Elaine Wah

Modern Markets, Modern Metrics - A Blog By IEX

In this blog by IEX's Elaine Wah, the newest public exchange looks to refute public claims that the metrics it uses are designed to inflate its own volume numbers and mislead people.

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August 31, 2004

Where's the Real Beef?

By Nina Mehta

Nhan Bui is one frustrated trader when she's looking for depth. "When I'm not able to see the depth of the market," she says. "I resort to crossing networks to find naturals."

Bui, who heads the U.S. equities trading desk at First Quadrant, also notes that penny increments and intraday volatility prevent traders from executing block trades even in liquid stocks. "To get just 10,000 shares off is difficult, because nobody's willing to show their hand," Bui says. "And with everybody using VWAP [volume-weighted average price] engines, orders are sliced into small trades."

The result: a tougher trading environment with higher execution costs for both Nasdaq and listed stocks.

First Quadrant, a Pasadena-based quantitative investment management firm, has $18 billion under management, which includes $5.5 billion in U.S. equities. Bui has traded equities at First Quadrant for the last 10 years.

In Bui's view, the reason for the rise in algorithmic trading is because buyside traders are "afraid to commit." For example, she points out that a stock could easily move 3 percent or 4 percent in a day. "If a trader makes a market call, he could get burned because volatility has increased," Bui says. "So to protect themselves, traders commit a part of their order to VWAP engines to hedge their position, and make a call on some of it."

First Quadrant is model-driven. The models generate a list of buys and sells every day. Client accounts are also rebalanced daily, although they're not traded every day. "We try to limit turnover to 100 percent a year because of the transaction costs," Bui says. "So we don't trade every day for every strategy, unless we have a change in our model."

Bui works with another senior trader and two junior traders to implement the day's orders. She works the larger, more institution-sized orders. Everything else is handled by her colleagues.

"We have a 5,000-share cutoff," Bui explains. "Anything greater than 5,000 shares or 5 percent of the 30-day average daily volume - whichever is greater - I do." Bui and her traders dole out trades to brokers. First Quadrant's desk uses ECNs for more liquid stocks. The desk also trades algorithmically through VWAP engines for smaller, "clean-up orders" which are orders that range from 5,000 to 15,000 shares, says Bui. Since one of the desk's benchmarks is VWAP, the algorithmic trading engines make achieving that easier and simplify the workflow at the same time.

Brokers are selected based on their execution history. First Quadrant keeps an in-house broker list and gets a quarterly evaluation of broker execution quality from the data analysis firm, Elkins/McSherry. "I call it the report card," says Bui. "Every quarter I tell my brokers where they rank. And if they don't do well, I put them in the penalty box." First Quadrant has records on execution quality dating back about 10 years. On Reg NMS, Bui is quick with her recommendations. The Intermarket Trading System should be improved; ECN access fees should be eliminated and subpenny trading prohibited, she says. Moreover, there should be a much more hybrid system with NYSE specialists and an automated fill for much larger orders, according to Bui.

"When you see a quote, you often can't get to it, and once you're there, there's no volume," she says of the NYSE. "We should also go to a nickel spread environment since there's more at risk with a nickel than a penny for someone who wants to step ahead."