Title:
Essays on the Wisdom of the Crowd in Crowdfunding
Essays on the Wisdom of the Crowd in Crowdfunding
Author(s)
Yao, Jiayu
Advisor(s)
Lin, Mingfeng
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Abstract
The rapidly growing crowdfunding has redefined financial behaviors and revolutionized traditional industries such as banking. My dissertation studies crowd behaviors in online crowdfunding and the impact of crowdfunding on entrepreneurial development. In my first essay, I propose several easily scalable variables derived from the heterogeneity of investors’ bids in terms of size and timing. I 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. In my second essay, I study the impact of peer behavior information display on lenders’ decision-making in crowdfunding. Utilizing two online controlled experiments and a real-world dataset, I examine lenders’ platform abandonment, decision time, investment willingness, and risk preference under different display formats of peer information. The results call attention to the potential information overload that detailed peer information may cause to investors: they are more likely to abandon the platform and require a longer decision time when presented with prior investment transactions. The results also highlight the benefits and risks of aggregated peer information: Compared with completely no peer information or extensive peer information, a moderate amount of peer information saves lenders’ decision time, increases participation rate, and also amplifies the influence of peers. In my third essay, I study if features from crowdfunding projects can predict entrepreneurs’ mass market potential. I also examine if and how market reactions, especially their non-financial aspects, contribute to the prediction of mass market potential. I build classification and interpretable machine learning models to predict and explain entrepreneurs’ market success using project and crowd factors of entrepreneurs’ crowdfunding campaigns. The results suggest that crowd features, especially non-financial features, play an important role in predicting mass market launch and market evaluation. The analyses of non-financial features suggest that crowdfunding success does not always translate into mass market success. Taken together, the dissertation contributes to a better understanding of crowd intelligence in crowdfunding and its value.
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Date Issued
2023-07-31
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Text
Resource Subtype
Dissertation