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- W4281778184 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. • We apply machine learning techniques to initial coin offerings (ICOs). • We use machine learning techniques to identify successful ICOs. • We find an increase in forecast accuracy with respect to standard model approaches. • An algorithm-based approach makes the analysis of whitepaper content available to all. • We show that the structure of ICO white papers forecasts success likelihood." @default.
- W4281778184 created "2022-06-13" @default.
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- W4281778184 date "2022-07-01" @default.
- W4281778184 modified "2023-10-03" @default.
- W4281778184 title "Machine-learning forecasting of successful ICOs" @default.
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- W4281778184 doi "https://doi.org/10.1016/j.jeconbus.2022.106071" @default.
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