Risks and Challenges of the Encryption Asset Market: Insights on Industry Development from the OM Flash Crash Incident

In the context of the rapid development of the digital economy, the encryption asset market is facing unprecedented risks and challenges. On one hand, there is the cloak of compliance and regulation, while on the other hand, there are severe issues of market manipulation and information asymmetry lurking underneath.

On April 14, 2025, at 4 a.m., the cryptocurrency market was once again thrown into chaos. The MANTRA(OM) token, once regarded as a "compliance RWA barometer", faced forced liquidation on multiple centralized exchanges simultaneously, with the price plummeting from $6 to $0.5, a single-day drop of over 90%, and a market capitalization evaporation of $5.5 billion, resulting in contract players losing $58 million. On the surface, it seemed like a liquidity storm, but in reality, it was a premeditated high-level manipulation and a cross-platform "harvesting game". This article will delve into the causes of this flash crash, reveal the truth behind it, and discuss the future development direction of the Web3 industry, as well as how to prevent similar incidents from happening again.

1. Comparison between the OM flash crash event and the LUNA collapse

The OM flash crash event has some similarities with the 2022 LUNA crash of the Terra ecosystem, but the causes are different:

LUNA crash: mainly triggered by the de-pegging of the stablecoin UST, the algorithmic stablecoin mechanism relies on LUNA supply balance. When UST deviates from the 1:1 dollar peg, the system enters a "death spiral", with LUNA falling from over 100 dollars to nearly 0 dollars, which is a systemic design flaw.

OM flash crash: Investigations indicate that this incident is related to market manipulation and liquidity issues, involving forced liquidations by centralized exchanges and high control actions by the team, rather than design flaws in the tokens.

Both triggered market panic, but LUNA was a collapse of the ecosystem, while OM is more like an imbalance in market dynamics.

2. Control Structure - 90% of the team and market makers hold secretly

ultra-high concentration control structure

On-chain monitoring shows that the MANTRA team and its associated addresses hold a total of 792 million OM, accounting for about 90% of the total supply, while the actual circulating tokens are less than 8.8 million, only about 2%. Such an astonishing concentration of holdings has resulted in a severe imbalance in trading volume and liquidity in the market, allowing large holders to easily manipulate price fluctuations during periods of low liquidity.

Phased Airdrop and Lock-up Strategy - Creating False Hype

The MANTRA project adopts a multi-round unlocking scheme, continuously extending the redemption period to transform community traffic into a long-term locking tool.

• 20% will be released upon the first launch to quickly spread market awareness;
• The first month unlocks in a cliff-like manner, followed by linear release over the next 11 months, creating the illusion of early prosperity;
• The partial unlocking ratio is as low as 10%, and the remaining tokens will gradually vest over three years to reduce the initial circulation.

This strategy appears to be a scientifically sound allocation on the surface, but in reality, it uses high commitments to attract investors. When user sentiment rebounds, the project party introduces a governance voting mechanism to shift responsibility in the form of "community consensus." However, in practice, voting rights are concentrated in the hands of the project team or affiliated parties, resulting in a high degree of controllability, creating a false sense of trading prosperity and price support.

OTC discount trading and arbitrage接盘

• 50% discount sales: Multiple reports from the community indicate that OM is being sold off in large volumes at a 50% discount in the over-the-counter market, attracting private placements and large holders.
• Off-chain and on-chain interaction: Arbitrageurs buy at a low price in the over-the-counter market, then transfer OM to centralized exchanges, creating on-chain trading enthusiasm and volume, attracting more retail investors to follow. This "off-chain harvesting and on-chain hype" dual cycle further amplifies price volatility.

3. Historical Issues of MANTRA

The flash crash of MANTRA has also buried hidden dangers for this event due to historical issues.

"Compliance RWA" label hype: The MANTRA project has gained market trust with its "Compliance RWA" endorsement, having signed a $1 billion tokenization agreement with UAE real estate giant Damac and obtained the VARA VASP license, attracting a large number of institutions and retail investors. However, the compliance license has not brought real market liquidity and decentralized holdings; instead, it has become a cover for the team's control over the market, using the Middle Eastern compliance license to raise funds, and regulatory endorsement has turned into a marketing tactic.

OTC Sales Model: According to reports, MANTRA has raised over $500 million through the OTC sales model in the past two years. The operation method is to continuously issue new tokens to absorb the selling pressure from previous round investors, forming a "new to old, old out new" cycle. This model relies on continuous liquidity, and once the market cannot absorb the unlocked tokens, it may lead to a system collapse.

Legal dispute: In 2024, the Hong Kong High Court is handling the MANTRA DAO case, involving allegations of asset misappropriation. The court has required six members to disclose financial information, as there are inherent issues with its governance and transparency.

4. In-depth Analysis of the Causes of Flash Crashes

1. The liquidation mechanism and risk model failure

Multi-platform risk parameter fragmentation: The risk control parameters for OM are not unified across various centralized exchanges, resulting in the same position facing drastically different liquidation thresholds on different platforms. When a certain platform triggers automatic position reduction during low liquidity periods, sell orders spill over to other platforms, causing "cascading liquidation".

