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Research Letter

Extracting narrative data via large language models for loan default prediction: when talk isn’t cheap

Yufei XiaBusiness School, Jiangsu Normal University, Xuzhou, Jiangsu, ChinaCorrespondence[email protected]
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,
Zhengxu ShiBusiness School, Jiangsu Normal University, Xuzhou, Jiangsu, ChinaView further author information
,
Xiaoying DuBusiness School, Jiangsu Normal University, Xuzhou, Jiangsu, ChinaView further author information
&
Qiong ZhengBusiness School, Jiangsu Normal University, Xuzhou, Jiangsu, ChinaView further author information
Published online: 03 Nov 2023

References

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  • Herzenstein, M., S. Sonenshein, and U. M. Dholakia. 2011. “Tell Me a Good Story and I May Lend You Money: The Role of Narratives in Peer-To-Peer Lending Decisions.” Journal of Marketing Research 48 (SPL): S138–S149. https://doi.org/10.1509/jmkr.48.SPL.S138.
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  • Lun, X., X. Meng, and J. Xu. 2023. “Is Cheap Talk Just Empty Words? The Signalling Value of Voluntary Promises for Loan Repayment.” Applied Economics Letters 1–5. https://doi.org/10.1080/13504851.2023.2206608.
  • Netzer, O., A. Lemaire, and M. Herzenstein. 2019. “When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications.” Journal of Marketing Research 56 (6): 960–980. https://doi.org/10.1177/0022243719852959.
  • Xia, Y., X. Guo, Y. Li, L. He, and X. Chen. 2022. “Deep Learning Meets Decision Trees: An Application of a Heterogeneous Deep Forest Approach in Credit Scoring for Online Consumer Lending.” Journal of Forecasting 41 (8): 1669–1690. https://doi.org/10.1002/for.2891.
  • Xia, Y., L. He, Y. Li, N. Liu, and Y. Ding. 2020. “Predicting Loan Default in Peer‐To‐Peer Lending Using Narrative Data.” Journal of Forecasting 39 (2): 260–280. https://doi.org/10.1002/for.2625.

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