Andrew Lo

December 5, 2011. I’m Chris Boebel. As part of the MIT150 Infinite
History project, we’re talking with Professor Andrew Lo. Professor Lo is the Harris &
Harris Group Professor of Finance at the MIT Sloan School
of Management and the director of MIT’s Laboratory
for Financial Engineering. His wide-ranging research
interests include financial asset pricing models, financial
engineering and risk management, trading technology,
computer algorithms and numerical
methods, financial visualization, hedge fund risk
and return dynamics and risk transparency, and evolutionary
and neurobiological models of individual risk preferences
in financial markets. His awards include– to
name just a few– the Alfred P. Sloan Foundation
Fellowship, the Paul A Samuelson Award, a Guggenheim
Fellowship, and multiple awards for teaching
excellence. He is a former governor of the
Boston Stock Exchange and currently a research associate
for the National Bureau of Economic Research, a member of
the NASD’s Economic Advisory Board, and founder and chief
scientific officer of Alphasimplex Group, LLC a
quantitative investment management company. Professor Lo received a BA in
economics from Yale University in 1980 and a PhD in economics
from Harvard in 1984. Professor Lo, thanks very
much for coming in to talk to us today. LO: Thanks for having me. INTERVIEWER: So let’s just
start at the beginning. Where were you born, and
where did you grow up? LO: I was born in Hong Kong. And shortly after, I moved to
Taiwan for about five years. And then, when I was five
years old, I came to the United States. And I grew up in
New York City. INTERVIEWER: Tell me a little
bit about that transition from a cultural perspective, that
educational perspective. LO: Well, it was a fantastic
experience in many ways. So I grew up in a single-parent household in New York. And my mother worked pretty hard
to put the three of the kids through school. We went through public schools
throughout, and the New York City public school systems are
among the best in the country. Certainly, they were at the
time, and so I feel I got a great education, and met some
really, really interesting people during my time there. INTERVIEWER: So you were an
urban kid for most of your– LO: I was. We lived in Queens, and I
commuted to the Bronx. I went to the Bronx High
School of Science. I’m very proud of that. I love that school, and I
learned a great deal from my classmates. It was a lot of fun. INTERVIEWER: So do you
have very early memories from Taiwan? Or does your consciousness
start in New York City? LO: No, we have some memories. I’ve got a number of things
that I remember well from those days: playing with
fireworks was one of the favorite activities in Taiwan,
but for the most part, my childhood was really
in New York. INTERVIEWER: So when did you
start developing an interest in economics, math? I’m sort of interested in your
entree to your field. Were there early signs? LO: Well, actually, starting
in high school. I had always been interested
in science, of course. In third grade, my third grade
teacher, Mrs. Barbara Ficalora, was wonderfully
supportive, and made me the class scientist. And so I got an early
introduction to doing experiments. It wasn’t until high school that
I became exposed to real serious scientific reasoning,
and also to the field of economics through a course that
I took in social studies, where we read Heilbroner’s
Worldly Philosophers. And that really changed my
thinking about the idea that you could apply interesting
mathematical principles to problems in economics. INTERVIEWER: Bronx Science is
obviously kind of a legendary high school. Can you talk a bit more about
your experience there? What were your career
ambitions at that point in your life? And what kinds of things
were you really studying in high school? LO: Well, for me, Bronx
Science was a really transformative experience,
because, up until then, the junior high school and
elementary school that I went to was really just local, kind
of community schools, where you had a wide mix of kids, some
of whom are interested in academics, but most of whom
were probably not. And so in that kind of
environment, to be doing well in school was to be a
bit of an outcast. It wasn’t until I got to Bronx
Science that it became cool to actually do well in school and
to be interested in academics. So for me, it was really
like an awakening. I had tremendous friends and
activities in Bronx Science that I really couldn’t have
access to in any of the schools that I went
to before that. Also, for me, it was a little
bit of an interesting experience in terms of the
mathematics at Bronx Science. Right around that time, Bronx
Science instituted– as all New York City
high schools– the so-called New Math. And if you know the history
of it, the New Math was an absolute disaster from the
perspective of the majority of the students. But for me, it was actually
transformative as well, because up until then, I had a
particular learning issue, a slight case of dyslexia
that we didn’t know until much later on. And so for the longest time,
I had difficulty with mathematical concepts,
multiplication, and really basic things that other kids
