Cited by 238Financial Distress and Bankruptcy PredictionFinTech, Crowdfunding, Digital FinanceComputational and Text Analysis Methods

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When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

Netzer, Oded, Lemaire, Alain, Herzenstein, Michal·2019·Journal of Marketing Research

DOI 10.1177/0022243719852959

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Abstract

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan above and beyond the financial and demographic variables commonly used in models predicting default. The authors use text-mining and machine learning tools to automatically process and analyze the raw text in over 120,000 loan requests from Prosper, an online crowdfunding platform. Including in the predictive model the textual information in the loan significantly helps predict loan default and can have substantial financial implications. The authors find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of God, the borrower’s financial and general hardship, pleading lenders for help, and short-term-focused words. The authors further observe that defaulting loan requests are written in a manner consistent with the writing styles of extroverts and liars.

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(2012) · Cited by 8,009
Econometric policy evaluation: A critique
Lucas, R. E. (1976) · Carnegie-Rochester Conference Series on Public Policy · doi:10.1016/s0167-2231(76)80003-6 · Cited by 6,110
Finding scientific topics
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
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Boys will be Boys: Gender, Overconfidence, and Common Stock Investment
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Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Unrealistic optimism about future life events
Weinstein, N. D. (1980) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.39.5.806 · Cited by 4,999
What Makes Online Content Viral?
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Private traits and attributes are predictable from digital records of human behavior
Kosinski, M., Stillwell, D., & Graepel, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218772110 · Cited by 2,829
The Psychology of Life Stories
McAdams, D. P. (2001) · Review of General Psychology · doi:10.1037/1089-2680.5.2.100 · Cited by 2,808 · cited 1×
“The premise that the text may be indicative of deeper traits is predicated on the idea that there is a systematic relationship between the words people use and their personality traits (Hirsh and Peterson 2009), identities (McAdams 2001), and emotional states (Tausczik and Pennebaker 2010).”
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Anderson Jon, Burks Stephen, DeYoung Colin, Rustichini Aldo (2011), “Toward the Integration of Personality Theory and Decision Theory in the Explanation of Economic Behavior,” presented at the IZA Workshop: Cognitive and Non-Cognitive Skills, Bonn, Germany (January 25–27), https://pdfs.semanticscholar.org/db58/6683a98173dba870d5e8fc93920fa867a1d2.pdf.
Bergstra James, 2012, Journal of Machine Learning Research, 13, 281
Brown Meta, 2015, Federal Reserve Bank
Gross Jacob P.K., 2009, Journal of Student Financial Aid, 39, 19
McLaughlin G. Harry, 1969, Journal of Reading, 12, 639
10.1353/jhr.2011.0025 · DOI
Ventrella Jeffrey, 2011, Virtual Body Language

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“…process and analyze loan requests to slice borrowers into good customers (those will pay back the loan) and bad customers (those will not) (Netzer et al. 2019), automated text analysis and correspondence analysis can be used for psychographic consumer segmentation in the art market (Pitt…”
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Authors: Netzer, O., Lemaire, A., & Herzenstein, M.
Affiliations: Columbia University (US)
University of Delaware (US)
Corresponding author: Netzer, Oded
ORCID: Netzer, Oded
Venue: Journal of Marketing Research
Publisher: SAGE Publications
Published: 2019-08-15
Volume: 56
Issue: 6
Pages: 960-980
ISSN: 0022-2437 (ISSN-L)
1547-7193
DOI: 10.1177/0022243719852959
OpenAlex: W3015337523
Cited by: 238
References: 76
Citations by year: 2026: 17 · 2025: 43 · 2024: 39 · 2023: 40 · 2022: 32 · 2021: 41 · 2020: 17 · 2019: 6
FWCI: 61.21
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Marketing Research (publishedVersion)
Topics: Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords: loan default prediction, extraversion, dishonesty, financial hardship, crowdfunding, PROSPER, text mining, writing style

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When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

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