New Challenges in the Crypto Assets Market: Analyzing the MANTRA (OM) Flash Crash Incident

In the context of the rapid development of the digital economy, the Crypto Assets market is facing unprecedented risks and challenges. On one hand, there are compliance and regulatory requirements, while on the other hand, there are serious issues of market manipulation and information asymmetry.

On April 14, 2025, at 4 a.m., the Crypto Assets market sparked another uproar. The MANTRA (OM) token, once regarded as a "compliance RWA pioneer," faced forced liquidation on multiple centralized exchanges, with its price plummeting from $6 to $0.5, a decline of over 90% in a single day, evaporating $5.5 billion in market capitalization, and causing contract traders to suffer losses of $58 million. On the surface, it appeared to be a liquidity storm, but in reality, it was a premeditated high-level manipulation and cross-platform "harvesting game." This article will delve into the causes of this flash crash, reveal the truth behind it, and explore the future development direction of the Web3 industry, as well as how to prevent similar incidents from happening again.

1. Comparison of the OM flash crash event and the LUNA crash

The OM flash crash event bears certain similarities to the LUNA collapse of the Terra ecosystem in 2022, but the causes are different:

The LUNA crash was primarily triggered by the de-pegging of the stablecoin UST, whose algorithmic stablecoin mechanism relies on a balance of LUNA supply. When UST deviates from the 1:1 dollar peg, the system enters a "death spiral," causing LUNA to plummet from over $100 to near $0, which is a systemic design flaw.

The OM flash crash is due to market manipulation and liquidity issues, involving forced liquidations by centralized exchanges and the team's high control over the market, rather than a flaw in the token design.

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

2. Control Structure - 90% Team and Large Holders Secretly Hold

ultra-high concentration control architecture

On-chain monitoring shows that the MANTRA team and its associated addresses collectively hold 792 million OM, accounting for about 90% of the total supply, while the actual circulating tokens are less than 88 million, only about 2%. Such an astonishing concentration of holdings has led to a severe imbalance in trading volume and liquidity in the market, allowing large holders to easily influence price fluctuations during periods of low liquidity.

Staged Airdrop and Lockup Strategy - Creating False Hype

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

• 20% released upon first launch to quickly expand market awareness
• The first month features a cliff-style unlocking, followed by linear releases over the next 11 months, creating an illusion of early prosperity.
• The partial unlock 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 scientific allocation on the surface, but in reality, it leverages high commitments to attract investors. When user sentiment rebounds, the project side 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 related parties, resulting in strong controllability, creating a false trading boom and price support.

OTC discount trading and arbitrage接盘

Multiple reports from the community indicate that OM is being sold off at a 50% discount on the over-the-counter market, attracting private placements and large holders to take over. Arbitrageurs purchase OM at a low price off-exchange, then transfer it to centralized exchanges, creating on-chain trading hype and volume, which draws more retail investors in. This "off-chain harvesting and on-chain hype" dual cycle further amplifies price fluctuations.

3. The Historical Issues of MANTRA

The flash crash of MANTRA has historical issues that have laid hidden dangers for this event:

"Compliance RWA" label hype: The MANTRA project gained market trust with its "Compliance RWA" endorsement, having signed a $1 billion tokenization agreement with a UAE real estate giant and obtained a VARA VASP license, attracting numerous institutions and retail investors. However, the compliance license did not bring real market liquidity and decentralized holdings; instead, it became a cover for the team to control the market, siphoning funds using the Middle Eastern compliance license, while regulatory endorsement turned into a marketing tactic.

OTC Sales Model: It is reported that MANTRA has raised over $500 million through the OTC sales model in the past two years, operating by continuously issuing new tokens to absorb the selling pressure from previous round investors, creating a cycle of "new replacing old, old exiting new". This model relies on continuous liquidity; once the market cannot absorb the unlocked tokens, it could lead to a system collapse.

Legal Dispute: In 2024, the Hong Kong High Court handled the MANTRA DAO case, involving allegations of asset misappropriation. The court required six members to disclose financial information, as there were issues related to governance and transparency.

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

1) The clearing mechanism and risk model have failed.

Multi-platform risk parameter fragmentation: Different centralized exchanges have not unified the risk control parameters for OM (maximum leverage, maintenance margin rate, automatic liquidation trigger point), resulting in the same position facing completely different liquidation thresholds on different platforms. When a platform triggers automatic liquidation during low liquidity periods, sell orders spill over to other platforms, causing "cascading liquidations".

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-amount hot wallet transfer and market maker withdrawal: A certain hot wallet transferred 33 million OM (approximately 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 in anticipation of extreme volatility, they often choose to withdraw the provided two-way liquidity to avoid market risk, leading to a rapid widening of the bid-ask spread.

The amplification effect of algorithmic trading: An automated strategy of a quantitative market maker initiates the "flash sell" module when it detects that the OM price has breached a key support level, engaging in cross-product arbitrage between index contracts and spot, which further exacerbates the selling pressure in the spot market and the surge in funding rates of perpetual contracts, forming a vicious cycle of "funding rate-price difference-liquidation."

3) Information asymmetry and the lack of early warning mechanisms

On-chain alerts and community response lag: Although there are mature on-chain monitoring tools that can provide real-time alerts for large transfers, 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 investor behavior: In the absence of authoritative information sources, retail investors and small to medium institutions rely on social media and market push notifications. When prices plummet rapidly, panic selling intertwines with "bottom fishing," amplifying trading volume (a 312% increase in trading volume compared to the previous 24 hours) and volatility (the 30-minute historical volatility once broke through 200%).

V. Industry Reflection and Systemic Countermeasure Recommendations

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

1. Unified and Dynamic Risk Control Framework

• Industry standardization: Establish cross-platform clearing protocols, including clearing threshold interoperability, real-time sharing of key parameters and large holder position snapshots across platforms; dynamic risk control buffer, activating a "buffer period" after a clearing trigger, allowing other platforms to provide limit buy orders or algorithmic market makers to participate in the buffer, avoiding instant large-scale selling pressure.

• Strengthening the tail risk model: introducing stress testing and extreme scenario simulations, embedding "liquidity shock" and "cross-commodity squeeze" simulation modules into the risk control system, and conducting regular systemic drills.

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 drops below the threshold, community nodes will compete to complete the clearing, with profits and penalties automatically distributed to the insurance pool.

• Flash Crash Insurance: Launching an options-based flash crash insurance product: When the OM price falls beyond the set threshold within a specified time window, the insurance contract automatically compensates the holder for part of the loss. The insurance premium rate is dynamically adjusted based on historical volatility and on-chain fund concentration.

3. On-chain Transparency and Early Warning Ecosystem Construction

• Whale Behavior Prediction Engine: Project parties should collaborate with data analysis platforms to develop an "Address Risk Scoring" model, scoring potential high-value transfer addresses. If a high-risk address undergoes a large transfer, it automatically triggers alerts to the platform and community.

• Community Risk Control Committee: Composed of project parties, core advisors, major market makers, and representative users, responsible for reviewing major 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 simulated trading environment that allows users to practice stop-loss, position reduction, hedging, and other strategies in extreme market conditions, enhancing risk awareness and response capabilities.

• Tiered leverage products: Tiered leverage products are launched for different risk preferences: low-risk levels use traditional clearing methods; 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 Crypto Assets field but also a severe test of the overall risk management and mechanism design of the industry. Extreme concentration of positions, market manipulation of false prosperity, and insufficient cross-platform risk control linkage have jointly 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 for investors can we fundamentally enhance the resilience of the Web3 market, prevent the recurrence of future similar "flash crash storms", and build a more stable and trustworthy ecosystem.