had no issues with. I had a hard time memorizing
the multiplication table. It wasn’t until I got to Bronx
Science that, because of the curriculum in mathematics– it was transformed from the
basic algebra, geometry, trigonometry to sets, rings,
fields, abstract algebra– that I turned from a C student
to an A student in math, so for me, that was really an
important experience. INTERVIEWER: That’s kind
of an amazing story. LO: I was one of the lucky ones
that benefited from the unfortunate aspects of the New
Math that was perpetrated on New York City high
school students. INTERVIEWER: At least
there was one. So you mentioned your
third grade teacher. Were there other mentors,
significant teachers, experiences you had in high
school or before that time that really pushed you in
a certain direction? LO: Oh, a number. One of the things that has
always struck me is how important teaching is, because a
good teacher can have such a positive influence on a
student for the rest of his or her life. And similarly, a bad teacher can
have tremendous negative consequences for that student. And so I’ve been very fortunate
in that, during the years, I’ve had some good
teachers, many good teachers, a few bad ones. So I have a good understanding
of what’s involved. And my third grade teacher,
Mrs. Ficalora, stands out. In high school, I had a
number of teachers. Bronx Science is filled with
really extraordinary faculty. In fact, we don’t think
of them as teachers. We think of them as faculty. Mrs. Mazen, my calculus
teacher. I learned more from her about
calculus than I think most college courses would teach
their students. So there are a number of very
talented instructors that I was very pleased and
lucky to have. INTERVIEWER: What were your
career aspirations at that point, just before college? LO: Well in high school, I think
that most of my friends and I were interested
in science and math. So at the time, my presumption
was that I would go into one of those disciplines. Being the youngest of three
children, and having an older brother and sister that were
also academically inclined made it relatively
easy for me. My brother is a mathematician
at the Jet Propulsion Lab at Caltech. And my sister is a biologist
at the University of Pittsburgh. So they both followed very
academic careers. And in my household, one had to
get a PhD just to measure up to the older siblings. So from high school on, I was
very much interested in following some kind of a career
path in academia, although my interests were
somewhat on the more applied side, as opposed to purely
theoretical kinds of issues. INTERVIEWER: You mentioned a
single-parent household. Was your mother at all
academically inclined? Was it your mother
that you were– LO: Yeah, my mother was very
much academically inclined, from the perspective
of what she valued. She felt that the life of a
scholar was among the most important and prestigious. She was a lawyer by training. But she had– as most, I think, Chinese
families did– a deep and abiding respect for academic
achievements. And so it was pretty clear, from
the kinds of things that she talked about and the values
that she held, that developing new knowledge was
really important to her and ultimately to all
of the children. INTERVIEWER: So at that point in
high school when you had to really start seriously thinking about the next steps– going to college, what
you might study– tell me about that
decision process. You ultimately went to Yale. And then talk a bit about
your experience there. LO: Well, I was a little
confused about what I wanted to do. My sister went to MIT
as an undergraduate. My brother went to Caltech. And so when I talked with my
mother about where I ought to go for college, she said, well,
maybe you ought to think a little bit more broadly about
the kind of things that you’re interested in. And so rather than pursuing a
somewhat more technical career path, I thought that maybe
applying to a general liberal arts college would
be a good idea. And I was thinking at that time
that I might want to do a combination of mathematics
and biology. I did a science project
as a senior. I was a Westinghouse Finalist. Intel, I guess, now is
what they call it. And I was very much steeped in
molecular biology at the time. And so Yale seemed to have a
good compromise in very strong humanities, but also very
good science programs. So I ultimately decided that
that would be, really, the best compromise. I visited the school,
and I was really enthralled with the campus. New Haven wasn’t so great. But Yale, itself,
was a wonderful physical space for students. And so it was a pretty easy
decision, after I had gone through and looked at all
the various different possibilities. INTERVIEWER: You mentioned the
Westinghouse Competition. It’s amazing how many of our
interviewees have a story about Westinghouse and
participating in that. LO: Well, it’s a wonderful
activity, and obviously not for everybody, but at Bronx
Science, most of the students that I interacted with
really got into it. And we learned so
much from it. I still remember, to this
day, every aspect of the experiments that I conducted
on the infective pathway of bacteriophage T4. And I actually corresponded with
