Caltech Economist Richard Roll Wins Onassis Prize in Finance

Richard Roll, the Linde Institute Professor of Finance at Caltech, has been named one of two recipients of this year's Onassis Prize in Finance. Awarded only once every three years, the Onassis Prizes honor the contributions of top thinkers in the fields of finance, international trade, and shipping.

"As the Linde Professor of Finance, Richard Roll anchors a revitalized program in finance at Caltech that is now making great strides in both original research and in improving the course offerings for both undergraduate and graduate students," says Jean-Laurent Rosenthal, chair of the Division of the Humanities and Social Sciences at Caltech. "It is wonderful to see him recognized with this prestigious prize."

The Onassis Prizes are awarded jointly by the Alexander S. Onassis Public Benefit Foundation and the Cass Business School, part of City University London. Roll shares this year's award for finance with Stewart Myers, an economics professor at MIT.

"The Onassis Prize in Finance has been awarded previously to Nobel laureate Eugene Fama, Caltech trustee and Deutsche Bank Prize recipient Stephen Ross, along with my esteemed corecipient this year, Stewart Myers. I am humbled and deeply honored to be included in such company," says Roll.

In a statement, the Cass Business School and Onassis Foundation noted, "This year's winners have made foundational contributions to finance since the beginning of its transformation to a rigorous science-based discipline, nearly a half century ago: Stewart Myers in corporate finance and Richard Roll in capital markets."

Roll is known for his work on portfolio theory—the design of optimal investment portfolios—and asset pricing. His most widely cited paper has come to be known as "Roll's Critique." That work cast into doubt empirical tests of the capital asset pricing model, then a premier model of risk and return. Roll has also collaborated with Caltech alumnus and trustee Stephen Ross (BS '65) to test Ross's alternative model of risk and return known as the arbitrage pricing theory. Roll continues to work in this area; in his most recent working paper, he proposes a new way to test the most prominent modern asset pricing theory.

Roll joined the faculty at Caltech in 2014 after spending nearly 40 years at the UCLA Anderson School of Management. He has also held faculty positions at Carnegie-Mellon University, the European Institute for Advanced Study of Management in Brussels, and the French business school Hautes Études Commerciales near Paris.

He earned his undergraduate degree in aeronautical engineering from Auburn University. He then was employed by Boeing, where he worked on the 727 and wrote the operating manual for the first stage booster of the Saturn moon rocket, while earning his MBA at the University of Washington. Realizing that he was more interested in business than engineering, he quit his job and completed a PhD in economics, finance, and statistics at the University of Chicago. 

In addition to his academic positions, Roll has founded several companies and has served as a consultant to numerous governmental agencies and corporations. He was a vice president of Goldman, Sachs & Co. from 1985 until 1987. He also served as president of the American Finance Association in 1987.

Roll has published more than 100 peer-reviewed articles and has won Graham and Dodd Awards for financial writing four times. Among other achievements, Roll has won the Leo Melamed Award for outstanding scholarship by a business school professor (1990), the Roger F. Murray Prize from the Institute for Quantitative Research in Finance (2001), and the Nicholas Molodovsky Award from the Association for Investment Management Research (2002), and was named "Financial Engineer of the Year for 2009" by the International Association of Financial Engineers. He is also a fellow of the Econometric Society.

The winners of the Onassis Prizes were announced by Alderman Alan Yarrow, the Lord Mayor of London, at Mansion House in London on March 20. Roll and the other 2015 honorees will receive their awards at a ceremony in September.

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The Secret Life of a Snowflake

How the Brain Learns from the Past and Makes Good Decisions for the Future: A Tour of Neural Reinforcement Learning

Watson Lecture Preview

It is often said that people who do not learn from history are doomed to repeat it. Not being one of those people requires a network of different brain regions to work in concert. On Wednesday, February 4 at 8 p.m. in Caltech's Beckman Auditorium, John P. O'Doherty, professor of psychology and director of the Caltech Brain Imaging Center, will discuss our current understanding of how we learn from experience. Admission is free.

 

Q: What do you do?

