Numerological Heuristics and Credit Risk in Peer-to-Peer Lending

• 成果类型: Article
• 署名作者: Hu, Maggie Rong; Li, Xiaoyang; Shi, Yang; Zhang, Xiaoquan (Michael)
• 署名单位: Chinese University of Hong Kong; Hong Kong Polytechnic University; University of Melbourne; Tsinghua University; Chinese University of Hong Kong
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2023.1202
• 发表日期: 2023
• 页码: 1744-1760
• 关键词: superstitious beliefs; online; INFORMATION; Embeddedness; incentives; precision; patterns; JUDGMENT; BEHAVIOR; IMPACT

摘要

Heuristics are mental shortcuts that have ubiquitous influences on decision making. We investigate whether and how different heuristics have distinct effects in the context of peer-to-peer (P2P) lending. Drawing on theories on the roles that heuristics play in decision making, we conjecture that when borrowers use different heuristics based on distinct motives to set their loan amounts, their funding success and repayment performance also differ. Using detailed P2P lending data from a Chinese P2P lending platform, we examine two important numerological heuristics, the round-number heuristic and the lucky-number heuristic, which are observable in over 80% of the submitted loan amounts. We find that round-number loans are less likely to get funded and exhibit poor repayment performance after being funded, whereas lucky-number loans exhibit the opposite pattern. These findings, which we attribute to the different motives behind the borrowers' heuristic choices, challenge the conventional understanding that generally treats all heuristics as behavioral biases. Our results are robust to various identification strategies, including coarsened exact matching and instrumental variable estimation. Our paper sheds new light on the heterogeneity of heuristics and their distinctive implications for the credit market.