The Relevance of Soft Information for Predicting Small Business Credit Default: Evidence from a Social Bank

Simon Cornee

Economics Working Paper Archive (University of Rennes & University of Caen) from Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS

Abstract: Using a unique, hand-collected database of 389 small loans granted by a French social bank dealing with genuinely small, informationally opaque businesses (mainly social enterprises), our study highlights the relevance of including soft information (especially on management quality) to improve credit default prediction. Comparing our findings with those of previous studies also reveals that the more opaque the borrower, the higher the predictive value of soft information in comparison with hard. Finally, a cost-benefit analysis shows that including soft information is economically valuable once collection costs have been accounted for, albeit to a moderate extent.

Keywords: Credit Default Prediction; Credit Rating; Relationship Lending; Social Banking (search for similar items in EconPapers)
JEL-codes: G21 M21 (search for similar items in EconPapers)
Date: 2012-06, Revised 2015-09
New Economics Papers: this item is included in nep-ban, nep-cba, nep-cfn, nep-cta, nep-for and nep-rmg
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Working Paper: The Relevance of Soft Information for Predicting Small Business Credit Default: Evidence from a Social Bank (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:tut:cremwp:201226

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