June 26th, 2026

Wall Street May Embrace Tokenized Stocks, But Not on Public Blockchains

Many crypto enthusiasts dream of trading traditional equities around the clock on public blockchains. They imagine a decentralized utopia where anyone can buy fractional shares of major corporations without traditional brokers.

This vision fundamentally misunderstands how institutional finance operates. In my opinion, major tokenized stocks will never migrate to public networks. The future of twenty-four-hour equity trading belongs exclusively to private or semi-private blockchain architectures.

Regulatory Signals Fuel the Narrative

The United States Securities and Exchange Commission recently proposed rescinding two key rules under Regulation National Market System.

These rules require trades to be routed to the national best price and prohibit locked or crossed quotes across venues. Analysts like Alex Thorn note that automated market makers on public chains conflict with these requirements because they execute against isolated liquidity pools without checking off-chain quotes. Removing the rules could theoretically open the door to compliant on-chain trading of tokenized United States equities.

However, this remains a medium-term structural adjustment rather than an immediate green light. The proposal still faces a lengthy comment process, and platforms would still need to register as exchanges or alternative trading systems, satisfy clearing obligations, and ensure token holders retain voting and dividend rights.

Traditional market groups also warn that removing the rules could reduce price transparency and fragment markets.

Operational Constraints of Public Blockchains

Even with favorable regulations, public blockchains present significant operational hurdles for institutional equity trading. Gas fee volatility remains a primary deterrent. A surge in retail activity can congest public networks and sharply increase transaction costs.

Institutions cannot risk large equity settlements being delayed or becoming more expensive because of unrelated retail traffic. Traditional finance requires deterministic execution.

A bank executing a large block trade needs certainty around cost and settlement timing. Institutional traders require millisecond precision and reliable finality. Public networks prioritize openness and censorship resistance over the predictable throughput global capital markets demand.

Maximal Extractable Value (MEV) presents another critical barrier. Public blockchains broadcast pending transactions in a public mempool before execution. Sophisticated actors deploy bots to scan this information and front-run large orders by manipulating transaction ordering.

Billions of dollars have been extracted through these practices in recent years. This directly conflicts with the fiduciary obligations of traditional brokers and institutional mandates requiring best execution. Financial institutions are unlikely to embrace a system that permits such extraction from client order flow.

Privacy, Compliance, and Control Requirements

Privacy and compliance requirements further strengthen the case against public ledgers. Traditional finance operates under strict Know Your Customer and Anti-Money Laundering regulations.

Public blockchains expose transaction data to everyone. Institutions cannot broadcast their strategic positioning or client holdings on a transparent ledger. Regulators also require the ability to freeze assets or reverse transactions under specific legal circumstances. Public blockchains generally resist these interventions, creating challenges when compliance frameworks require administrative control.

Private networks provide the logical solution. A private blockchain functions as a shared, cryptographically secure ledger maintained by a trusted group of regulated institutions.

This architecture delivers many of the benefits of distributed ledger technology without the unpredictability of public networks. Competitors cannot observe order flows, trade sizes, or account balances. Transactions remain confidential between authorized participants and regulators.

These networks can also streamline clearing and settlement by enabling institutions to transact directly with one another. This lowers costs, reduces counterparty risk, and supports continuous settlement. Enterprise networks further offer dedicated support and contractual service guarantees that public protocols do not provide.

Institutional Adoption Is Already Underway

Major financial institutions already recognize this reality. J.P. Morgan operates its Onyx platform for tokenized intraday repurchase agreement trades and payments. Goldman Sachs uses its Digital Asset Platform to issue and trade digital bonds and other institutional instruments.

HSBC’s Orion platform supports tokenized gold and digital bond issuance. These examples demonstrate that financial institutions view blockchain primarily as infrastructure for automation, synchronization, and efficiency within controlled environments.

The Direction of Tokenized Equities

Market participants continue to pursue the vision of trading major corporate shares on public decentralized exchanges. Yet the structural, regulatory, and operational realities of global finance point elsewhere.

The Securities and Exchange Commission may eventually adapt market rules for digital assets, but the infrastructure itself will remain largely in private hands.

Tokenized equities are far more likely to thrive on secure, permissioned networks designed for institutional performance and compliance than on fully public chains. The future of financial innovation is not public exposure. It is private, efficient infrastructure built to meet the demands of modern capital markets.

Source:

https://www.financemagnates.com/cryptocurrency/wall-street-may-embrace-tokenized-stocks-but-not-on-public-blockchains/

Anndy Lian

Anndy Lian is an early blockchain adopter and experienced serial entrepreneur who is known for his work in the government sector. He is a best selling book author- “NFT: From Zero to Hero” and “Blockchain Revolution 2030”.

