Abstract
AbstractThe act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. It finds that a simple logistic regression model can easily be extended to a multiple logistic regression model by integrating more than one prediction variable, which indicates increasing difficulty in obtaining multiple observations with an increasing number of independent variables. It compares the efficiency of the logistic regression with that of a linear regression in predicting whether recovery is due in a credit operation, and, thus, identifies the best model for this purpose.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
