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Women in Trading 2007 : Armed and Dangerous

Revisiting the subject of the preeminent women in trading, we’ve cast an eye toward technology, shining a spotlight on the females leading the industry's march into the twenty-first century.
By: Leah McGrath Goodman
November 2007 , Page 67

In the arms race among traders, algorithms are the bomb. These formulas for telling machines what to do can vary from the simple — benchmarking trades according to VWAP — to the complex, such as overlaying multiple strategies into one amalgamated cluster strike.

One of the firms leading this campaign for computer-leveraged stealth and speed is TradeTrek Securities, a Newark, New Jersey–based independent provider of algorithms and quantitative execution services. Its clients are mainly institutional money managers and hedge funds with billions under management and intense pressure to perform. TradeTrek’s eight-person staff includes a trio of West Point graduates, two of them former Army Rangers. The firm even borrowed from military lingo for the names of some of its custom algos — “Fire 2.1,” for example, inspired by “heat,” military jargon for heavy artillery — a radically advanced strategy so adaptive that the computer can probe liquidity pools, not to mention adjust to markets as they move, in real time.

Although she doesn’t have a military background, TradeTrek’s Deborah Kase helps command this techno team. The 54-year-old former assistant to George Soros is waging an all-out offensive on electronic execution in ways not possible even a few years ago.

“We’ve created about a dozen different algos, some of which are fusions of two or three especially robust algos,” Kase explains. “The idea is to offer execution that dynamically adjusts to pricing and liquidity availability across fragmented markets.”

As a woman on the front lines of the trading revolution, Kase is hardly alone. From building computer models from scratch to navigating perilous dark pools to designing hybrid algorithms designed to handily slay any market, such women increasingly populate the ranks of the quantitative traders and Ph.D.’s leading Wall Street into the twenty-first century.

For Mary McDermott-Holland, being at the forefront of the trading landscape’s transformation over the past two decades has felt like a warp-speed jaunt from the first century to present day. “We used to do everything on paper,” says the 46-year-old head trader at Franklin Portfolio Associates in Boston. She can still remember the mid-’80s, when she kept track of hundreds of daily trades by writing them down. Back then, reams of paperwork bogged down trading. Watching markets often took a back seat to simply contending with a battery of mind-numbing administrative tasks. “We would have trade tickets all over our desks,” McDermott-Holland recalls. “It was a mess. If we had extra work to do, we would have to throw bodies at it. Now we just throw technology at it.”

A veteran of the industry with a quarter-century’s worth of experience, McDermott-Holland is the sole female on the five-person team that built Franklin, known as one of the first quantitative-investment firms in the nation. It now manages some $35 billion. McDermott-Holland started there as a secretary.

Such a dramatic rise is not uncommon in a world where the truly talented are rewarded for their daring and skill. And with the floor in full retreat, trading is no longer the physical job it was 30, 20 or even 10 years ago. The ranks of female traders, generally speaking, are, however, still relatively thin. Similarly, the ranks of quants are populated mostly by men. “Of the 7,000 quants we have in our global database, only about 3 percent are women,” says Dominic Connor, director of Paul & Dominic Quantitative Recruitment in London. But that percentage is going up. “Based on the number of females we’ve seen coming into the market in the past quarter, I predict that around 16 percent of quants entering the field will be women between now and year’s end,” he says.

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Theirs is a well-timed invasion, as algo experts — male or female — have never been in greater demand. Manning the motherboard in this rise of the machines is yet another Soros-bred talent, Maria Vassalou. Those who know the 40-year-old Ph.D., the head of quantitative strategies at Soros Fund Management, are awed by her skills. Born in Athens, Greece, Vassalou spent 11 years as a finance professor at Columbia. Soros plucked her from academia in 2005.

Mary McDonnell, chief executive of Geneva Trading Chicago, and Marsha Lipton, head of quant research at JPMorgan, are also heralded as trading-technology bona fides. McDonnell, 51, cemented her reputation in 2000, when she was literally spat upon by pit traders at the Chicago Board of Trade for having had the gall to introduce electronic trading to the floor — only to hang tough and be proven, in the end, utterly prescient. Today, McDonnell runs one of Chicago’s largest proprietary-trading shops.

Meanwhile, Lipton, an all-star trader who holds both a doctorate in chemical physics and an MBA from the University of Chicago, has etched a remarkable career. The Russia-born quant czar honed her technology skills at Bankers Trust (later acquired by Deutsche Bank), then at DLJ (later acquired by Credit Suisse). She left DLJ in 2000 and soon cofounded Thor Capital Management. She subsequently landed a job at JPMorgan in New York, where she wielded considerable influence over the global markets, trading commodities, currency, fixed-income and equity indices. Now 40 years old, she recently left to explore other opportunities, and may launch a hedge fund. “Intellectual stimulation,” she says when asked why she so aggressively embraces quantitative techniques. “To me, there is no other way to make money.”

