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Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms

Gregor Dorfleitner, Christopher Priberny, Stephanie Schuster, Johannes Stoiber, Martina Weber, Ivan de Castro and Julia Kammler

Journal of Banking & Finance, 2016, vol. 64, issue C, 169-187

Abstract: We examine the relation of soft factors that are derived from the description texts to the probability of successful funding and to the default probability in peer-to-peer lending for two leading European platforms. We find that spelling errors, text length and the mentioning of positive emotion evoking keywords predict the funding probability on the less restrictive of both platforms, which even accepts applications without credit scores. This platform also shows a better risk-return profile. Conditional on being funded, text-related factors hardly predict default probabilities in peer-to-peer lending for both platforms.

Keywords: Peer-to-peer lending; Soft information; Funding probability; Probability of default (search for similar items in EconPapers)
JEL-codes: G20 G32 (search for similar items in EconPapers)
Date: 2016
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