market temporally increased the volume, triggering a secondary response from other traders. However, the true reason behind these volume anomalies remains an open question. Given the uncertainty and as this study aims to focus on the modeling of a fraudulent trader, we will not attempt to model LSEs as actions of some specific agents, but we will assume them in the simulation as prior knowledge (exogenous events). Inter-exchange influence and liquidity Before we start building the agent-based model of the market, it is important to discuss our assumption that influencing the price on two exchanges is sufficient to influence the market price across all other exchanges. The direct way in which one exchange can influence the price is by trading large volumes of Bitcoin. Most web services that report the price of Bitcoin calculate the price as an average over the last traded price on several exchanges, weighted by the traded volume. These services must have a way of detecting wash trading, but they can hardly filter out a fraudulent trade, such as the one described in previous sections. Therefore, if seemingly legal fraudulent trades of large volumes are executed on one exchange, then the reported price will be skewed by the activity of this exchange, diminishing the influence of the other exchanges. It is clear that if fraudulent buy orders are matched with sell orders with high limit prices, the calculated Bitcoin market price will consequently be pushed higher than the average price traded on other exchanges. A second way the activity on one exchange can influence the whole market is by traders observing price fluctuations on multiple exchanges and generating a profit by taking advantage of these small price differences. It was concluded in Chordia et al. ( 2008 ) that such an arbitrage activity, if stimulated by sufficient liquidity, results in higher price efficiency, which, in turn, results in a more stable market price unless new external information enters the In the literature, we find various studies that attempt to explain price as a direct consequence of manipulative behavior. A study (Gandal et al. 2018 ) analyzed suspicious market practices on the Mt.Cox exchange concludes that fraudulent actions influenced the price growth from $150 to $1000 in late 2013. More recently, Griffin and Shams ( 2019 ) argue that the Bitcoin market price might have been inflated by the issuance of Tether. As observed in a 2014 study (Robleh et al. Our findings might challenge the opinion that the main predictors of the Bitcoin bubble of late 2017 and the beginning of 2018 would be variables associated with the market sentiment (see Kapar and Olmo 2021 ). While we do not deny that market sentiment plays a major role, our results confront the thesis that the occurrence of this price bubble is spontaneous or a consequence of the widespread popularity of Bitcoin. In this sense, we contribute to the ongoing discussion among economists on the price formation of cryptocurrencies. Before arriving at the target exchanges, the flow passes through several addresses on the Tether blockchain. Once the Tether is exchanged for Bitcoin, Bitcoin flows back to Bitfinex. As analyzed in their study, these flows were highly correlated with the price increase. Additionally, Griffin and Shams ( 2019 ) identified the dominant addresses and concluded that the addresses were likely controlled by the same individual. We will use these insights to model the manipulator’s behavior by observing the change in the balance of the most relevant address. The strategy of price inflation mostly relies on the assumption that the market will respond with positive feedback (inflow of buy orders) as a consequence of the Bitcoin buy orders executed by the fraudulent trader. Once the positive trend of the market price is established and sustained, the trader”s cash buffer can be refilled if needed, which means that there will be enough cash for the EoM statements to be satisfied. In principle, the positive feedback assumption is unnecessary because a long position is built up even if the market reacts negatively. In both cases, such actions must be visible in the total Bitcoin trade volume and several large exchanges’ volumes. Data collection As the trade volumes of Poloniex and Bittrex were several times higher trade volumes than other large exchanges such as Coinbase or Bitflyer, we have decided not to use this data, as they probably experienced wash trading. Instead, we used traded volume data from exchanges that obtained a Bitlicense (Chohan 2018 ) issued by the New York State Department of Financial Services or had similarly reported volumes. For instance, for the reasons described in the section discussing volume anomalies we deem a plausible assumption, that it was sufficient for the fraudulent trader to influence 11 Individual governments can decide the strength of regulations in agreement to their long-term strategy and consider the consequences of their decisions concerning innovation. These decisions can be effectively implemented at the domestic level; however, there might be an incentive to avoid regulations in the case of exchanges, as they can pose the risk of a decrease in traded volume or engage in illicit behavior. In addition to the legislative regulations implemented in various countries, a different self-regulatory approach can be adopted. This study assumes that exchanges benefit from money laundering; reporting suspicious transactions can increase money laundering activity. One must be aware that a similar situation can occur when dealing with market manipulation. It can be argued that one of the main reasons for the widespread popularization of Bitcoin was the price increase orchestrated in 2017. Even though the exchanges likely knew about the issue, 12 as apparent both from the statistical evidence presented in Griffin and Shams ( 2019 ) and EoM events reconstruction by our model, the manipulation continued.