Investor Learning in Crowdfunded Supply Chain Finance Markets

32 Pages Posted: 8 Mar 2018 Last revised: 29 Jan 2021

See all articles by Zhijin Zhou

Zhijin Zhou

University of Washington - Michael G. Foster School of Business

Shengsheng Xiao

Shanghai University of Finance and Economics

Yi-Chun (Chad) Ho

George Washington University - School of Business

Yong Tan

University of Washington - Michael G. Foster School of Business

Date Written: January 28, 2021

Abstract

Problem definition: Crowdfunded Supply Chain Finance (SCF) is an innovative Fintech service that transforms financial flows, allowing individual investors to serve as funders under the SCF paradigm. As required by crowdfunded SCF platforms, the unique presence of loan guarantors in the financing process alters how fundraisers and investors interact, which gives rise to investor learning. Academic/practical relevance: Understanding how such learning behavior impacts investors’ decision-making leads to actionable recommendations for platform managers who desire to encourage investor participation as well as for capital seekers who wish to stimulate fundraising performance. Methodology: We develop a Bayesian learning model, wherein we conceptualize individual perception of guarantor reliability as a subjective attitude underlying the perceived risk of a loan listing. Given that a guarantor may be involved in multiple loans in this unique market, we consider that individuals can learn about a guarantor’s true reliability and dynamically update their perception as they receive more repayments, or lack thereof, over time. We model investor behavior as two separate yet interdependent outcomes: (1) the incidence decision of whether to invest and (2) the amount decision of how much to invest. Results: Our estimation results confirm the existence of investor learning: an individual’s incidence decision and amount decision are both driven by her perception of guarantor reliability. In addition, we observe that this latent perception has different moderating effects on investor responses to listing attributes, such as interest rate and loan duration. Managerial implications: Our counterfactual simulations generate useful implications. For platform managers, enabling investor learning from correlated investment experience can help mitigate adverse selection and improve overall market efficiency. For supply chain members, optimizing the structure of loan listings could accelerate investor learning, which in turn can help simulate fundraising performance as a desirable outcome of reputation building.

Keywords: FinTech, Supply Chain Finance; Crowdfunding; Platform Policy; Learning

Suggested Citation

Zhou, Zhijin and Xiao, Shengsheng and Ho, Yi-Chun (Chad) and Tan, Yong, Investor Learning in Crowdfunded Supply Chain Finance Markets (January 28, 2021). Available at SSRN: https://ssrn.com/abstract=3136240 or http://dx.doi.org/10.2139/ssrn.3136240

Zhijin Zhou (Contact Author)

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

Shengsheng Xiao

Shanghai University of Finance and Economics ( email )

No. 100 Wudong Road
Shanghai, Shanghai 200433
China

Yi-Chun (Chad) Ho

George Washington University - School of Business ( email )

Washington, DC 20052
United States

HOME PAGE: http://business.gwu.edu/chad-ho

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

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