an MIT faculty, who, at the time, I didn’t know. But Jonathan King actually had
some strains of bacteriophage that I was interested in and was
very generous in sharing it with me. So it was a wonderful experience
in getting me to understand how research is
conducted, and, really, what the academic style of
interactions might be. INTERVIEWER: So you
chose Yale. You decided that that was
the place for you. Tell me a little bit about
your time there, how your academic and career interests
developed, and just sort of what the experience was like. LO: Well, I had a great
time at Yale. It was a really remarkable
experience in a number of different ways. When I arrived, I had thought
that I was going to be doing math, biology, maybe applied
sciences of some sort. But I ended up taking an
introductory economics course that was completely different
from anything that I had seen before. There was a substantial amount
of mathematics involved. But yet the ultimate
applications were really quite relevant to day to
day experience. And that’s quite different
from math or physics, particularly at the
undergraduate level. So I got very excited
about that. The other thing that I found
remarkable about Yale was that, really, for the first time
since I started getting interested seriously
in academics– for the first time, I actually
met people at Yale that I considered to be really smart,
but who had no abilities to do mathematics. In high school, certainly at
Bronx Science, you very often equated intelligence with
technical abilities. You’re good at math,
physics, biology— you were smart. At Yale, I ran into a number
of individuals that were extremely intelligent but were
simply not numerate. Their form of intelligence I
found really different and fascinating. And that is sort of
the beginnings of my interest in economics. I realized that mathematics
was not the only way of understanding interactions in a
very deep way, and that yet you could actually put the two
together in some interesting fashion to come up with
some new insights. INTERVIEWER: So was it sort
of an immediate click? You mentioned the Intro
to Economics class. LO: No, it wasn’t. I was very confused
for a long time. What ultimately decided it for
me was a teacher, a professor, Sharon Oster, who taught a
fantastic intermediate microeconomics class. She was spellbinding. She provided intuition,
developed some very rigorous mathematical models, and made
it all relevant and really interesting. So I found her to be an
enormously inspiring teacher. And from the moment I took her
course, I took every other course she ever taught, and
ultimately asked her to be my undergraduate advisor. And I was a research assistant
for her, and ultimately wrote my senior thesis with her and
some other faculty at Yale. It was a tremendous
experience. And so I think that’s really
what ultimately made me focus on economics as the field
that I went into. INTERVIEWER: Some of those
early ideas you’ve talked about, the relationship or the
tension between mathematical models and human behavior, are
still present in a lot of what you are interested in, which
we’ll talk about later. But that’s an interesting
kind of continuity. LO: Well, it is. And I think it’s also a part of
my interests even back in high school, with The Worldly
Philosophers, but also from the science fiction
perspective. I, as a high school student,
read Isaac Asimov’s Foundation Trilogy. And the notion of using
mathematics to predict the course of human evolution, I
found completely captivating. And I didn’t know it at the
time, but economics was probably the closest field to
this fictitious psychohistory that Asimov talks about. And so I suspect that that
had something to do with it as well. But all of these pieces
were really amorphous to me at the time. And only with the benefit of
hindsight does some of it seem to make sense. INTERVIEWER: It’s funny. I just randomly, for some
reason, over the weekend had picked up the first
Foundation, the first of the trilogy. I had read it years ago. Were you a science
fiction fan? Was that something that
kind of drove your interest in science? LO: I was. I read a lot of things
in that genre as a high school student. But mostly Arthur C. Clarke. He was one of my favorite
writers, Robert Heinlein. But Isaac Asimov was a favorite,
not so much because of his writing style. I actually found Asimov’s
writing style not nearly as pleasurable as Arthur
C. Clarke. But the range of ideas that
Asimov had in his books were just astonishing, from I, Robot
to the Foundation to all of the other short stories that
he wrote, his field of vision was really tremendous. And so that got me very
much excited about the possibilities of science. And many of his ideas of science
fiction have actually become science fact over the
last couple of decades. INTERVIEWER: So like so many
people at MIT, you had an early interest in science
fiction and science. But you chose to really move
in kind of a different direction in college. Was it difficult to say
goodbye to science? It’s not saying goodbye. Maybe that’s the wrong term. LO: Well, that’s just it. You see, I actually didn’t think
