The general acceptance of cryptocurrencies, especially Bitcoin, was a blessing from Wall Street, which institutionalized them as ETFs for comprehensive access by the general public and institutional investors. There is little to no denying now that this new asset class is becoming more traditional, often used as part of a diversified portfolio, and not taken as an alternative investment for Internet geeks anymore. Debates of its hedge properties and difficulties with traditional valuation methods persist, and volatility even of flagship BTC and ETH is not for faint-headed investors who buy at the top (all-time-highs) and may not see appreciation of invested sum until the next cycle boom.
What is still present is natural distrust for decentralized exchanges, even after years of FTX and MtGox fiascos and scandals, which are often accused of market manipulation in the form, for example, of generating fake trading volume in traditional finance, almost equaling the definition of “wash trading.” Today’s paper written by Jan Sila, Evžen Kočenda, Ladislav Kristoufek, and Jiri Kukacka presents the possibilities of fraud detection for these shady practices. Regulatory interventions to foster a transparent and reliable financial ecosystem for digital assets are yet to be fully clarified, and it would help gain the trust of even more mainstream investors and get rid of the stigma of fraud about cryptos that is still present.
Authors of the paper suggest to use wash trading volume metric that effectively captures immediate manipulative practices, highlighting significant trading distortions. Deviations from Benford’s Law reveal sustained periods of market manipulation detectable through statistical anomalies in trade data. Increased market volatility is a significant driver of wash trading, with higher volatility leading to more pronounced wash volumes in Bitcoin, Ethereum, and Litecoin. Public interest and information dissemination adversely impact wash trading in assets other than Bitcoin, indicating Bitcoin’s dominant influence in crypto markets.
Due to their significant market presence, Bitcoin and Ethereum exhibit consistently high levels of wash trading, particularly during periods of rapid price appreciation. Exchanges and traders may trade wash to inflate perceived liquidity and drive market optimism in bullish phases. XRP also shows elevated wash trading levels, particularly during legal disputes with the SEC, suggesting efforts to counteract negative sentiment and create an illusion of liquidity. On the other hand, Litecoin demonstrates lower levels of wash trading, reflecting its steadier market environment and reduced speculative interest.
Authors: Jan Sila, Evžen Kočenda, Ladislav Kristoufek, and Jiri Kukacka
Title: Determinants of wash trading in major cryptoexchanges
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4971590
Abstract:
This article investigates wash trading as a crypto-market-wide phenomenon that affects exchange integrity and the accuracy of liquidity claims. We examine four main cryptocurrencies using a dataset spanning November 15, 2020, to January 31, 2022. We employ two detection approaches to assess the extent of wash trading: the roundness of trade sizes and Benford’s Law. We examine over 40 different explanatory variables, including blockchain and crypto measures and financial and macroeconomic factors. Variable selection is conducted using a robust combination of Variance Inflation Factor and Bayesian Model Averaging. Our findings show that market volatility, exchange flows, and public attention all have a major influence on wash trading, as exchanges may use volatile conditions to engage in manipulative behaviors. Models in our study offer insights helpful for regulators and market participants to detect and mitigate such practices, thereby enhancing market integrity and investor confidence.
As always we present several interesting figures and tables:
Notable quotations from the academic research paper:
“Wash trading occurs when a single entity or a coordinated group buys and sells a financial asset either simultaneously or within a short time frame without facing any actual market risk, with the intent of artificially inflating trading volume and creating a misleading impression of the market activity or asset’s liquidity.1 While illegal on conventionally regulated asset exchanges,2 it has been widely documented on cryptocurrency exchanges, especially on smaller or newly established platforms seeking to expand their user base and operating with minimal regulatory oversight (Cong et al., 2023; Amiram et al., 2024). In- deed, wash trading has become a pervasive issue in cryptocurrency markets. From the leaked Bitcoin individual transaction data from the Mt. Gox exchange era, Aloosh and Li (2024) trace the origins of crypto wash trading back to June 26, 2011, and offer direct evidence indicating that while fabricated transactions represented only over 2% of trades, they may have accounted for as much as 60% of the daily volume in the period between June 2011 to May 2013. Next, Pennec et al. (2021) find that for a group of suspicious centralized exchanges, 96-98% of reported trading volume is highly questionable.
The main contributions of this paper are as follows: existing literature has primarily focused on developing methodologies to detect wash trading using qualitative approaches or indirectly quantifying its occurrence on individual exchanges, largely to guide and support regulatory efforts (Fusaro and Hougan, 2019) in the cryptocurrency sector. In contrast, we take a broader economic approach and aim to position wash trading in the global economic context. This study examines wash trading as a market-wide phenomenon on centralized exchanges and explores its dynamics empirically, utilizing a large, daily-frequency dataset of publicly available variables from both the cryptocurrency segment and the world economy as a whole. Furthermore, it assesses the predictive potential of the variables selected by the Bayesian Model Averaging (BMA) methodology to guide market participants and regulators in identifying future periods with a high likelihood of wash trading, as identified as a critical next research step by Aloosh and Li (2024).
The estimated wash trading metrics (Figure 1) reveal significant differences in wash trading behavior across Bitcoin, Ethereum, Litecoin, and XRP from late 2020 to early 2022. According to the wash trading volume roundness-based metric, Ethereum exhibits the highest levels of wash trading, followed by XRP and Bitcoin, with Litecoin showing the lowest levels. Ethereum’s peaks in wash trading align with major price surges, indicating attempts to inflate trading volumes and enhance perceived liquidity, a pattern also observed by Cong et al. (2021) in their study on decentralized exchanges during price increases. XRP shows significant wash trading, particularly during its legal battles with the U.S. SEC, likely aimed at countering negative market sentiment. This finding is consistent with Pennec et al. (2021), who observed increased wash trading during periods of regulatory scrutiny. Bitcoin, while still showing notable wash trading, ranks lower than both Ethereum and XRP in terms of inflated trading volumes, aligning with the findings of Foley et al. (2019), who documented substantial but lower levels of wash trading in Bitcoin compared to altcoins. Litecoin, as expected, experiences the least amount of wash trading, reflecting its reputation as a more stable asset with less speculative activity, as also observed by Makarov and Schoar (2020). This stability likely results in fewer attempts to artificially boost trading volumes.
The analysis reveals that both the wash volume metric and, to a weaker level, Benford’s Law-based metric respond to market volatility and uncertainty but capture different aspects of manipulative behaviors. The wash volume metric is more sensitive to immediate market conditions and behavioral factors such as price volatility, momentum, and public interest. It highlights how exchanges and possibly traders might use wash trades to influence perceived liquidity and price movements in the short term. For example, in BTC and ETH, increased volatility leads to higher wash trading, indicating that exchanges exploit volatile conditions to engage in wash trades.”
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