CLS Global FZC LLC, a cryptocurrency market maker headquartered in the UAE, claims to support new project token trading by providing liquidity. From August 23 to September 18, 2024, CLS Global was accused of market manipulation against the "NexFundAI" crypto asset through wash trading, creating false trading volumes to induce investor purchases. The SEC determined "NexFundAI" as a security, violating the anti-fraud and market manipulation provisions of the Securities Act of 1933 and the Securities Exchange Act of 1934.
According to the SEC investigation, CLS Global used 30 wallets to conduct 740 wash trades, generating nearly $600,000 in false trading volume, accounting for 98% of the total trading volume during that period. These trades were driven by algorithms and robots, aimed at creating an illusion of market activity to attract retail investors. Ironically, this manipulation was a "market service" hired by "NexFundAI" promoters, with CLS Global profiting while the project and investors suffered losses.
I. Legal Action and Judgment
On October 9, 2024, the SEC filed a civil lawsuit against CLS Global and its employee Andrey Zhorzhes (Case No. 1:24-cv-12590-AK). Simultaneously, the Massachusetts District Attorney's Office initiated criminal proceedings against them, charging market manipulation and wire fraud. This action was part of an FBI "fishing" operation aimed at combating crypto market irregularities.
On April 7, 2025, the civil case reached a final judgment, requiring CLS Global to:
- Pay fines: $425,000 in civil penalties, $3,000 in illegal gains, and $80.39 in pre-judgment interest;
- Behavioral restrictions: Ensure non-US customers within 30 days, implement compliance policies within 45 days, and submit annual compliance reports for the next three years;
- Fine offset: Criminal fine payments can be deducted from civil penalties.
Andrey Zhorzhes' civil penalties remain unclear and may still be addressed in criminal proceedings, adding uncertainty to the case. The CLS Global case is one of the SEC's landmark enforcement actions against crypto market manipulation in recent years.
II. Market Maker Chaos: From Loan Option Models to Wash Trading
CLS Global's wash trading is just the tip of the iceberg of predatory market maker behaviors in the crypto market. The previously analyzed "Loan Option Model" chaos by Aiying shares similar characteristics, exploiting market opacity and project team inexperience.
Predatory Operations of Loan Option Models
In the crypto market, market makers provide liquidity through "loan option models". Projects lend tokens to market makers, who buy and sell on exchanges to stabilize prices. Contracts typically include option clauses allowing market makers to return or purchase tokens at specific prices in the future. However, some unscrupulous market makers abuse this model:
- Dumping for profit: Massive selling of borrowed tokens to lower prices, triggering panic selling by retail investors, then buying back at low prices to return, profiting from the difference;
- Option manipulation: Using option clauses to return tokens at price troughs, maximizing personal profits;
- Information asymmetry: Project teams lack understanding of contract risks, signing opaque agreements, becoming market makers' "prey".
These actions are devastating for small projects: token prices plummet, community trust collapses, exchanges may delist due to insufficient trading volume, and project financing and survival are jeopardized.
CLS Global's Wash Trading
CLS Global's wash trading shares similarities with the predatory loan option model, focusing on creating market illusions through market maker roles:
- False trading volume: Through self-trading, CLS Global made "NexFundAI" appear active, attracting retail investors;
- Trust destruction: After the false prosperity collapses, investors suffer losses, and the project's reputation is damaged;
- Regulatory loopholes: Wash trading exploits the crypto market's lack of real-time monitoring and transparency, similar to the opaque contracts of loan option models.
Additionally, other market maker tactics mentioned, such as "invisible knife" contracts, liquidity "kidnapping", and false "full package" services, are equally prevalent in the industry. These behaviors collectively cause small project market value to evaporate, communities to dissolve, and severely erode industry trust.
III. Traditional Finance Experience: A "Textbook" for Crypto Markets
Traditional financial markets have also faced similar market manipulation issues but have significantly reduced predatory behaviors through mature regulatory and transparent mechanisms. The CLS Global case sounds an alarm for the crypto industry, and learning from traditional finance is imperative.
Traditional Finance's Approach
Strict Regulation: The US SEC's "Rule SHO" limits naked short selling, requiring stock borrowing before selling; the "uptick rule" prevents malicious price suppression. Section 10b-5 of the Securities Exchange Act severely punishes market manipulation, with similar effects from the EU's Market Abuse Regulation (MAR).
Information Transparency: Listings and market maker agreements must be reported to regulators, trading data is publicly accessible, large transactions must be reported, reducing opportunities for opaque operations.
Real-time Monitoring: Exchanges use algorithms to monitor abnormal fluctuations, triggering investigations; circuit breakers pause trading during severe price volatility to prevent panic spread.
Industry Standards: FINRA sets ethical standards for market makers; NYSE's Designated Market Makers (DMM) must meet strict requirements.
Investor Protection: Class action lawsuits and the Securities Investor Protection Corporation (SIPC) provide accountability and compensation channels.
These measures create a multi-layered protective network, effectively constraining market maker behaviors in traditional markets. For example, during the 2008 financial crisis, malicious short-selling of bank stocks was quickly investigated by the SEC, with multiple institutions fined and regulatory improvements implemented.
