Unpacking the black box of ICO white papers: a topic modeling approach

Anna Pastwa, Prabal Shrestha, James Thewissen and Wouter Torsin
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James Thewissen: Université catholique de Louvain, LIDAM/LFIN, Belgium

No 2021018, LIDAM Discussion Papers LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

Abstract: We apply a novel topic modeling method to map Initial Coin Offerings’ (ICOs’) white paper thematic content to analyze its information value to investors. Using a sentence-based topic modeling algorithm, we determine and empirically quantify 30 topics in an extensive collection of 5,210 ICO white papers between 2015 and 2021. We find that the algorithm produces a semantically meaningful set of topics, which significantly improves the model performance in identifying successful projects. The most value-relevant topics concern the technical features of the ICO. However, we find that white paper’s informativeness substantially diminishes after the token is listed. Moreover, we show that credibility-enhancing mechanisms (i.e., regulations and ICO analysts) reinforce the information value of ICO white papers. Overall, our results suggest that the topics discussed in white papers and the attention devoted to each topic are useful ICO performance indicators.

Keywords: ICOs; White paper informativeness; Topic modeling; ICO regulation; ICO analysts (search for similar items in EconPapers)
JEL-codes: D80 G15 L26 M13 (search for similar items in EconPapers)
Pages: 58
Date: 2021-01-01
New Economics Papers: this item is included in nep-ban and nep-cmp
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Working Paper: Unpacking the black box of ICO white papers: a topic modeling approach (2022)
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