By Thomas R. Keene and Shannon D. Harrington
Sept. 21 (Bloomberg) -- Financial markets have grown too dependent on mathematicians who use models to anticipate price moves and need to start injecting "common sense" into the equation, said Paul Wilmott, a London-based author and quantitative finance instructor.
Wilmott has warned that so-called quants who use mathematics to forecast how markets will behave can overlook errors in the models, leading to flawed predictions. In a New York Times column July 28, Wilmott also said so-called high- frequency trading, where hedge funds and other firms use advanced computers to buy and sell thousands of shares a second, threatens to destabilize the market.
"There is too much mathematics in this business," Wilmott, author of "Paul Wilmott on Quantitative Finance," said in a Bloomberg Radio interview. "I just want people to stop and think for once. People just rush into these things without any thought for what the consequences might be."
Wilmott is the co-founder of the Certificate in Quantitative Finance, a six-month program founded in 2003 that stresses the "practical" application of math in finance and admitted 195 students in January.
"We explain to people how to think for themselves," said Wilmott, who also founded the Diploma in Mathematical Finance at Oxford University, according to his Web site. "People don't really question those assumptions enough. If the assumptions are wrong, then obviously the models and what follows can be wrong as well."
Reliance on computer models and trading using algorithmic formulas also has led to a shift in the types of people hired by Wall Street, he said.
"You go back 20 years, and people running finance, they were maybe history graduates," Wilmott said. Now, much of the industry is run by mathematicians, he said. "A lot of mathematicians do not have that common sense that the old guard had," he said.
To contact the reporter on this story: Thomas R. Keene in New York tkeene@bloomberg.net; Shannon D. Harrington in New York at sharrington6@bloomberg.net.
Last Updated: September 21, 2009 15:39 EDT
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