Improving Funding Operations of Equity-based Crowdfunding Platforms

• 成果类型: Article
• 署名作者: Aggarwal, Rohit; Lee, Michael J.; Osting, Braxton; Singh, Harpreet
• 署名单位: Utah System of Higher Education; University of Utah; Nevada System of Higher Education (NSHE); University of Nevada Las Vegas; Utah System of Higher Education; University of Utah; University of Texas System; University of Texas Dallas
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13505
• 发表日期: 2021
• 页码: 4121-4139
• 关键词: Crowdfunding; paired comparisons; startup valuation

摘要

Equity-based crowdfunding platforms enable investors to come together to invest in startups and help lay-investors to follow the lead of investors with good startup evaluation skills. Crowdfunding platforms often gather users' inputs to evaluate investors and startups, but such inputs are quite noisy and often rely on past performance. Many investors with good evaluation skills do not have substantial past investment experience but still can lead investment rounds. This helps provide investment opportunities to lay-investors who otherwise do not get to join investors with proven records. Without identifying such investors with potential, platforms lose the opportunity to put together investors to fund worthy startups and lose business. We develop a Bayesian model to address this problem and improve funding operations of equity-based crowdfunding platforms. Specifically, the model helps platforms to better assess investors' evaluation skills, identify lead investors for lay-investors to follow, and increase funding opportunities on the platforms. To test the effectiveness of the proposed model, we gathered data from 319 actual investors listed on one of the largest crowdfunding platforms in the United States, picked startups randomly for investors to evaluate, and had investors evaluate startups in two ways-our approach and the conventional approach. We also discuss an extension of this Bayesian model that penalizes investors in case investors perform well by randomness. Furthermore, we used a Bayesian framework to help platforms better predict startup valuations accounting for investors' evaluation skills.