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Words Matter: The Role of Readability, Tone, and Deception Cues in Online Credit Markets

Published online by Cambridge University Press:  09 September 2022

Qiang Gao ,
Mingfeng Lin  and
Richard Sias Open the ORCID record for Richard Sias [Opens in a new window]
Qiang Gao
Affiliation:
City University of New York Zicklin School of Business [email protected]
Mingfeng Lin
Affiliation:
Georgia Institute of Technology Scheller College of Business [email protected]
Richard Sias*
Affiliation:
University of Arizona Eller College of Management
*
[email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

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Using debt crowdfunding data, we investigate whether borrowers’ writing style is associated with an online lender and borrower behaviors, whether the information contained in linguistic style can mitigate information asymmetry in peer-to-peer markets, and whether online investors correctly interpret the economic value of written texts. Peer-to-peer lenders bid more aggressively, are more likely to fund, and charge lower rates to online borrowers whose writing is more readable, more positive, and contains fewer deception cues. Moreover, such borrowers are less likely to default. Online investors, however, fail to fully account for the information contained in borrowers’ writing.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 58 , Issue 1 , February 2023 , pp. 1 - 28
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

This article was previously circulated under the titles “Linguistic Features and Peer-to-Peer Loan Quality: A Machine Learning Approach” and “Economic Value of Texts in Online Debt Crowdfunding.” We thank an anonymous referee, Hendrik Bessembinder (the editor), and seminar participants at Carnegie Mellon University, Georgia Institute of Technology, Michigan State University, National Taiwan University, Shanghai University of Finance and Economics, Tsinghua University, University of Arizona, University of California San Diego, University of Rochester; as well as conference participants at the 2014 Berkeley Crowdfunding Symposium, the Board of Governors of the Federal Reserve System’s 2016 Financial Innovations Conference, the 2014 Winter Conference in Business Intelligences, INFORMS 2014 Annual Meeting, INFORMS 2014 Conference on Information Systems and Technology, and the 2017 Marketplace Innovation Conference at Stanford University, for their valuable comments and suggestions.

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