Soft Information and Small Business Lending

Yehning Chen, Rachel Huang, John Tsai and Larry Tzeng

Journal of Financial Services Research, 2015, vol. 47, issue 1, 115-133

Abstract: Using data from a Taiwanese finance company, this paper empirically investigates the value of soft information, information that requires the subjective interpretation by the loan officers who collect it and cannot be credibly transmitted to others, for making small business loans. It finds that the use of soft information significantly improves the power of default prediction models. It also identifies the types of soft information that are helpful for predicting loan defaults. In addition, it shows that borrowers with more favorable soft information enjoy lower interest rates. These results imply that soft information is important for small business lending. Copyright Springer Science+Business Media New York 2015

Keywords: Soft information; Small business lending; Default prediction; Credit scoring; G21; G33 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10693-013-0187-x (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jfsres:v:47:y:2015:i:1:p:115-133

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10693

DOI: 10.1007/s10693-013-0187-x

Access Statistics for this article

Journal of Financial Services Research is currently edited by Haluk Unal

More articles in Journal of Financial Services Research from Springer, Western Finance Association Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().