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.
Table of Contents
1 Introduction
2 Background information
2.1 The cryptocurrency ecosystem
2.2 Regulation of relevant crypto exchanges
3 Data
4 Empirical Evidence
4.1 Benford's Law
4.1.1 General
4.1.2 Pearson’s Chi-Squared Test for Benford's Distribution
4.1.3 Statistical Results
4.2 Clustering at key psychological numbers
4.3 Volume Spike Analysis
4.4 Discussion of statistical results
5 Incentives, Perpetrators and Impact
6 Measures to reduce washtrading
7 Conclusion
8 Outlook on future research
9 Bibliography
Objectives and Research Scope
This thesis aims to investigate the prevalence and nature of washtrading on regulated cryptocurrency exchanges by applying statistical analysis to historical transaction data from the first quarter of 2020. The primary research goal is to determine if highly regulated platforms exhibit signs of manipulative volume, while simultaneously identifying the key incentives and perpetrators driving these fraudulent activities within the ecosystem.
- Application of Benford’s Law to detect anomalies in transaction digit distributions.
- Analysis of trade size clustering at psychological round numbers as an indicator of authentic versus bot-driven volume.
- Examination of cross-exchange volume correlations to identify potential market manipulation footprints.
- Evaluation of regulatory frameworks and their effectiveness in mitigating washtrading incentives.
Excerpt from the Book
4.1 Benford's Law
The original discovery of this phenomenon was actually described by the Canadian astronomer Simon Newcomb back in 1881. He noticed that in logarithm tables the earlier pages starting with the number 1 were significantly more worn than the later pages (Newcomb (1881)). Frank Benford replicated these findings in 1938 on various datasets, including surface areas of rivers, the sizes of US populations, physical constants, molecular weights, entries from mathematical handbooks, numbers contained in an issue of Reader's Digest, the street addresses of persons listed in American Men of Science and death rates (Benford (1938)). He was later credited for the discovery, probably also due to Newcomb not providing any theoretical explanation for the phenomena as well as his article going mostly unnoticed at the time. It has subsequently been confirmed, that the logarithmic Distribution of first significant natural digits of many large financial datasets like these (historical trade prints), is described by Benford's Law with minor deviations taken into account. Hill (1995) provided a mathematical approach, selecting random distributions out of these random samples and proving the results are in accordance with Benford’s Law.
Summary of Chapters
1 Introduction: Provides an overview of the rise of cryptocurrency markets and defines the practice of washtrading as a significant issue for market integrity.
2 Background information: Describes the evolution of the cryptocurrency ecosystem and examines the regulatory status of selected exchanges.
3 Data: Explains the collection, processing, and robustness of the historical transaction datasets used for the empirical study.
4 Empirical Evidence: Presents the statistical findings based on Benford's Law, trade size clustering, and volume spike analysis across the chosen exchanges.
5 Incentives, Perpetrators and Impact: Identifies the primary drivers behind washtrading and discusses how these practices negatively influence market transparency and investor trust.
6 Measures to reduce washtrading: Proposes regulatory and technical strategies to mitigate washtrading activities and improve market oversight.
7 Conclusion: Summarizes the study's findings, highlighting that while some anomalies exist, the overwhelming volume on regulated exchanges appears to be authentic.
8 Outlook on future research: Suggests avenues for future analysis using more granular data, such as trader IDs, to achieve higher precision in quantifying manipulation.
9 Bibliography: Lists the academic and industry sources utilized for this thesis.
Keywords
Washtrading, Cryptocurrency Exchanges, Benford's Law, Market Manipulation, Regulatory Frameworks, Trade Size Clustering, Financial Fraud, BitLicense, Volume Analysis, Statistical Significance, Decentralized Finance, Market Integrity, Algorithmic Trading, Retail Investors, Transaction Data
Frequently Asked Questions
What is the core focus of this thesis?
The thesis investigates the presence and extent of washtrading activities on regulated cryptocurrency exchanges by analyzing transaction data from the first quarter of 2020.
What are the primary thematic fields covered?
The work covers statistical market analysis, crypto-asset regulation, the economics of exchange-driven volume inflation, and fraud detection methods.
What is the central research question?
The research asks to what extent regulated exchanges engage in or facilitate washtrading and whether the observed trading patterns deviate significantly from expected authentic human behavior.
Which scientific methods are employed?
The author uses Benford’s Law with Pearson’s Chi-squared tests, trade size clustering analysis at psychological round numbers, and cross-exchange correlation analysis of volume spikes.
What is discussed in the main body of the work?
The main body details the methodology, presents statistical evidence of market activity on exchanges like Kraken, Gemini, Bitstamp, and Zaif, and explores the incentives of actors involved in manipulation.
What are the key terms that define this work?
Key terms include washtrading, regulated crypto exchanges, Benford's Law, market transparency, and volume manipulation.
Why are regulated exchanges specifically chosen for this study?
Regulated exchanges were chosen to test if even platforms with high compliance standards exhibit manipulative footprints, as opposed to unregulated exchanges where such fraud is more widely accepted as common.
How does the author explain the deviation from Benford’s Law?
The author suggests that while deviations exist, they likely represent a combination of minor washtrading by individual users and legitimate market variance, rather than large-scale exchange-led fraud.
What is the significance of "trading games" mentioned in the research?
Trading games are identified as indirect incentives that can encourage users to perform washtrades or wash-sales to reach volume-based reward tiers, thereby inflating apparent liquidity.
What conclusion does the author reach regarding the overall market?
The author concludes that while washtrading occurs, the overwhelming majority of volume on the studied regulated exchanges is authentic, and stricter regulatory frameworks significantly mitigate the incentives for large-scale fraudulent manipulation.
- Quote paper
- Philipp Zeyer (Author), 2021, Crypto Washtrading. Empirical Evidence and Measures to Reduce Washtrading, Munich, GRIN Verlag, https://www.grin.com/document/1159543