I was saying goodbye to science, although in many
respects, I think I should have been. And we can discuss that later. But my thinking was that
economics could be as rigorous a science as the physical
and biological sciences. And I remember having many
dinner conversations with my elder siblings, who, of course,
were scientists, and who, of course, as elder
siblings do, spent a fair bit of their time torturing
me, asking me to justify my existence. And so I’ve actually spent a
fair bit of time thinking about whether or not economics
is or is not a science. I, of course, think it is. And when I was in college, my
interest in mathematical economics was probably motivated
by that drive. The ability to use formal
mathematical and statistical models to make precise
statements about economic phenomenon was what I
thought I was doing and studying in college. And it wasn’t until grad school
that I had a bit of a rude awakening to that effect. INTERVIEWER: We’ll move on to
grad school in just a moment. I just thought I would ask,
were there other very formative or important
experiences in college that pushed you in a particular
direction or helped form your ideas? LO: A couple. One was another professor,
Herbert Scarf, who taught a graduate course in economics,
microeconomics, and economic theory. And game theorists, Pradeep
Dubey and Martin Shubik, I took their courses as well. And during those courses, it
became clear to me that using formal models to study human
behavior was both a bit of a treacherous exercise– there
is a lot of behavior that doesn’t fit neatly into
these models. But at the same time, those were
the heydays of general equilibrium analysis. And tremendous progress was
being made in developing mathematical theorems that
would demonstrate the existence and uniqueness of
economic equilibrium. So it was a heady time
for the literature. And as an undergraduate, I got
exposed to some of it through the faculty. Yale’s economics program is
really tremendous in that they do expose the undergraduate
students to graduate level courses if and when they’re
ready and they’re interested. And because they also allow for
opportunities to write an undergraduate thesis, you
actually can engage in research even at that level. So my senior thesis was on game
theory, and ultimately, I actually got it published. So that was a really fascinating
process that I enjoyed and it gave me a taste
for the academic and a sense that there’s a lot that could be
done with relatively simple mathematical tools. INTERVIEWER: You decided to
pursue graduate studies. You went onto Harvard. You went immediately
from undergrad on? Or did you– LO: I did. And in retrospect, that might
not have been the best thing to do. But part of it was financially
driven. Because we came from a
single-parent household and we didn’t have very much in the way
of financial resources, I actually graduated from
Yale a year early. And when I was thinking about
what to do, I was actually choosing between law school
and graduate school. Because I was also interested in
applications and seeing how these ideas could actually
affect reality in practice. But then I did a very simple
economic analysis. Law school was three years, and
the tuition was however many tens of thousands
of dollars. And graduate school was,
for all intents and purposes, free. Not only was it free, I found
that they actually paid you to go to graduate school! They gave you a stipend. And so, to me, the
answer was clear. I have got to get a PhD. That, and also the fact that
my brother and sister were PhDs, as I said, provided some
motivation for me to achieve that level of success from
an academic perspective. So I think that ultimately I
decided that going to grad school was the right decision. And given that my undergraduate
advisor, Sharon Oster herself, received her PhD
at Harvard and spoke very highly of the program,
that was a very easy decision for me. Actually, MIT and Harvard were
the two choices that I had considered. At the time, I really
didn’t have much of an interest in finance. I didn’t know what finance
was about. And so, really, for my interest
in mathematical economics, I thought
that Harvard would be a better choice. INTERVIEWER: Did you
know Boston at all? Had you been here? I was actually going to ask
if you had been to MIT? You had a sibling who
had attended MIT. LO: I have very fond memories
of MIT because when I was in junior high school, my sister
was an undergraduate here. And so we would come up
every fall, and bring her up here by car. And I would stay here for a
couple of days to make sure she got settled. And while here, I spent an
enormous amount of time in the Student Center playing, at the
time, pinball machines. I don’t know if they have any
pinball machines here now. But I spent a lot of time there
and roaming the campus. So I loved it. And I developed early on
an affection for MIT. INTERVIEWER: But you went to
Harvard, nevertheless. So tell me a little bit about
your graduate school years. Again, I’m very interested in significant professors, mentors. LO: Harvard was a bit
of a rude awakening in a couple of respects. Probably the most significant
was that the faculty member that I was hoping to work with
