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Machine-learning forecasting of successful ICOs

Author

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  • Meoli, Michele
  • Vismara, Silvio

Abstract

The identification of ventures that are more likely to be successful is a complex task for equity investors, such that a mix of assessment criteria is typically employed. Machine learning (ML) techniques may provide valid support to investors in processing the set of available information. This paper applies ML to initial coin offerings (ICOs), which allows firms to raise blockchain finance through public online offerings. After implementing this novel approach to a sample of 383 ICOs launched between August 2014 and December 2019, we found an increase in forecast accuracy, from 54.3% when using standard Logit models to 72.8% when using ML techniques. Therefore, we contribute to the scientific debate and practice by introducing an algorithm-based approach that makes the analysis of whitepaper content available to individual investors. Importantly, we document that the structure of ICO white papers, a so far neglected dimension, is a significant determinant of the success of ICOs.

Suggested Citation

  • Meoli, Michele & Vismara, Silvio, 2022. "Machine-learning forecasting of successful ICOs," Journal of Economics and Business, Elsevier, vol. 121(C).
    • Handle: RePEc:eee:jebusi:v:121:y:2022:i:c:s0148619522000273
    DOI: 10.1016/j.jeconbus.2022.106071
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    References listed on IDEAS

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    Cited by:

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    2. Bongini, Paola & Osborne, Francesco & Pedrazzoli, Alessia & Rossolini, Monica, 2022. "A topic modelling analysis of white papers in security token offerings: Which topic matters for funding?," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Bitetto, Alessandro & Cerchiello, Paola, 2023. "Initial coin offerings and ESG: Allies or enemies?," Finance Research Letters, Elsevier, vol. 57(C).
    4. Carolina Camassa, 2023. "Legal NLP Meets MiCAR: Advancing the Analysis of Crypto White Papers," Papers 2310.10333, arXiv.org, revised Oct 2023.
    5. Douglas Cumming & Satish Kumar & Weng Marc Lim & Nitesh Pandey, 2023. "Mapping the venture capital and private equity research: a bibliometric review and future research agenda," Small Business Economics, Springer, vol. 61(1), pages 173-221, June.

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