Cargando…

Identification of Scams in Initial Coin Offerings With Machine Learning

Following the emergence of cryptocurrencies, the field of digital assets experienced a sudden explosion of interest among institutional investors. However, regarding ICOs, there were a lot of scams involving the disappearance of firms after they had collected significant amounts of funds. We study h...

Descripción completa

Detalles Bibliográficos
Autores principales: Karimov, Bedil, Wójcik, Piotr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525659/
https://www.ncbi.nlm.nih.gov/pubmed/34676361
http://dx.doi.org/10.3389/frai.2021.718450
_version_ 1784585722530889728
author Karimov, Bedil
Wójcik, Piotr
author_facet Karimov, Bedil
Wójcik, Piotr
author_sort Karimov, Bedil
collection PubMed
description Following the emergence of cryptocurrencies, the field of digital assets experienced a sudden explosion of interest among institutional investors. However, regarding ICOs, there were a lot of scams involving the disappearance of firms after they had collected significant amounts of funds. We study how well one can predict if an offering will turn out to be a scam, doing so based on the characteristics known ex-ante. We therefore examine which of these characteristics are the most important predictors of a scam, and how they influence the probability of a scam. We use detailed data with 160 features from about 300 ICOs that took place before March 2018 and succeeded in raising most of their required capital. Various machine learning algorithms are applied together with novel XAI tools in order to identify the most important predictors of an offering’s failure and understand the shape of relationships. It turns out that based on the features known ex-ante, one can predict a scam with an accuracy of about 65–70%, and that nonlinear machine learning models perform better than traditional logistic regression and its regularized extensions.
format Online
Article
Text
id pubmed-8525659
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-85256592021-10-20 Identification of Scams in Initial Coin Offerings With Machine Learning Karimov, Bedil Wójcik, Piotr Front Artif Intell Artificial Intelligence Following the emergence of cryptocurrencies, the field of digital assets experienced a sudden explosion of interest among institutional investors. However, regarding ICOs, there were a lot of scams involving the disappearance of firms after they had collected significant amounts of funds. We study how well one can predict if an offering will turn out to be a scam, doing so based on the characteristics known ex-ante. We therefore examine which of these characteristics are the most important predictors of a scam, and how they influence the probability of a scam. We use detailed data with 160 features from about 300 ICOs that took place before March 2018 and succeeded in raising most of their required capital. Various machine learning algorithms are applied together with novel XAI tools in order to identify the most important predictors of an offering’s failure and understand the shape of relationships. It turns out that based on the features known ex-ante, one can predict a scam with an accuracy of about 65–70%, and that nonlinear machine learning models perform better than traditional logistic regression and its regularized extensions. Frontiers Media S.A. 2021-10-05 /pmc/articles/PMC8525659/ /pubmed/34676361 http://dx.doi.org/10.3389/frai.2021.718450 Text en Copyright © 2021 Karimov and Wójcik. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Karimov, Bedil
Wójcik, Piotr
Identification of Scams in Initial Coin Offerings With Machine Learning
title Identification of Scams in Initial Coin Offerings With Machine Learning
title_full Identification of Scams in Initial Coin Offerings With Machine Learning
title_fullStr Identification of Scams in Initial Coin Offerings With Machine Learning
title_full_unstemmed Identification of Scams in Initial Coin Offerings With Machine Learning
title_short Identification of Scams in Initial Coin Offerings With Machine Learning
title_sort identification of scams in initial coin offerings with machine learning
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525659/
https://www.ncbi.nlm.nih.gov/pubmed/34676361
http://dx.doi.org/10.3389/frai.2021.718450
work_keys_str_mv AT karimovbedil identificationofscamsininitialcoinofferingswithmachinelearning
AT wojcikpiotr identificationofscamsininitialcoinofferingswithmachinelearning