Crypto Washtrading. Empirical Evidence and Measures to Reduce Washtrading

Bachelor Thesis from the year 2021 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,3, University of Frankfurt (Main) (House of Finance), language: English, abstract: This thesis makes use of statistical methods such as the Pearson's Chi-squared analysis to find significant deviations in the first significant digit distributions in the historical transaction record of selected regulated crypto exchanges compared to Benford's Law. The analysis of trade size clustering behavior at key round numbers is used in order to detect possible signs of washtrading, followed by the volume spike analysis, where the correlation between the four exchanges in terms of rise and fall of their volume is carefully observed. Aloosh and Li (2019) and Lin et al. (2021) suggest a divergence between regulated and unregulated exchanges in regards to the washtrading activity, in the sense that most regulated exchanges seemed to confirm most statistical analysis while many unregulated crypto exchanges have shown signs of significant violations. Opposed to these findings, the focus will lie on regulated crypto exchanges only, for which partly abnormal patterns are in fact found, at least regarding the first significant digit distribution. Furthermore, the various regulatory frameworks for the selected exchanges are illustrated, consisting of Gemini, Bitstamp, Kraken and Zaif. The centre of attention will then shift to showing off possible incentives for the various parties to engage in washtrading in the first place. The thesis lays out how these activities distort exchange ratings and the connected metrics as well as aid in creating illegal schemes such as pump and dumps.