Words Matter: The Role of Texts in Online Credit Markets
Journal of Financial and Quantitative Analysis, forthcoming
58 Pages Posted: 6 Jun 2014 Last revised: 14 Aug 2021
Date Written: September 24, 2018
We use debt crowdfunding data to examine how borrowers’ writing style is associated with lender and borrower behavior. Controlling for credit and auction characteristics, lenders bid more aggressively, are more likely to fund, and charge lower rates to borrowers whose writing is more readable, more positive, and contains fewer deception cues. Consistent with information garnered from writing driving the relation, controlling for credit and auction characteristics, borrowers whose writing is more readable, more positive, and has fewer deception cues are less likely to default. Investors, however, fail to fully account for the information contained in borrowers’ writing—especially deception cues.
Keywords: texts, peer-to-peer lending, crowdfunding, predictive analysis, sentiment analysis, subjectivity analysis, readability analysis, deception detection, machine learning
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