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

Year: 2017

Type: article

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.

Source: Journal of Small Business Management

DOI: 10.1111/jsbm.12318

Author: Simon Cornée

Institutions: Centre National de la Recherche Scientifique, Centre de Recherche en Économie et Management, Université Rennes 1, Université de Rennes

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W1531329487

FWCI: 21.27

Cites: 84

Cited by: 92

Related to: 10