Blind spots in the tail risk of risk models: Most centralized exchanges use VAR models based on historical volatility, which underestimate extreme market conditions and fail to simulate "gaps" or "liquidity exhaustion" scenarios. Once market depth suddenly declines, the VAR model becomes ineffective, and the triggered risk control instructions exacerbate liquidity pressure.

2. On-chain capital flow and market maker behavior

Large Hot Wallet Transfers and Market Maker Withdrawals: FalconX's hot wallet transferred 33 million OM( ≈ 20.73 million USD) to multiple centralized exchanges within 6 hours, suspected to be due to market makers or hedge funds liquidating positions. Market makers typically hold net neutral positions in high-frequency strategies, but under extreme volatility expectations, they often choose to withdraw the provided two-way liquidity to avoid market risks, leading to a rapid widening of the bid-ask spread.

The amplification effect of algorithmic trading: When a quantitative market maker's automated strategy detects that the OM price has broken through a key support level, it activates the "flash sale" module, arbitraging between index contracts and spot trading, further intensifying the selling pressure in the spot market and the surge in the funding rate of perpetual contracts, creating a vicious cycle of "funding rate - price spread - liquidation."

3. Information asymmetry and lack of warning mechanism

On-chain warnings and community response lag: Although there are mature on-chain monitoring tools that can provide real-time alerts for large transfers, the project parties and major centralized exchanges have not established a "warning-risk control-community" closed loop, resulting in on-chain capital flow signals not being converted into risk control actions or community announcements.

The herd effect from the perspective of behavioral finance: In the absence of authoritative information sources, retail investors and small to medium-sized institutions rely on social media and market alerts. When prices rapidly decline, panic selling and "bottom fishing" intertwine, amplifying trading volume and volatility in the short term.

5. Industry Reflection and Systemic Countermeasure Suggestions

In response to such events and to prevent the recurrence of similar risks in the future, we propose the following countermeasures and suggestions for reference only:

1. Unified and Dynamic Risk Control Framework

• Industry Standardization: For example, developing cross-platform clearing protocols, including clearing threshold interoperability, real-time sharing of key parameters and large account position snapshots among platforms; dynamic risk control buffering, initiating a "buffer period" after the clearing trigger, allowing other platforms to provide limit buy orders or algorithmic market makers to participate in the buffer, avoiding instantaneous large-scale sell-offs.
• Strengthening tail risk models: Introducing stress testing and extreme scenario simulations, embedding "liquidity shocks" and "cross-asset squeezes" simulation modules into the risk control system, and conducting systematic drills regularly.

2. Decentralization and Insurance Mechanism Innovation

• Decentralized Clearing Chain: A clearing system based on smart contracts that puts clearing logic and risk control parameters on-chain, making all clearing transactions public and auditable. By utilizing cross-chain bridges and oracles to synchronize prices across multiple platforms, once the price falls below a threshold, community nodes compete to complete the clearing, and profits and penalties are automatically allocated to the insurance pool.
• Flash Crash Insurance: Launching an options-based flash crash insurance product: When the OM price falls more than the set threshold within a specified time window, the insurance contract automatically compensates the holder for part of the loss. The insurance rate is dynamically adjusted based on historical volatility and on-chain capital concentration.

3. On-chain Transparency and Early Warning Ecosystem Construction

• Whale behavior prediction engine: Project parties should collaborate with data analysis platforms to develop the "Address Risk Score" model, scoring potential large transfer addresses. If an address with a high ARS undergoes a large transfer, it will automatically trigger alerts to the platform and the community.
• Community Risk Control Committee: Composed of project parties, core advisors, major market makers, and representative users, responsible for reviewing significant on-chain events and platform risk control decisions, and issuing risk notices or recommending risk control adjustments when necessary.

4. Investor Education and Market Resilience Enhancement

• Extreme Market Simulation Platform: Develop a simulation trading environment that allows users to practice stop-loss, position reduction, hedging, and other strategies in simulated extreme market conditions, enhancing risk awareness and response capabilities.
• Tiered leverage products: For different risk preferences, tiered leverage products are launched: low-risk levels use traditional clearing models; high-risk levels require additional "tail risk margin" and participation in the flash crash insurance pool.

Conclusion

The flash crash event of MANTRA(OM) is not only a significant shock in the encryption currency field but also a severe test of the overall risk management and mechanism design of the industry. Extreme concentration of positions, false prosperity in market operations, and insufficient cross-platform risk control linkages have collectively forged this "harvesting game."

Only through cross-platform standardized risk control, decentralized clearing and insurance innovation, on-chain transparent early warning ecosystem construction, and extreme market education aimed at investors can we fundamentally enhance the resilience of the Web3 market, prevent the recurrence of similar "flash crash storms," and build a more stable and trustworthy ecosystem.