Bursa Malaysia reprimands, strikes off TA Securities dealer
http://www.thesundaily.my/news/1137953
That's a question that cuts to the heart of the vulnerability exposed in this case. Based on the details, it appears it was surprisingly easy for Oh to manipulate multiple counters simultaneously, due to a "perfect storm" of factors that existed at the time.
Here’s why it was likely easier than one might think:
1. Nature of the Counters: Small, Illiquid Stocks
The six counters (Biosis, Metronic, Ariantec, Luster, Harvest Court, Naim Indah) were almost certainly small-cap or micro-cap stocks with low liquidity (thin trading volume).
Why this matters: In an illiquid stock, a relatively small amount of money can move the price significantly. Placing a few large orders can set the price, and creating even a modest volume of artificial trades can make it look "active." Manipulating a highly liquid blue-chip stock like Maybank would require billions and would be nearly impossible for one dealer.
2. The Mechanism: Married Trades and Cross-Trades
Oh’s primary tools—Married DBTs and matched cross-trades—are executed within the same brokerage (TA Securities).
Internal Matching: He was essentially moving shares between his own pool of client accounts. This doesn't require finding an independent, outside buyer or seller on the open market. The trade is pre-arranged and then executed on the exchange, creating a legitimate-looking record but with no genuine transfer of risk or ownership intent.
Control Over Both Sides: By having authority or influence over the accounts involved (including family members), he controlled both the buy and sell orders. This made execution straightforward and predictable.
3. Lack of Effective Real-Time Surveillance (Circa 2014)
While Bursa Malaysia had surveillance systems, the sophistication in 2014 might not have been as advanced as today in detecting complex, multi-account relationship patterns in real-time.
Detection was likely post-trade: The manipulation occurred "over a period of several months," suggesting it was detected by analyzing historical patterns and linking accounts afterward, not stopped immediately.
4. Exploiting the Commissioned Dealer Role
As a commissioned dealer, his income depended on generating trading volume. This provided both motive and a veil.
Volume as Cover: High volume, even if artificial, could be rationalized as being "for the clients" and generating commissions for the firm. It didn't necessarily raise immediate red flags internally if it was profitable for the branch.
Client Trust/Family Links: Using accounts of family members and compliant clients removed a major obstacle—client suspicion. The clients in on the scheme were beneficiaries; others may have trusted him blindly.
5. The "One-to-Many" Model
Oh didn’t need to manipulate each stock in isolation with a separate scheme. The case describes a hub-and-spoke model:
Oh was the hub. He used the same pool of interconnected client accounts to execute similar manipulative patterns across different counters. The technique (married DBTs, cross-trades) was identical; only the stock ticker changed. Once the method was established, applying it to a sixth stock was just incremental work.
6. Potential Lack of Rigorous Internal Controls
The fact that this went on for months raises questions about TA Securities' internal compliance and supervisory oversight at the time.
Dealer Discretion: He may have had excessive discretion to execute complex off-market (DBT) transactions without immediate supervisory approval.
Focus on Revenue vs. Risk: A high-commission producer might have been subject to less scrutiny.
Conclusion: It Was Relatively Easy Within That Specific Ecosystem
It was not easy in the sense of being simple or risk-free, but it was highly feasible given the confluence of:
The Target: Illiquid, low-priced stocks.
The Method: Internal trades he fully controlled.
The Position: Direct access to the trading system as a dealer.
The Motive: Commission and client favor.
The Weakness: Likely lagging surveillance on relationship-based trading and inadequate internal controls.
In today's environment, such a scheme would be significantly harder to sustain for months due to:
Advanced AI-driven surveillance that maps relationships and detects manipulative patterns in real-time.
Tighter regulations on DBTs and conflicts of interest.
Stronger internal compliance mandates at brokerages.
The case serves as a historical lesson on why regulatory technology (RegTech) and a strong compliance culture are essential to close these systemic loopholes.
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