Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258538 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 9 [Article No.:] 434 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-15
Publisher: 
MDPI, Basel
Abstract: 
The purpose of this study is to address the critical issue of optimal credit allocation. Predicting a borrower's probability of default is a key requirement of any credit allocation system but turning it into labeled classes leads to problems in performance measurement. In this paper the connection between the probability of default and optimal credit allocation is established through a conceptual construct called the Kelly criterion. Conflicting performance measures in dichotomous classification are replaced with coherent criteria for judging the performance of credit allocation decisions. Extensive testing on peer-to-peer lending data shows that the Kelly strategy enables consistent outperformance and efficiency in processing information relative to alternative credit allocation approaches.
Subjects: 
credit allocation
Kelly criterion
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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