“finance”

“How many think spreads will widen?” she asked.

The hands of about half the smartest people on Wall Street shot up.

“And how many think they’ll narrow?”

The other half—equally smart—raised their hands.

“Well,” she said. “That’s what makes a market.”

If they didn’t know, nobody could.

..it is hard to overstate the effect that securitisation has had on financial markets. Until the early 1980s, finance hewed to an “originate and hold” model. Banks generally held loans on their balance sheets to maturity; some debts were sold on loan-by-loan, but this market was small and lumpy. This began to give way to an “originate and distribute” model after America’s government-sponsored mortgage giants issued the first bonds with payments tied to the cash flows from large pools of loans.

Wall Street built on this innovation, and securitisation took off soon after, then paused before exploding in the 1990s.. It was given a lift by America’s savings-and-loan crisis, which encouraged mortgage lenders to jettison their riskier loans, and by new technologies, such as credit-scoring, that facilitated loan-pooling. Around 56% of America’s outstanding residential mortgages were packaged in this way, including more than two-thirds of the subprime loans issued in 2006. Thanks largely to securitisation, global private-debt securities are now far bigger than stockmarkets.

“As long as you have some history and some quantifiable data from past experiences,” Mr. Snijders claims, a simple formula will soon outperform a professional’s decision-making skills.

Something researchers have known for decades: that mathematical models generally make more accurate predictions than humans do. Studies have shown that models can better predict, for example, the success or failure of a business start-up, the likelihood of recidivism and parole violation, and future performance in graduate school.

They also trump humans at making various medical diagnoses, picking the winning dogs at the racetrack and competing in online auctions. Computer-based decision-making has also grown increasingly popular in credit scoring, the insurance industry and some corners of Wall Street.

The algorithms behind so-called quant funds, he said, act with ” much greater depth of data than the human mind can. They can encapsulate experience that managers may not have.”

Other cherished decision aids, like meeting in person and poring over dossiers, are of equally dubious value when it comes to making more accurate choices, some studies have found, with face-to-face interviews actually degrading the quality of an eventual decision.

“People’s overconfidence in their ability to read someone in a half-an-hour interview is quite astounding,” said Michael A. Bishop, an associate professor of philosophy at Northern Illinois University who studies the social implications of these models.

Max H. Bazerman, a professor at Harvard Business School, wonders how many managerial decisions can actually be modeled. “The vast majority of decisions that we make in professional life don’t have this quality,” he said.

He agrees that models can make better decisions about credit card applications and college admissions, he said, “but there are many decisions that are much more unique, where that database doesn’t exist. I’m as skeptical about human intuition as these folks, but it’s not only a computer model that we replace it with. Sometimes it’s thinking more clearly.”

Many in the field of computer-assisted decision-making still refer to the debacle of Long Term Capital Management, a highflying hedge fund that counted several Nobel laureates among its founders. Its algorithms initially mastered the obscure worlds of arbitrage and derivatives with remarkable skill, until the devaluation of the Russian ruble in 1998 sent the fund into a tailspin.

Mark E. Nissen, a professor at the Naval Postgraduate School in Monterey, Calif., who has been studying computer-vs.-human procurement, sees a fundamental shift under way, with humans becoming increasingly peripheral in making routine decisions, concentrating instead on designing ever-better models.

By making smart use of computer models’ advantages, ” you’ll become like the crafty A student who doesn’t work that hard but gets mostly right answers, rather than the really hard-working student who gets lots of wrong answers and as a result gets C’s.”