Revealed Wisdom of the Crowd: Bids Predict Loan Quality

48 Pages Posted: 4 May 2021 Last revised: 24 Aug 2022

See all articles by Jiayu Yao

Jiayu Yao

Scheller College of Business, Georgia Institute of Technology

Mingfeng Lin

Scheller College of Business, Georgia Institute of Technology

D. J. Wu

Georgia Institute of Technology - Ernest Scheller Jr. College of Business

Date Written: August 2022

Abstract

Despite the popularity of the phrase “wisdom of the crowd,” not all crowds are wise because not everyone in them acts in an informed, rational manner. Identifying informative actions, therefore, can help to isolate the truly “wise” part of a crowd. Motivated by this idea, we evaluate the informational value of investors’ bids using data from online, debt-based crowdfunding, where we were able to track both investment decisions and ultimate repayment statuses for individual loans. We propose several easily scalable variables derived from the heterogeneity of investors’ bids in terms of size and timing. We first show that loans funded with larger bids relative to the typical bid amount in the market, or to the bidder’s historical baseline, particularly early in the bidding period, are less likely to default. More importantly, these variables improve the predictive performance of state-of-the-art models that have been proposed in this context. Even during the fundraising process, these variables improve predictions of both funding likelihood and loan quality. We discuss the implications of these variables, including loan pricing in secondary markets, crowd wisdom in different market mechanisms, and financial inclusion. Crowdfunding platforms can easily implement these variables to improve market efficiency without compromising investor privacy.

Keywords: wisdom of the crowd, platform, crowdfunding, fintech

Suggested Citation

Yao, Jiayu and Lin, Mingfeng and Wu, D. J., Revealed Wisdom of the Crowd: Bids Predict Loan Quality (August 2022). Georgia Tech Scheller College of Business Research Paper No. 3837049, Available at SSRN: https://ssrn.com/abstract=3837049 or http://dx.doi.org/10.2139/ssrn.3837049

Jiayu Yao

Scheller College of Business, Georgia Institute of Technology ( email )

United States

Mingfeng Lin (Contact Author)

Scheller College of Business, Georgia Institute of Technology ( email )

United States

D. J. Wu

Georgia Institute of Technology - Ernest Scheller Jr. College of Business ( email )

800 West Peachtree Street, NW
Atlanta, GA 30308
United States
404-894-4364 (Phone)
404-894-6030 (Fax)

HOME PAGE: http://scheller.gatech.edu/wu

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
211
Abstract Views
972
rank
210,771
PlumX Metrics