Forecasting the Success Rate of Reward Based Crowdfunding Projects

Ivelin Elenchev and Aleksandar Vasilev

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

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 on-line 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 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 ndings prove that a number of product and project specifi c 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; entrepreneurial finance; crowdfunding (search for similar items in EconPapers)
JEL-codes: C0 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-big, nep-ent, nep-pay and nep-ppm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Forecasting the Success Rate of Reward Based Crowdfunding Projects (2019)
Working Paper: Forecasting the Success Rate of Reward Based Crowdfunding Projects (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:170681

Access Statistics for this paper

More papers in EconStor Preprints from ZBW - Leibniz Information Centre for Economics Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().