Currently, he is appointed as the Chief Digital Advisor at Mongolia Productivity Organization, championing national digitization. Prior to his current appointments, he was the Chairman of BigONE Exchange, a global top 30 ranked crypto spot exchange and was also the Advisory Board Member for Hyundai DAC, the blockchain arm of South Korea’s largest car manufacturer Hyundai Motor Group. Lian played a pivotal role as the Blockchain Advisor for Asian Productivity Organisation (APO), an intergovernmental organization committed to improving productivity in the Asia-Pacific region.

An avid supporter of incubating start-ups, Anndy has also been a private investor for the past eight years. With a growth investment mindset, Anndy strategically demonstrates this in the companies he chooses to be involved with. He believes that what he is doing through blockchain technology currently will revolutionise and redefine traditional businesses. He also believes that the blockchain industry has to be “redecentralised”.

www.anndy.com

j j j

April 26th, 2026

AI trading agents are only as trustworthy as their data

Key points:

AI agents now pose a greater systemic risk to crypto than traditional hackers or fraud.
Markets are vulnerable because attackers can easily poison the news data that AI agents ingest.
AI often follows patterns without understanding context, leading to immediate and highly amplified market errors.
Minimal capital is needed to trigger a crash by seeding false narratives across social media.
Maintaining human oversight is the most vital safeguard against rapid and synchronized algorithmic market failures.

Imagine a major crypto exchange declaring insolvency out of the blue. In the past, hackers or fraud caused wipeouts worth billions of dollars, but today? AI could just as easily be the culprit.

With AI agents that can autonomously trade on cryptocurrency exchanges being pushed by various players in the industry, agents causing a crypto crash is a plausible scenario.

Simply put, if an AI agent is designed to make trades based on market information – including news articles or social media posts – it would be relatively easy to “poison” those sources with false narratives. This could trigger a wave of automated selling from agents that couldn’t distinguish the rumor from reality, which could then crash a coin or a whole market.

While no such attack has happened yet, the conditions for one already exist. The question is no longer if an AI-driven financial crisis will occur, but when – and, more unsettlingly, how little capital it might take to trigger one.

In my work as an advisor to Web3 companies and government organizations, I have watched the narrative around AI in crypto shift from cautious optimism to uncritical adoption.

Today, 45.7% of platform interactions on Binance are system-triggered rather than user-initiated, which means they are carried out by a computer, not a human. That share is only growing, and every percentage point represents a wider attack surface for anyone looking to exploit these agents.

How AI trading agents work

While AI trading agents are designed to bring efficiency, they are also highly vulnerable. The combination of autonomous agents, high-frequency trading infrastructure, and an information ecosystem saturated with synthetic media has created a perfect storm for potential attacks.

At a basic level, these agents ingest market data – price movements, order books, news, and social sentiment – and use machine learning models to identify patterns or signals that inform trading decisions. Once certain conditions are met, they execute trades automatically, often at high speed and without human intervention.

However, recent research underscores how fragile these agents are in ways that should alarm anyone using them.

A study released in February tested 13 AI trading models using distorted or misleading market data. Most didn’t adapt at all, and their performance barely changed, suggesting they were just following fixed strategies rather than reacting to new signals.

When false signals were introduced, some models saw sharp drops in performance, showing how easily they could be thrown off by bad information.

The study also identified what it calls a “competence mirage”: models that identified the correct trading strategy but got the underlying numbers wrong. Knowing what to do and being able to execute it accurately are, it turns out, very different things.

This serves as a reminder that AI agents aren’t sophisticated market participants but pattern-matching engines operating on the data they are fed. When that data is poisoned through coordinated fake news or purchased synthetic datasets, the reaction is immediate and amplified.

Plan of attack

How would such an attack on crypto trading agents work in practice?

An attacker wouldn’t need large amounts of capital to influence the flow of information that trading systems respond to. That could mean seeding false narratives across news outlets, social media, or data feeds using trigger phrases like “liquidity crisis” or “regulatory crackdown,” prompting the agents to react as if the threat were real.

This isn’t purely theoretical, as false information has moved markets before. When the Associated Press Twitter account was hacked in 2013, a single fake tweet briefly wiped billions off the S&P 500.

Events like the 2010 Flash Crash have also shown how automated trading can amplify shocks at speed. In crypto markets, where sentiment already drives volatility, the bar to trigger a cascade may be even lower.

A relatively well-funded actor could seed false narratives across news feeds, coordinate bot networks to amplify them, and target the data sources that trading systems rely on. Normally, it takes hundreds of millions to move markets, but not in this case.

Protection

There are existing safeguards that can help mitigate these risks, like trading halts or AI-driven fraud detection. Traditional financial markets have mechanisms to halt trading during extreme volatility.

