The Cryptocurrency Asset Market in the Wave of Digital Economy: Risks and Challenges Coexist

Against the backdrop of the rapid development of the digital economy, the encryption asset market is facing unprecedented challenges. On one hand, there are the requirements of regulatory compliance, while on the other hand, there are serious issues of market manipulation and information asymmetry.

In the early morning of April 14, 2025, the cryptocurrency market was once again thrown into chaos. The MANTRA(OM) token, once seen as the "compliance RWA benchmark", faced forced liquidations simultaneously across multiple trading platforms, with its price plummeting from $6 to $0.5, a one-day decline of over 90%, resulting in a market cap evaporation of $5.5 billion, and contract players suffering losses of $58 million. On the surface, it appeared to be a liquidity storm, but in reality, it was a highly orchestrated control and cross-platform "harvesting game" that had been premeditated. 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 of the OM Flash Crash Event and the LUNA Crash

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

The LUNA crash: primarily triggered by the depegging of the stablecoin UST, the algorithmic stablecoin mechanism relies on the balance of LUNA supply. When UST deviates from the 1:1 dollar peg, the system enters a "death spiral", and LUNA falls from above 100 dollars to nearly 0 dollars, which is a systemic design flaw.

OM flash crash: Investigations indicate that this incident is due to market manipulation and liquidity issues, involving forced liquidations by the trading platform and the team's high control actions, rather than a flaw in the token design.

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

2. Control Structure - 90% of the Team and Whales Holding Stealthily

ultra-high concentration control structure

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 8.8 million, representing only about 2%. Such an astonishing concentration of holdings has led to a severe imbalance in both trading volume and liquidity in the market, allowing large holders to easily influence 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 realization cycle to transform community traffic into a long-term locking tool.

• 20% released upon first launch to quickly spread market awareness;
• The first month features a cliff-style unlocking, followed by linear release over the next 11 months, creating the illusion of early prosperity;
• The partial unlocking ratio is as low as 10%, with the remaining tokens gradually vesting 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 related parties, resulting in a high degree of controllability, creating a false trading boom and price support.

OTC discount trading and arbitrage take-over

50% discounted sales: Multiple reports from the community indicate that OM is selling off in large quantities at a 50% discount in the over-the-counter market, attracting private placements and large investors to take over.

Off-chain and on-chain linkage: Arbitrageurs purchase at low prices off the market, then transfer OM to the exchange to create on-chain trading heat and volume, attracting more retail investors to follow. This dual cycle of "off-chain harvesting and on-chain hype" further amplifies price fluctuations.

3. Historical Issues of MANTRA

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

The hype around the "compliant RWA" label: The MANTRA project gained market trust with its "compliant RWA" endorsement, having signed a $1 billion tokenization agreement with UAE real estate giant Damac and obtained a VARA VASP license, attracting a large number of institutional and retail investors. However, the compliant license did not bring real market liquidity and decentralized holdings, but instead became a cover for the team's control over the market, using the Middle Eastern compliant license to attract funds, while regulatory endorsement turned into a marketing tool.

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

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

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

1) Clearing mechanism and risk model failure

Multi-platform risk parameter fragmentation:

Different exchanges have not unified the risk control parameters for OM, such as the leverage limit, maintenance margin rate, and automatic deleveraging trigger point (, which leads to entirely different liquidation thresholds for the same position on different platforms. When a platform triggers automatic deleveraging ) during low liquidity periods, sell orders spill over to other platforms, causing "cascading liquidations" (.

Blind spots of tail risk in risk models:

Most exchanges use a VAR) Value at Risk( model based on historical volatility, which underestimates extreme market conditions) tail events( and fails to simulate "gap" or "liquidity exhaustion" scenarios. Once the market depth suddenly drops, the VAR model becomes ineffective, and the triggered risk control measures instead exacerbate liquidity pressure.

) 2( On-chain capital flow and market maker behavior

Large Amount Hot Wallet Transfer and Market Maker Withdrawal:

The FalconX hot wallet transferred 33 million OM) ≈ 20.73 million USD### to multiple 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 expectations 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 certain quantitative market maker triggers the "flash selling" module when it detects that the OM price has fallen below the key support line of the 10-day moving average by 5% (, engaging in cross-species arbitrage between index contracts and spot, further intensifying the selling pressure in the spot market and causing the funding rate of perpetual contracts to soar, forming a vicious cycle of "funding rate - price spread - liquidation."

) Information asymmetry and lack of early warning mechanisms

On-chain alerts and community responses are lagging:

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

Herding 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 plummet rapidly, panic selling and "bottom fishing" intertwine, significantly increasing trading volume in the short term. 24-hour trading volume increased by 312% compared to the previous period, and volatility reached 130-minute historical volatility, which once exceeded 200%.

V. Industry Reflection and Systematic Policy Recommendations

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

( 1. Unified and Dynamic Risk Control Framework

• Industry Standardization: For example, formulating cross-platform clearing protocols, including: clearing threshold intercommunication, real-time sharing of key parameters and large holder position snapshots among platforms; dynamic risk control buffer, initiating a "buffer period" after the clearing is triggered, allowing other platforms to provide limit buy orders or algorithmic market makers to participate in the buffer to avoid instantaneous large-scale selling pressure.

• Strengthening tail risk models: Introducing stress testing and extreme scenario simulations, embedding "liquidity shocks" and "cross-commodity squeezes" simulation modules into the risk control system, and regularly conducting systematic drills.

) 2. Decentralization and Innovation of Insurance Mechanisms

• Decentralized Settlement Chain

The clearing system based on smart contracts integrates clearing logic and risk control parameters on-chain, with all clearing transactions being publicly auditable. By utilizing cross-chain bridges and oracles to synchronize prices across multiple platforms, once the price falls below the threshold, community nodes will bid to complete the clearing, with profits and penalties automatically allocated to the insurance pool.

• Flash Crash Insurance

Launch of an options-based flash crash insurance product: When the OM price falls by more than the set threshold (, such as 50%, 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 Warning Ecosystem Construction

• Whale Behavior Prediction Engine

The project team should collaborate with the data analysis platform to develop the "Address Risk Score"###ARS) model to score potential large transfer addresses. If an address with a high ARS undergoes a large transfer, it will automatically trigger alerts for 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 alerts or recommending risk control adjustments when necessary.

( 4. Investor Education and Market Resilience Improvement

• 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 their risk awareness and response capabilities.

• Tiered Leverage Products

For different risk preferences, tiered leverage products are launched: 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 cryptocurrency field but also a severe test of the overall risk management and mechanism design of the industry. As we elaborated in the article, extreme concentration of positions, false prosperity in market operations, and insufficient cross-platform risk control coordination collectively created this "harvesting game."

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

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