Article ID Journal Published Year Pages File Type
5088353 Journal of Banking & Finance 2016 53 Pages PDF
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.
Related Topics
Social Sciences and Humanities Economics, Econometrics and Finance Economics and Econometrics
Authors
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