Abeer ElBahrawy

TL;DR: It is revealed that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years.

TL;DR: In this paper, the authors use machine learning and AI-assisted trading to predict the short-term evolution of the cryptocurrency market and show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks.

TL;DR: In this article, the authors use machine learning and AI-assisted trading to predict the short-term evolution of the cryptocurrency market and show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks.

TL;DR: In this paper, the authors consider the history of the entire market and analyse the behavior of 1,469 cryptocurrencies introduced between April 2013 and June 2017, revealing that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years.

TL;DR: In this paper, the authors investigate how the dark market ecosystem re-organises following the disappearance of a market, due to factors including raids and scams, and find that migrants are on average more active users in comparison to non-migrants and move preferentially towards the coexisting market with the highest trading volume.