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Forecasting the Success Rate of Reward Based Crowdfunding Projects

Ivelin Elenchev and Aleksandar Vasilev

Bulgarian Economic Papers (www.bep.bg) from St Kliment Ohridski University of Sofia, Faculty of Economics and Business Administration / Center for Economic Theories and Policies

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 analyzes transaction-level data in addition to information about completed crowdfunding projects. This provides an unique perspective in the ways crowdfinance 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 fundings 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 (search for similar items in EconPapers)
JEL-codes: M20 G24 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-ent, nep-pay and nep-ppm
Date: 2017-11, Revised 2017-11
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https://www.uni-sofia.bg/index.php/eng/content/dow ... file/BEP-2017-09.pdf First version, 2017 (application/pdf)

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Working Paper: Forecasting the Success Rate of Reward Based Crowdfunding Projects (2017) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:sko:wpaper:bep-2017-09

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