ultimately ended up being on leave the year that I
arrived, in 1980. So I ended up taking classes
in micro, macro, and econometrics like all the other
first year students. And I was hoping that the
material that I learned as an undergraduate would be expanded upon in graduate school. I knew that the models that we
developed in undergraduate classes were relatively limited,
and that with more mathematics and more
understanding of economic concepts we could develop
more realistic models. So I was actually quite
disappointed when, after the first semester of my first year,
I realized the models that we developed were pretty
much identical to what I had done at Yale, and that there
wasn’t anything more. It was a bit frustrating
for me. In addition, at the time the
Harvard economics program had some difficulties. They were going through a
transition where some of their faculty were on leave. And the faculty that were there
at that time were not really supposed to be teaching
in a first year core. So the core was somewhat
uneven. And a number of us became quite
frustrated with that experience. And so by the end of the first
semester, I had actually filled out my application
for law school. I thought I had made
a bad mistake. And it wasn’t until I happened
to take a course in the spring semester taught by Bob Merton
in finance here at MIT that changed my mind completely
about graduate school. That was really the most
formative experience for me, realizing that you can
actually apply very sophisticated mathematics, but
in very practical settings. And that was what was missing
from general equilibrium theory and game theory. I didn’t feel that the
mathematics really brought us to any closer understanding
for practical kinds of situations, whereas finance
seemed exactly what I was looking for. So after that point,
I realized I wanted to do finance. INTERVIEWER: So for the truly
ignorant, such as myself, what do you mean when you
say finance? I think people tend to gloss
economics, finance, even business into one bucket. Talk about exactly what was
appealing and what it was. LO: Sure. In fact, they’re very closely
related, not surprisingly. Finance started out as a
branch of economics. But it has gotten to the point
where it has become so much more sophisticated in terms of
the models and methods that are applied that it has taken
on a life of its own. So in a nutshell, finance is
simply applying economic and mathematical principles to the
study of money, investments, in a world of uncertainty. Uncertainty is really the key,
because, for the most part, economics is actually pretty
well understood in the case of perfect certainty. If there is no randomness in
the world, we actually understand a lot about supply
and demand and how individuals engage in various kinds
of economic decisions. The sole aspect of the world
that makes finance interesting and nontrivial is the fact
that we don’t know what’s going to happen tomorrow. And uncertainty really
underlies all of what financial models are about. So in trying to model the
dynamics of financial markets, banks, asset management
companies, hedge funds, investment decisions,
corporate financing challenges, all of those are
problems that ultimately involve economics, but
financial economics. And because the tools of
finance have evolved so rapidly and so differently from
other areas of economics, it has really become almost a
separate field unto itself. In fact, most finance research
is done in business schools. Many economics departments,
including Harvard, have now hired a number of very talented
first rate financial economists. But for the most part, the
majority of the financial economists are actually in
business schools not economics departments. INTERVIEWER: So you had this
very, very significant experience taking this course
with Bob Merton. Talk about how that influenced
your path in grad school. LO: Well, it really changed
it completely. Up until then, my focus was
really on mathematical economics and game theory. But once I took Bob Merton’s
course on introductory finance, I realized that there
is so much more applications of genuinely substantive
mathematics to problems that cannot be solved in any other
way, and that yet can bring tremendous insight that
ultimately affects practice. That’s not something that game
theory or general equilibrium theory has really
been able to do. So once I took Bob Merton’s
course in finance, I basically took every other finance course
offered at the time at the MIT Sloan School. Fortunately, at the time, and
even to this day, Harvard and MIT have a very collegial
relationship, where students from one university can cross-
register, and almost seamlessly take classes in
the other university. So it worked out beautifully,
where I was able to take all of my courses in
finance at MIT. And when it came time for me to
take my qualifying exams at Harvard, I petitioned to create
a special field which was finance. At the time, they didn’t
have a field called financial economics. So I had to petition, and,
fortunately, was able to get one of the economics faculty at
MIT to examine me in that discipline. INTERVIEWER: So the research you
