CHICAGO — The scene is the trading floor at the Chicago Mercantile Exchange. Over $300 trillion in trade contracts flow through this clearinghouse each year. Since the exchange’s inception as the Chicago Butter and Egg Board in 1898, the action in “the pit” has become an iconic image of American capitalism in action: a raucous swarm of traders, elbowing their way forward through the human mass, shouting out trade positions, frantically waving hand signals to attract buyers or sellers. Only the biggest, strongest and loudest traders would make their way to the front of the pack and to the head of the industry.
But those were the old days.
Today, the deafening roar of Buy! and Sell! is being replaced by a more subtle sound: Click
This change has come about thanks to the work of a new breed of traders. These are the “quants,” the financial engineers. What they lack in spunk and swagger, they make up for in derivatives and stochastic calculus.
“The transition from ‘open outcry’ floor-based trading to electronic trading has created demand for a completely different skill set,” said Cristhian Dick, a financial engineer. “In the good old days, firms looked for football players that were 6–foot-7. Now they look for kids out of college with physics and computer science degrees.”
Steven Pollock, the most recent head of the University of Michigan’s financial engineering, or FE, program jokes that FE is what all graduate finance professors would like to teach their MBA students, if only the students could comprehend the math.
“It used to be that the financial engineers were the people with thick glasses that you stuck in the back room and told them to crunch numbers. Now, having a high level of mathematical ability and technological ability is almost a prerequisite for getting into the industry, it seems,” said Grand Rapids native Ben Van Vliet, associate director of the Center for Financial Markets at Illinois Institute of Technology.
That conclusion led Dick to pursue a graduate degree in IIT’s financial engineering program. After several years of working in and around Chicago commodities exchanges, the 1999 Kalamazoo College graduate realized he needed to go back to school. He chose the academically challenging FE program instead of a traditional MBA, based strictly on the numbers — or more precisely, the digits.
“When you are trading on the floor,” he said, “it takes some time to move to the front of the pit to get orders from brokers. My feeling was that by the time I got to the front, the marketplace would have gone electronic.”
Dick is now helping to make that transition. He designs and programs automated trading systems. These systems not only replace the mechanical exchange of information necessary to perform a trade, they also choose the right trades.
But as the markets become more automated, volatility decreases. Profitability goes along with it. Van Vliet has seen drastic changes in the last decade.
“It’s had a huge impact on the market already,” he said. “It used to be that people would go in and buy because they thought the market was going up, or sell because they thought the market was going down. We call that directional trading. It used to be that people could make money doing directional trading. Nobody’s trading directional trades anymore. There’s not enough volatility in stocks to make any money. The markets are too efficient. And in large part, I believe, it’s due to development of automated trading systems.”
In today’s markets, especially in options and futures, making the right trade is all about relationships — statistical relationships.
Financial engineers feed market data into proprietary software that attempts to find relationships between the numbers. Then, based on an established relationship, the software executes trades. Van Vliet said this type of trading has very little to do with company valuation; it’s strictly a numbers game.
“The longer you hold, the more the fundamentals — the underlying nature of the stock, or the underlying factors that affect the price of wheat, or oil, or an index — the longer you hold it, the more susceptible you are to fundamental factors affecting the price of your investment,” he said.
Options traders tend to hold on to investments for very short periods of time.
“I build systems that will look for an opportunity and get in and out of a trade in a second or two,” Van Vliet said. “A lot of these quantitative systems are looking for short-term inefficiencies: a couple minutes, a couple seconds, maybe instantaneous.”
But even those inefficiencies are becoming harder to find, so the programs must become more sophisticated. So must the programmers.
Dick said that advanced ability in mathematics and computer programming can only take a financial engineer so far. A fundamental understanding of the financial markets is mandatory for success.
“Finding the balance between being a trader and a quant creates a lot of opportunity,” he said. “So it is actually a very creative field. It is not just about knowing how to value an asset, but rather a field of ideas.”
Van Vliet agrees that the human side of market analysis remains important.
“What you can’t teach a computer to do is to read a news story that says, ‘The plane hit the building.’ I mean the computer can’t read that and make a determination as to what to do with that kind of information. So you’re stuck with what’s quantifiable or what data you can get from the market.”
However, armed with the most sophisticated derivatives and the most knowledgeable financial engineers, there’s still no way to take the risk out of financial markets.
“It is important to always keep in mind that the market cannot be completely modeled or thought to be completely efficient all the time,” Dick said. “If everyone is assuming the same thing, then these assumptions will fail.”
Those failures lead to bad things.
The story of Long-Term Capital Management (LTMC) has become a cautionary tale for financial engineers. The book “When Genius Failed,” by Roger Lowenstein, recounts the tale of LTMC, formed around a brain trust of finance’s brightest minds (including Nobel laureate Myron Scholes). The hedge fund turned an impressive $1.25 billion in start-up capital into $160 billion in gross assets within three years. The fund had not only the smartest financial minds in the business, it also had the most advanced computer models. It was making money by using techniques no one had ever used before.
A year later the fund had only $600 million in equity remaining and was teetering on the edge of collapse. The Federal Reserve orchestrated a $3.5 billion bailout because it feared that the domino effect from a default by LTCM could spell disaster for the country’s financial markets as a whole.
The fall of LTCM was triggered by the 1998 devaluation of the Russian ruble. The computers never saw that one coming.