Such success and prowess blast away stereotypes about men having an inherent advantage in math skills. In fact, many industry observers say, women seem to bring certain crucial attributes to the trading table, among them multitasking ability and unerring patience, both vital traits when designing cutting-edge systems and strategies. Indeed, according to Hedge Fund Research, women excel at managing money, period. The Chicago-based fund tracker recently launched a Diversity Index, back-calculated to 2003. Hedge funds run by women (and minorities), it found, have an average annualized return of 10.5 percent, net of fees, since 2003, compared with the HFRX Global Index’s average annualized return of 6.5 percent over the same period.

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Quantitative Recruitment’s Connor says he recently witnessed a bidding war between two prominent investment banks over a woman with experience in developing trading strategies using digital-signal processing technology, a method of predicting market moves by using computers to search for specific drops of key information in an ocean of data. In a world where a trader’s Holy Grail is being able to figure out, before the next person does, what is likely to transpire in the markets, digital-signal processing technology offers nearly limitless, if not yet fully realized, opportunities for snaring big payouts.

“Nowadays, these types of fights over quants — especially women — are common,” Connor says.

Despite the criticism directed at quant methods and computer models in the wake of this summer’s global credit crisis — when modeling techniques led to massive, counterintuitive sell-offs and bafflingly irrational volatility — the case for widespread use of trading technology remains solid. Surely no one would choose to go back to the way things were in the mid-’80s. As the dust settles, though, it has become abundantly clear that any shortcomings in the computer models fine-tuned for trading are the fault of the humans, not the machines.

“Any and all technology is simply a reflection of human designs,” says Wendy Rudd, chief executive of TriAct Canada Marketplace and former head of equity-market development for the Toronto Stock Exchange. She should know — in addition to leading a number of trading-technology projects over her career, Rudd, 44, built her Toronto-based company’s Canadian crossing network two years ago. This network lets traders circumvent public markets in search of better stock prices. TriAct’s owner, Investment Technology Group, provides access to such alternative-trading systems — also known as “dark pools” — through a variety of algorithms and tools.

“Computer models simply embody what humans are trying to achieve, so they only go as far as what the people who designed them have thought of,” says Rudd, who studied mathematics and computer science before earning her MBA from Wilfrid Laurier University in Waterloo, Ontario. This disconnect might explain how Wall Street somehow experienced several trading days in August that, according to computer models, were statistical aberrations.

Such anomalies are certainly cause for concern, Rudd says, but not a reason to abandon the master plan. “It’s times like this, when we have a market meltdown, that we learn the limitations of our technology and where we need to go back to install different checks and balances,” she says.

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Trading technology may not be without its flaws, in other words, but like women on the trading floor, its staying power is undeniable.

Ruth Colagiuri, 34, a product manager for algorithms and crossing products at Merrill Lynch in New York, has helped build the bank’s electronic-trading business since its inception in 2003 and played a key role in the development of its 20 algorithms, some of which offer nearly unlimited customization of trades for institutional clients. The aim is to give traders the tools they need to hunt down liquidity in a global market that is rapidly dispersing into increasingly smaller, more diffuse trading pools.

Colagiuri, a native Australian who studied electrical engineering at the University of Sydney, competed as a national-level gymnast before embarking on a career in finance. “When you spend enough time on the desk talking to traders, you start to learn their lingo, how they think,” she says. “You watch how they trade and eventually realize how to translate that into an algorithm that is going to behave.”

This year, Colagiuri’s team launched the Merrill Lynch Crossing Network, which is targeting some 400 million to 500 million shares a day by combining the bank’s internal trade flows with those of its external clients, creating what is known as “resident liquidity,” increasingly bypassing public markets such as the New York Stock Exchange and Nasdaq. The rationale is simple: “We consider it our obligation to get our customers’ orders through our internal marketplace at a price that’s the same or better than the one you’ll get in the public markets,” she says.

Claudia Williams, 54, director of trading for the $115 billion, Austin-based pension-fund manager Teacher Retirement System of Texas, considers an array of options when executing trades. A big player (her trades run anywhere from 25,000 to 1 million shares), she runs a greater risk than most of being copied or preyed upon.

“We also need the ability to combine information with instinct,” says Williams, who has been trading for 25 years. “Both execution and technology expertise are required to provide liquidity management. The busier the desk, the more tools are needed.”

