Flash Crash Storm in the Crypto Market: Depth Analysis and Future Outlook

In the wave of rapid development of the digital economy, the crypto market is facing unprecedented risks and challenges. On one hand, there is the facade of compliance and regulation, while on the other hand, there are severe issues of market manipulation and information asymmetry lurking beneath.

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

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

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

LUNA flash crash: Mainly triggered by the depegging 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," causing LUNA to drop 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 a flaw in the token design.

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

2. Control Structure - 90% Team and Market Makers Holding in Secret

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 88 million, making up only about 2% of the proportion. 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 locked asset tool.

• Release 20% upon first listing to quickly expand market awareness;
• The first month features a cliff-like unlock, followed by a 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 team 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 control over outcomes, creating a false trading boom and price support.

OTC discount trading and arbitrage接盘

50% discount sale: Multiple reports from the community indicate that OM is being sold off in large quantities at a 50% discount in the over-the-counter market, attracting private placements and large holders to take over.

Off-chain and on-chain interaction: Arbitrageurs purchase at a low price off the 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 historical issues that have buried 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 UAE real estate giant Damac and obtained a VARA VASP license, attracting a large number of institutions and retail investors. However, the compliance license did not bring true market liquidity and decentralized holdings, but instead became a cover for team control, leveraging Middle Eastern compliance licenses to attract capital, while regulatory endorsement 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 the previous round of investors, forming a cycle of "new in for old, old out for new." 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 handled the MANTRA DAO case, involving allegations of asset misappropriation. The court required six members to disclose financial information, and there were already issues with governance and transparency.

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

1) The clearing mechanism and risk model fail.

Fragmentation of risk parameters across multiple platforms:

The risk control parameters (leverage limit, maintenance margin rate, automatic deleveraging trigger point) for OM are not uniform across centralized exchanges, leading to drastically different liquidation thresholds for the same position on different platforms. When a platform triggers automatic deleveraging (ADL) during a low liquidity period, sell orders spill over to other platforms, causing "cascading liquidations."

Blind spots in the tail risk of risk models:

Most centralized exchanges use VAR (Value at Risk) models based on historical volatility, which underestimate extreme market conditions (tail events) and fail to simulate "gaps" or "liquidity exhaustion" scenarios. Once the market depth suddenly drops, the VAR model becomes ineffective, and the triggered risk control instructions actually exacerbate the 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 (≈ 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 expectations of extreme volatility, they often choose to withdraw the bidirectional liquidity provided to avoid market risk, causing the spread to widen rapidly.

The Amplification Effect of Algorithmic Trading:

An automated strategy of a certain quantitative market maker activated the "flash selling" module when it detected that the OM price fell below a key support (5% below the 10-day moving average), engaging in cross-commodity arbitrage between index contracts and spot, which further intensified the selling pressure in the spot market and caused the funding rate of perpetual contracts to soar, creating a vicious cycle of "funding rate - price difference - liquidation."

3) Information asymmetry and lack of early warning mechanism

On-chain warning 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 transformed 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 updates. When prices rapidly decline, panic liquidations and "bottom fishing" intertwine, significantly amplifying trading volume (a 312% increase in trading volume compared to the previous 24 hours) and volatility (the 30-minute historical volatility briefly exceeded 200%).

V. Industry Reflection and Systemic Policy Recommendations

To respond to such events and 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 a Cross-Exchange Liquidation Protocol (CELP), which includes: intercommunication of liquidation thresholds, real-time sharing of key parameters (maintenance margin rate, ADL trigger line) and snapshots of large holders' positions across platforms; dynamic risk control buffers, initiating a "liquidation grace period" (T+δ) after a liquidation trigger, allowing other platforms to provide limit buy orders or algorithmic market makers to participate in the buffer, avoiding instantaneous large-scale sell pressure.

• Enhanced tail risk model: Introduce stress testing and scenario analysis, embedding "liquidity shock" and "cross-asset squeeze" simulation modules in the risk control system, and conduct regular systemic drills.

2. Decentralization and Innovation of Insurance Mechanisms

• Decentralized Liquidation Chain

The clearing system based on smart contracts puts the clearing logic and risk control parameters on-chain, making all clearing transactions publicly auditable. Utilizing cross-chain bridges and oracles to synchronize multi-platform prices, once the price falls below the threshold, community nodes (liquidators) bid to complete the clearing, with profits and penalties automatically distributed to the insurance pool.

• Flash Crash Insurance

Launch a flash crash insurance product based on options: when the OM price falls by more than a set threshold (e.g., 50%) 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 capital concentration.

3. On-chain Transparency and Early Warning Ecosystem Construction

• Whales Behavior Prediction Engine

The project party should cooperate with data analysis platforms to develop the "Address Risk Score" (ARS) model, scoring potential large transfer addresses. Addresses with a high ARS will automatically trigger alerts from the platform and community once a large transfer occurs.

• Community Risk Committee

Composed of project parties, core consultants, 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 capability.

• Tiered leverage products

In response to different risk preferences, leveled leverage products are launched: low-risk level (leverage ≤2×) uses traditional clearing mode; high-risk level (leverage ≥5×) requires an additional "tail risk margin" and participation in the flash crash insurance pool.

VI. 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 holdings, manipulative market operations, and insufficient cross-platform risk control linkage 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 for investors can we fundamentally enhance the resilience of the Web3 market, prevent the recurrence of similar "flash crash storms" in the future, and build a more stable and trustworthy ecosystem.