Predicting the Success Rate of Reward-Based Crowdfunding Campaigns: Evidence from Machine Learning

Prior research argues that the characteristics of crowdfunding campaigns affect their success rate. We examine this further to understand whether success in funding projects can be predicted by associated project characteristics. We apply machine learning to classify reward-based projects in the crowdft.m.ding market. Specifically, we construct three classification tree-based models and provide evidence that the proposed machine learning models have a strong out-of-sample predictive power over the probability of fundraising success. In addition, the robustness check through both logistic regression and propensity score matching approaches confirms that project characteristics, except for those linked to venture capital, are among the factors behind crowdfunding campaign success. This study can assist entrepreneurs in understanding the impact of project characteristics on the crowdfunding success rate. © 2023, Tamkang University. All rights reserved.