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Activity Number: 589
Type: Topic Contributed
Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #316571
Title: A Bayesian Statistical Model for Online Crowdfunding
Author(s): Jason Duan* and Zhouxin Li
Companies: The University of Texas at Austin and The University of Texas at Austin
Keywords: crowdfunding ; hazard model ; Bayesian inference ; counterfactual analysis ; network effect
Abstract:

Crowdfunding is a fast emerging internet fundraising mechanism for soliciting capital from the crowd to support entrepreneurial ventures. This paper empirically investigates the dynamics of investors' backing behaviors in the presence of network externalities and a finite time window. The proposed model captures how investors dynamically update their expectations on the prospect of a project based on its current funding status and time progress. Model estimation shows that investors are more likely to back a project that has already attracted a critical mass of funding (positive network externalities). For the same amount of achieved funding, the backing propensity declines over time (negative time effects). These two opposing forces give rise to a critical mass of funding the project must attain on time to achieve successful funding by the deadline. Counterfactual simulations show that projects may fail to attain the critical mass because of unfavorable shocks in investor visits at the early stage of the funding cycle. We derive dynamic seeding strategies for project owners to maximize the likelihood of funding success.


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