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

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  • Simon Cornée

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.

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  • Simon Cornée, 2015. "The Relevance of Soft Information for Predicting Small Business Credit Default: Evidence from a Social Bank," Working Papers CEB 15-044, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:sol:wpaper:2013/219172
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    More about this item

    Keywords

    Credit Default Prediction; Credit Rating; Relationship Lending; Social Banking;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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