Profitability of cryptocurrency Pump and Dump schemes

One of the price manipulation schemes achieved by artificially increasing the trading volume of the target asset, Pump and Dump (P&D) schemes, has a long history in the stock market and is usually considered unlawful. In cryptocurrency markets, however, this scheme has not been well-regulated and uniquely orchestrated through a new type of social media platform such as Telegram. This paper aims to identify the features of P&D organized through Telegram and examine the market resilience to these activities. The regression model will be placed, in a Bayesian hierarchical framework, to clarify variables that contribute to the profitability (i.e., price change) of P&D attempts. It is revealed that the effect of trading volume on profitability significantly differs across each exchange market. Particularly, Yobit and Cryptopia are more sensitive (easily manipulated) to the increase in the trading volume than Binance and Bittrex, while controlling other significant factors, including the timing of the pump (hourly, yearly), the currency, and the Telegram channel. Furthermore, this paper builds a machine learning model to identify the price hike (successful schemes) given information before the pump starts and achieved more than 75% accuracy using tree-based ensemble models. The contribution of this paper is to provide a detailed analysis of P&D schemes using a novel statistical approach, while particularly focusing on the effect of each exchange, therefore provides a better understanding of how the market is manipulated by the crowd of people in social media platforms.