Forecasting the Success Rate of Reward Based Crowdfunding Projects

Abstract

The present paper develops three models that help predict the success rate and attainable investment levels of online crowdfunding ventures. This is done by applying standard economic theory and machine learning techniques from computer science to the novel sector of online crowd-based micro-financing. In contrast with previous research in the area, this paper analyses transaction-level data in addition to information about completed crowdfunding projects. This provides a unique perspective in the ways crowd-finance ventures develop. The models reach an average of 83% accuracy in predicting the outcome of a crowdfunding campaign at any point throughout its duration. These findings prove that a number of product and project specific parameters are indicative of the success of the venture. Subsequently, the paper provides guidance to capital seekers and investors on the basis of these criteria, and allows participants in the crowdfunding marketplace to make more rational decisions.

Keywords

• microfinance
• entrepreneur finance
• crowdfunding