However, these frameworks were built with human behavior in mind and often fail to account for automated systems. As crypto markets operate 24/7 with fewer trading halts, there are a lot more opportunities for attacks.

Others suggest AI will eventually learn to detect manipulation. But research from HEC Paris notes that AI excels at short-term pattern recognition but fails at long-term contextual understanding.

When multiple AI agents rely on similar models and react to identical signals, they tend to make the same decisions at the same time. If those signals are wrong, the mistake spreads across the market, and at the speed of modern trading, that can quickly turn into a wave of synchronized selling.

As with much in AI, keeping a human in the loop may be the most effective safeguard.

The human layer in trading – analysts, compliance officers, and risk managers – shouldn’t disappear but evolve. Their role should be to question information, verify whether news is real, assess where data comes from, and apply judgment that AI lacks.

It may seem like friction to have humans involved. But in a system where speed is the vulnerability, friction is the point.

## What this means for industry players

For founders and investors operating in the crypto trading space, they shouldn’t treat the manipulation of agents as a theoretical risk.

The founders building AI trading infrastructure must position resilience as a value proposition. If they can build systems that can withstand poisoned data, use diverse data sources, and create transparent AI decision pathways, their solutions will stand out.

Meanwhile, investors backing such platforms should look closely at their “human-in-the-loop” protocols. Does the startup rely on fully autonomous execution, or is there mandatory human oversight for critical decisions?

The latter is a safer bet, as the risk of liability in a flash crash scenario driven by an agent’s error is massive.

The convergence of AI and financial products in both crypto and traditional finance is inevitable, but its trajectory is not predetermined. We can choose to build systems that are resilient, transparent, and human-centric, or we can sleepwalk into a future where a few lines of poisoned code cause huge losses.

The choice is ours, but the window for action is closing.

Source: https://www.techinasia.com/ai-trading-agents-trustworthy-data

Anndy Lian

www.anndy.com

j j j

April 19th, 2026

Binance and Bitget Announce to Investigate RaveDAO Token Trading

Binance and Bitget have both announced that they are going to investigate trading activity involving the RaveDAO token after concerns were raised about possible market manipulations.

In response to ZachXBT’s post, Bitget CEO Gracy Chen also confirmed the same direction, stating, “thanks for highlighting! We’ve started investigating into $RAVE.”

Shortly after, Binance co-CEO Richard Teng also responded directly to the issue, saying, “Thanks for flagging this with us @zachxbt. We’re looking into it. We will always do our part to investigate all market misconduct.”

How it all started

The issue became public when ZachXBT shared on X that “pump and dump activity for RAVE token originated on Bitget, Binance and Gate,” adding that insiders were controlling more than 90% of the supply.

He called on Binance co-founder He Yi and Bitget CEO Gracy Chen to carry out internal checks and remove those responsible from their platforms. He also placed a $10,000 bounty for whistleblowers who could provide proof of manipulation. Bitget later confirmed that an investigation into $RAVE had started.

ZachXBT shared early warning signals from on-chain data

ZachXBT explained that wallets linked to the RaveDAO project sent about 18.58 million RAVE tokens to Bitget before any price movement began. At that time, the token was trading under $0.50 and there was no public announcement about the transfer.

Roughly ten hours later, trading activity picked up sharply. At the same time, reports showed that about 74% of traders on Binance were holding short positions, meaning they were betting the price would fall.

Later, around 29.78 million tokens were pulled out from Bitget, which reduced the amount of tokens available for selling on the market. This shift in liquidity is said to have helped fuel a fast price surge, as short positions were squeezed out of the market.

The price moved from about $0.27 to over $14 within seven days, marking a rise of more than 5,500%. In a separate chart shared by ZachXBT, RAVE also showed a 10,383% increase over a 30-day period, highlighting the extreme volatility in trading activity.

ZachXBT said he had already contacted a RaveDAO co-founder before going public but received no response. In his words, “We cannot allow this blatant market manipulation by insiders controlling more than 90% RAVE support to further extract from retail investors.” He later pushed exchanges again to act quickly and investigate all linked accounts involved in the activity.

Insider control Allegations grow stronger

Blockchain analyst Anndy Lian also pointed to heavy token concentration. He stated that the top 10 wallets hold around 98.16% of total supply.

At the same time, the token structure also raised concerns. The fully diluted valuation was said to be around four times higher than the current market cap, a pattern often followed by large corrections in crypto markets. No public codebase or completed security audit has been released for the project, which added more questions around transparency.

RAVE is down 30% in 24 hours

Despite the concerns, RAVE continued to trade actively. At one point, its market capitalization reportedly surged to over $6.52 billion. The price also rose by about 44% on Saturday, reaching around $27.23 during early trading hours. However, it is now down by 30% to about $11.