The Crisis and Challenges of the Digital Asset Market: A Thrilling Flash Crash Event

In today's rapidly developing digital economy, the cryptocurrency market is facing unprecedented risks and challenges. On one hand are the requirements for compliance and regulation, while on the other hand lie serious issues of market manipulation and information asymmetry.

On April 14, 2025, at 4 a.m., the cryptocurrency market experienced another upheaval. The MANTRA (OM) token, once regarded as the "benchmark for compliant RWA," faced forced liquidation on multiple centralized trading platforms simultaneously, plunging from $6 to $0.5, with a daily drop exceeding 90%, resulting in a market cap evaporation of $5.5 billion and contract traders losing $58 million. On the surface, it appears to be a liquidity crisis, but in reality, it is a meticulously planned high-level control and cross-platform "harvesting" operation. This article will analyze the reasons behind 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 Incident and the LUNA Crash

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

LUNA flash crash: Primarily triggered by the depegging of the stablecoin UST, its algorithmic stablecoin mechanism relies on LUNA supply balance. When UST deviates from the 1:1 dollar peg, the system falls into a "death spiral," with LUNA dropping from over 100 dollars to nearly 0 dollars, which is an inherent flaw in the system design.

OM flash crash: Investigations show that this event stemmed from market manipulation and liquidity issues, involving forced liquidations on centralized trading platforms and the project team's high level of control, rather than a design flaw in the token.

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

2. Control Structure: 90% is secretly controlled by the team and large holders.

highly concentrated control structure

On-chain monitoring has revealed that the MANTRA team and its associated addresses collectively hold 792 million OM, accounting for approximately 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 market trading volume and liquidity, 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, transforming community traffic into a long-term locking tool by extending the redemption period.

• 20% released upon first launch, quickly expanding market recognition
• First month cliff-style unlock, followed by linear release over the next 11 months, creating an illusion of early prosperity.
• The partial unlocking ratio is as low as 10%, and the remaining tokens will gradually be vested over three years to control the initial circulation.

This strategy appears to be a scientific 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 highly controllable outcome that creates a false trading boom and price support.

OTC discount trading and arbitrage takeover

50% discount sale: The community has repeatedly reported that OM is being sold off in large quantities on the OTC market at a 50% discount, attracting private placements and large investors to take over.

Off-chain and on-chain linkage: Arbitrageurs purchase at low prices off-exchange, then transfer OM to centralized exchanges, creating on-chain trading hype and volume, attracting more retail investors to follow. This "off-chain harvesting and on-chain momentum building" dual cycle further amplifies price volatility.

3. The Historical Issues of MANTRA

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

"Regulatory RWA" label hype: The MANTRA project gained market trust with the endorsement of "Regulatory RWA," having signed a $1 billion tokenization agreement with a UAE real estate giant and obtained a license from a regulatory agency, attracting a large number of institutions and retail investors. However, the regulatory license did not bring real market liquidity and decentralized holdings; instead, it became a cover for team control, leveraging the Middle Eastern regulatory license to attract capital, and regulatory endorsement turned into a marketing tactic.

OTC Sales Model: According to reports, MANTRA has raised over $500 million in the past two years through the OTC sales model, operating by continuously issuing new tokens to absorb the selling pressure from previous round investors, creating a "new in, old out" cycle. This model relies on continuous liquidity, and once the market is unable to absorb unlocked tokens, it may lead to a system collapse.

Legal dispute: In 2024, a high court in a certain location handled the MANTRA DAO case, involving accusations of asset misappropriation. The court required six members to disclose financial information, and there were already issues with governance and transparency.

4. Analysis of the Deeper Causes of Flash Crashes

1) Clearing mechanism and risk model failure

Multi-platform risk parameter fragmentation:

Different trading platforms have not unified the risk control parameters for OM (leverage limit, maintenance margin rate, automatic liquidation trigger point), resulting in the same position facing completely different liquidation thresholds on different platforms. When a certain platform triggers automatic liquidation during low liquidity periods, sell orders overflow to other platforms, causing "cascading liquidations."

Blind spots in tail risk of risk models:

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

2) On-chain capital flow and market maker behavior

Large-scale hot wallet transfers and market maker withdrawals:

A certain hot wallet transferred 33 million OM (approximately 20.73 million USD) to multiple exchanges within 6 hours, suspected to be due to a market maker or hedge fund 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 certain quantitative market maker activates the "flash crash" module when it detects that the OM price has fallen below a key support level, engaging in cross-commodity arbitrage between index contracts and spot trading, which further exacerbates the selling pressure in the spot market and the surge in funding rates for perpetual contracts, forming a vicious cycle of "funding rate-price difference-liquidation."

3) Information asymmetry and lack of warning mechanism

On-chain alerts 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 exchanges have not established a "warning-risk control-community" closed loop, resulting in the on-chain capital flow signals not being translated 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 plummet rapidly, panic selling 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 once surpassed 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:

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; set up dynamic risk control buffers that initiate 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 instantaneous large-scale selling pressure.

• Enhanced tail risk modeling: Introduce stress testing and extreme scenario simulations, embedding "liquidity shock" and "cross-asset squeeze" simulation modules into the risk control system, and conduct 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 publicly auditable. It utilizes 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, with profits and penalties automatically allocated to the insurance pool.

• Flash Crash Insurance: Launching an options-based flash crash insurance product: when the price falls beyond a set threshold within a specified time window, the insurance contract automatically compensates the holder for a portion 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 an "Address Risk Scoring" model to score potential large transfer addresses. If a high-risk address experiences a large transfer, it will automatically trigger 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 simulated extreme market conditions, enhancing risk awareness and response capabilities.

• Tiered leverage products: Launching tiered leverage products for different risk preferences: low-risk levels use traditional clearing models; high-risk levels require additional payment of "tail risk margin" and participation in the flash crash insurance pool.

6. Conclusion

The flash crash event of MANTRA (OM) was not only a significant shock to the cryptocurrency sector but also a severe test of the overall risk management and mechanism design within the industry. Extreme concentration of holdings, market manipulation of false prosperity, and insufficient cross-platform risk control coordination collectively led to 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", and build a more stable and trustworthy ecosystem.