Multiperiod Corporate Default Prediction Through Neural Parametric Family Learning

Luo, Wei-Lun; Liu, Yuming; Yang, Jheng-Hong; Duan, Jin‐Chuan; Wang, Chuan‐Ju

doi:10.1137/1.9781611977172.36

Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) 2022

DOI: 10.1137/1.9781611977172.36

|View full text |Cite

Multiperiod Corporate Default Prediction Through Neural Parametric Family Learning

Wei-Lun Luo¹,

Yuming Liu²,

Jheng-Hong Yang³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multiperiod Bankruptcy Prediction Models with Interpretable Single Models

Beade,

Rodríguez,

Santos

2023

Comput Econ

View full text Add to dashboard Cite

This study considers multiperiod bankruptcy prediction models, an aspect scarcely considered in research despite its importance, since creditors must assess the risk of loans over the entire life of the debt and not at a specific point in the future. Two possibilities for the implementation of multiperiod prediction models are considered: Multi-Model multiperiod Bankruptcy Prediction Models (MMBPM) and Single-Model multiperiod Bankruptcy Prediction Models (SMBPM). The former considers the conditional probabilities obtained by individual models predicting bankruptcy at specific times in the future, while the latter is a single model predicting bankruptcy at a specific time interval in the future. The results show that there are no significant differences between the two approaches when compared using data after the learning period. However, SMBPMs have the important advantage of interpretability for decision-making, which is discussed with examples. Moreover, a comparison of SMBPM performance with external references is performed.

show abstract

Multiperiod Bankruptcy Prediction Models with Interpretable Single Models

Beade,

Rodríguez,

Santos

2023

Comput Econ

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Multiperiod Corporate Default Prediction Through Neural Parametric Family Learning

Cited by 1 publication

References 0 publications

Multiperiod Bankruptcy Prediction Models with Interpretable Single Models

Multiperiod Bankruptcy Prediction Models with Interpretable Single Models

Contact Info

Product

Resources

About