Document Type

Thesis or dissertation

Date of this Version

2016

Advisor

Pinar Yildirim

Abstract

Equity crowdfunding is an increasingly popular means of raising capital for early stage startups. It enables entrepreneurs to finance their companies with smaller contributions from a variety of people. This paper studies the relationship between the characteristics of a given company and its ability to raise funds on an equity crowdfunding platform. A series of statistical and machine learning models are fit to data from a U.S.-based equity crowdfunding website, including a logistic regression, a CART decision tree, a naïve Bayes classifier, and a support vector machine. This study demonstrates that a connection exists between the probability of a company’s crowdfunding success and its previous funding history, Twitter presence, media buzz, size, location, and its founders’ educational backgrounds. As a whole, however, the classification quality of the various models leaves something to be desired. This suggests the need for additional data inputs and more longitudinal research in the field of equity crowdfunding.

Keywords

venture capital, equity crowdfunding, machine learning

Included in

Business Commons

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Date Posted: 10 August 2016

 

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