A: I study how we learn from experience. Humans and other animals have to make decisions all the time to maximize their benefits and minimize danger. These decisions range from what I should have for dinner or should I cross the road—which could have life-changing consequences if I'm wrong—to the selection of a life partner. I don't claim that "Who should I marry?" is equivalent to "Carrots or Brussels sprouts?" but we do think that many decisions share certain commonalities. So we look at very simple tasks that give us a window into how the brain solves problems to maximize future rewards.

We study brain activity by putting your head in an fMRI scanner. "MRI" stands for magnetic resonance imaging, and you've probably had one if you've had a sports injury. The "f" stands for "functional," and an fMRI scan detects changes in the oxygenation levels in the blood. If a certain part of the brain is active, its oxygen supply increases. We map those increases onto the brain's anatomy in 3-D while our volunteers perform some task that involves learning.

A task might be playing virtual slot machines. You have a choice of three machines, and we tell you one machine pays better than the others. So you choose one, press the button, and get instant feedback—you win or you lose. As you try to work out which machine is better, we monitor the patterns of activity in various parts of your brain. One of our goals is to find the part of the brain that represents the experienced value of the things we meet in the world—how good it feels to win, or how bad to lose.

We're also interested in how the brain changes its expectations. As you play the machines, you're constantly revising your estimate of which machine is better. We have computational models that we think represent how the brain internalizes feedback, and we're trying to find brain areas where the activity matches those models.

We think that understanding the neural circuits and computations that underpin our decision-making capacity may shed some light on certain psychiatric disorders, such as obsessive-compulsive disorder, depression, and addiction. On some level, all of these can be seen as decision-making gone wrong. Addiction, for example, involves a choice—voluntary or otherwise—to engage in a certain pattern of behavior.

 

Q: Setting aside clinical disorders, why do people make garden-variety bad decisions? What leads us to cross a busy road and almost not make it?

A: First, it's important to emphasize that humans are collectively pretty good at making decisions. That's why we've been so successful as a species. But there could be all sorts of reasons why an individual might make a poor decision. For example, you might underestimate how fast the traffic is moving.

My lab is particularly interested in how two distinct decision-making mechanisms may interact to produce bad outcomes. One mechanism is "goal-directed," in which you evaluate the consequences of your action in light of the goal you're pursuing. This requires a lot of mental energy. In contrast, "habit-controlled" decision-making is basically stimulus-response—you react to some cue from the environment. Habits can be very beneficial, because you can execute them quickly without thinking deeply. Once you learn to ride a bicycle, for example, you don't have to concentrate on keeping your balance. It becomes routine, and you can focus your mental energy on other things. Poor decisions can result when the habit system drives your behavior when you really should be solving things in a goal-directed manner. This may be how addiction becomes compulsive. The goal-directed system says, "I don't want to take this drug any more," but the habitual system overrides it.

 

Q: How did you get into this line of work?

A: Even as a kid I was interested in science and its unsolved mysteries. I was actually keen on astronomy as a teenager and really considered going in that direction. Then I started getting interested in how computers work, which led me to start wondering about how the most complex computer that we know of works, namely our brain. So I basically had a career choice between studying the universe or studying the brain, which are probably the world's two greatest outstanding mysteries. I decided to take my chances on the brain.

At the time, the field of cognitive neuroscience was based on the paradigm that the brain is like a digital computer, and brain processes were modeled in essentially in the same way. There were lots of studies of memory, such as recalling lists of words, but very little was known about how the brain assigns a greater value to some things than others. But it's a really fundamental question, because the ability to work out whether something is good or bad—and to maximize behaviors that lead to good things and avoid bad things—is critical for survival. Digital computers typically don't make value judgments of that sort unless they are programmed to do so. So that's what excited me, trying to unlock how it is that the brain assigns value to things in the world.

 

Named for the late Caltech professor Earnest C. Watson, who founded the series in 1922, the Watson Lectures present Caltech and JPL researchers describing their work to the public. Many past Watson Lectures are available online at Caltech's iTunes U site.

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Learning From Experience — How Do We Do It?
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