ace992142e1cb18a7ef,

title = "Severe Weather and Peer-to-Peer Farmers{\textquoteright} Loan Default Predictions: Evidence from Machine Learning Analysis",

author = "Wei Gao and Ming Ju and Tongyang Yang",

note = "In theory, climate change affects farmers' loan default risk because severe weather conditions caused by climate change negatively affect farmlands' p...",

year = "2023",

month = dec,

language = "American English",

volume = "58",

journal = "Finance Research Letters",

}

TY - JOUR

T1 - Severe Weather and Peer-to-Peer Farmers’ Loan Default Predictions: Evidence from Machine Learning Analysis

AU - Gao, Wei

AU - Ju, Ming

AU - Yang, Tongyang

N1 - In theory, climate change affects farmers' loan default risk because severe weather conditions caused by climate change negatively affect farmlands' p...

PY - 2023/12

Y1 - 2023/12

UR - https://www.sciencedirect.com/science/article/pii/S1544612323006591

M3 - Article

VL - 58

JO - Finance Research Letters

JF - Finance Research Letters

ER -

Severe Weather and Peer-to-Peer Farmers’ Loan Default Predictions: Evidence from Machine Learning Analysis

UN SDGs

Disciplines

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