Such is the demand for advanced algos capable of finding the sweet spots amid a wealth of crossing networks acting as alternatives to the public markets. Traders estimate there are now as many as 50 dark pools in North America alone. That said, Jennifer Setzenfand, 34, a senior trader at $260 billion–in-assets Federated Investors, insists she’s ready to approach the next frontier: algos that mimic human judgment when making decisions about timing trades.

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“I’ve tried every algorithm in the world, but it doesn’t really help with a large-cap stock,” she says. “You’re not looking for liquidity; you have liquidity. What you need is the right price, and an algorithm doesn’t do it. I mean, if I put my whole size into the algo, the algo’s not smart enough to know not to do that trade if the liquidity’s there. By contrast, any experienced trader is going to know to wait if the price looks like it might eventually come her way.”

While the emergence of dark pools this year has brought forth a large number of liquidity-seeking algos, Setzenfand identifies a starting point for the next algo generation. “I think the demand is there for algorithms that can read the market more intuitively,” she says. “We’re only beginning to figure out how we might take all the information that’s in our traders’ heads and put that into the algos.” Says TradeTrek’s Kase: “Algos are not finite; they’re evolving. It’s been only about seven or eight years since we’ve had them, and I think every desk is already on its fifth generation.”

nowing how to use your weapon of choice — not just having it at your disposal — is a key to trouncing your market, says Lisa Utasi, 44, who as a senior equity trader for Legg Mason’s ClearBridge Advisors manages portfolios worth $20 billion. “I have certain weapons in my holster that I think are much better than others. It’s all about knowledge of what each tool does and when it works best,” she says. “People who have that are the most successful. When people use algos the wrong way, they get taken advantage of all day.”

Laila Kollmorgen, 35, head of trading of asset-backed securities and collateralized debt obligations at BNP Paribas in London, says her secret weapon is a network of OTC databases and calculators she calibrates to project the value and behavior of a preponderance of sensitive assets in her $7 billion book. “Everything we do involves balancing statistics with different risk factors and assumptions,” she says. “Take a collateralized debt obligation, for example. You have to look at its interest-rate sensitivity — will it be sensitive to rates? Will it be sensitive to losses? What will its recovery rate be if there’s a problem? If a loan were to default, how much would you get back on it, and in how long? This is how we use technology to improve our performance.”

Michelle Behrend, 25, who manages a large book of credit default swaps for HSBC in New York, says that even in her voice-brokered environment, she relies on the bank’s proprietary trading system to tell her in real time how much money she stands to make or lose per basis point whenever her risk whipsaws. This is especially helpful, she says, during situations like the summer credit crunch. “It’s been an exciting, sometimes frustrating time,” she says. “During periods of extreme volatility, this system has alerted me to when I really need to go on defense to protect my gains.” That said, however, some traders are finding the sweeping changes that have come with the advent of new technology to be a little too much.

Jewel Ann Weiss, 59, who has been an independent cotton-futures trader at the New York Board of Trade since 1980, says that while she doesn’t mind screen trading — indeed, she has taken to it adeptly — she nonetheless plans to retire once floor trading goes all electronic. “Trading is like a puzzle to me,” she says, “and when the trades are on the screen, we can’t see the flow. We’re missing pieces of the puzzle. That may be better for some, but I’m used to synthesizing all the information I’m seeing down in the pits.”

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Like most traders, Elaine Garzarelli, 49, president of Garzarelli Capital in New York, sees it differently. Technology is not only useful for the execution of trades, she says, but also for helping determine which trades to make in the first place.

Garzarelli, a Ph.D. who planned to be a physician before diving into statistics and economics while attending Drexel University, used computer models to predict Black Monday in October 1987. (During the fallout from the crash, she raked in $50 million and garnered so much attention that she was asked to appear in a L’eggs pantyhose commercial.) She now uses similar models at her research and wealth-management firm, which caters to institutional investors.

“I have built all my models myself,” says Garzarelli, whose investments in S&P 500 futures and exchange-traded funds have outperformed their benchmarks by an average of 400 to 500 basis points a year. “The positive of technology and quantitative models is that they take the emotion out of analysis. Still, our models require good inputs, so one must be a superior student of the economy.”

The advent of ever-evolving technologies has certainly accelerated the pace at which both traders and clients need to adapt, says Julia Williams, 35, a trader and broker for Sucden UK in London. She isn’t sure technology will ever completely take over — there is, she notes, still a strong need for the human touch.

“I think it’s obvious that people will only be coming to rely more and more heavily on technology going into the future,” she says. “But even when they’re hooked up at home, our private clients will still call to talk about their trades. I have one farmer in the southwest countryside who’s been speculating on index options. What I don’t know about bovine tuberculosis is just not worth knowing.”


http://www.traderdaily.com/magazine/article/12219-2.html