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Using past contribution patterns to forecast fundraising outcomes in crowdfunding

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  • Fan-Osuala, Onochie
  • Zantedeschi, Daniel
  • Jank, Wolfgang

Abstract

The crowdfunding mechanism has proven to be a practical way of raising funds, especially with the widespread use of the Internet. However, one limitation of current crowdfunding platforms is that it is hard for creators and backers to anticipate the success of a campaign. This paper tackles this limitation. We take a two-pronged approach to building our forecasting model. First, we explore the nature and heterogeneity of contribution dynamics in crowdfunding campaigns and compare them across two natural groups (successful and unsuccessful campaigns). We then use insights generated from our exploratory analysis and draw upon the general laws of motion for stochastic processes in order to introduce a new dynamic model for predicting crowdfunding outcomes. Our model incorporates the history and dynamics of both the focal crowdfunding campaign and other campaigns for predicting outcomes. We compare our model to other parametric and semi-parametric benchmark models, and show substantial improvements.

Suggested Citation

  • Fan-Osuala, Onochie & Zantedeschi, Daniel & Jank, Wolfgang, 2018. "Using past contribution patterns to forecast fundraising outcomes in crowdfunding," International Journal of Forecasting, Elsevier, vol. 34(1), pages 30-44.
    • Handle: RePEc:eee:intfor:v:34:y:2018:i:1:p:30-44
    DOI: 10.1016/j.ijforecast.2017.07.003
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    Cited by:

    1. Matthew Ellman & Michele Fabi, 2022. "A Theory of Crowdfunding Dynamics," Working Papers 1349, Barcelona School of Economics.
    2. Bi, Gongbing & Geng, Botao & Liu, Lindong, 2019. "On the fixed and flexible funding mechanisms in reward-based crowdfunding," European Journal of Operational Research, Elsevier, vol. 279(1), pages 168-183.
    3. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    4. Massa Saluzzo, Federica & Alegre, Inés, 2021. "Supporting entrepreneurs: The role of third-party endorsement in crowdfunding platforms," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Wang, Wei & Guo, Lihuan & Wu, Yenchun Jim, 2022. "The merits of a sentiment analysis of antecedent comments for the prediction of online fundraising outcomes," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Santautė Venslavienė & Jelena Stankevičienė & Agnė Vaiciukevičiūtė, 2021. "Assessment of Successful Drivers of Crowdfunding Projects Based on Visual Analogue Scale Matrix for Criteria Weighting Method," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    7. Yanhong Guo & Shuai Jiang & Wenjun Zhou & Chunyu Luo & Hui Xiong, 2021. "A predictive indicator using lender composition for loan evaluation in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.

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