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Forecasting the Success Rate of Reward Based Crowdfunding Projects

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  • Elenchev, Ivelin
  • Vasilev, Aleksandar

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.

Suggested Citation

  • Elenchev, Ivelin & Vasilev, Aleksandar, 2017. "Forecasting the Success Rate of Reward Based Crowdfunding Projects," EconStor Preprints 170681, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esprep:170681
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    References listed on IDEAS

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    Cited by:

    1. Michael J. Ryoba & Shaojian Qu & Yongyi Zhou, 2021. "Feature subset selection for predicting the success of crowdfunding project campaigns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 671-684, September.

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    More about this item

    Keywords

    microfinance; entrepreneurial finance; crowdfunding;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General

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