were interested in taking on as a grad student, just talk
about it for a bit in light of this new interest that
developed in finance. LO: One of the things that I
started out with thinking about in economics
was investments. I was fortunate to have as one
of my main advisers professor Andy Abel, who currently
is at Wharton. But at the time, he was a junior
faculty at Harvard. And Andy had been working on
investment theory, the idea of how capital in the United
States and elsewhere get created from various kinds of
economic considerations. When you buy a machine, and you
invest in it, you plan to use it for many years. What makes you decide to buy a
machine, versus renting or postponing? This kind of investment
theory fascinated me. But I couldn’t understand how
the kind of investment in machines translated
to investments in the stock market. I knew that the two
had to be related. We both use the word investment
in those contexts. And yet, the kind of models
that were developed seemed really different. My finance courses seemed
very different from my macroeconomics courses
in that respect. And so I spent a good part of
my graduate days trying to reconcile the two. I remember talking with Fischer
Black during his office hours and asking him how
could it be that, as an economist, we use the word
investment to mean purchasing physical capital, whereas in
finance, when we think about investment, we talk about purchasing shares of a company? Those two activities ought to
be related in some very fundamental way,
shouldn’t they? And Fischer Black said,
yes, they should. And I said, well, but there’s
nobody who’s working on that. How do we reconcile the two? Doesn’t this bother you? And he replied that when he runs
into contradictions and inconsistencies, that actually
delights him. Because he realizes that means
that there is work to be done! And so that was a big insight
for me, that I shouldn’t get frustrated. I should actually be thankful
that there was a thesis topic that was emerging. And ultimately, that’s what I
spent my years working on in my thesis, integrating real and
financial investments in a mathematically consistent
framework. INTERVIEWER: One thing that I
meant to ask you before– I don’t want to get
sidetracked. But I’m just curious. Maybe you can sort of put it
in the context of the work that you’re taking on. What is general equilibrium
theory? And what are what you saw
as its shortcomings. LO: General equilibrium theory
is a fascinating idea that was developed centuries ago by
French mathematician, Leon Walras, and others. And the idea sounds so simple. But actually, it’s quite
complicated to work out. The idea behind general
equilibrium is that when you look at an economy, you have
to focus on all the various different markets that exist,
each one corresponding to a different commodity or good. And within each market, you’ve
got individuals that demand a good and individuals that
supply the good. And in each market, ultimately
the intersection of supply and demand determines the so-called
equilibrium price for that market. Well, the fact is that all of
these markets are going on at the same time. And so instead of looking at
what happens in one market, in order to truly understand how an
economy changes over time, you actually have to ask the
question, how do all the markets equilibrate together? General equilibrium
does exactly that. It says that, given a collection
of individuals that all consume certain commodities,
and given a set of businesspeople that produce
those commodities, there have to be a set of prices for all
the goods that are traded in that economy so as to equate
all the supply with all the demand across all the markets. Now that seems like a really
tall order, to expect that this kind of an equilibrium
would occur across all of these different venues
and settings. And the idea behind general
equilibrium is to determine the conditions under which such
a general equilibrium across all these markets
could actually occur. Some beautiful mathematics
are involved in this. And not only are there
interesting mathematics about the existence of equilibrium,
there’s additional mathematics that say something about whether
the equilibrium is unique, and what happens when
you’re outside of an equilibrium, and how you reach
an equilibrium, how you move from one to the other. So it’s an endlessly fascinating
series of questions that actually relies
on some very deep mathematics to understand. But the problem with these
so-called theories is that they have become so general,
they are so abstract, that they’ve become divorced
from reality. Because in fact, in practice,
you actually don’t see general equilibrium occurring. In fact, in many cases, as we’ve
seen over the last few years, markets are often
in disarray and in disequilibrium. Prices are moving around
all the time, trying to equilibrate. And unfortunately, the
mathematics and the direction of the literature on general
equilibrium hasn’t really focused as much on the
transitions, the dynamics from one to the other, as opposed
to what occurs at an equilibrium. And so in that sense, I think
finance has become a much more relevant discipline, because
it actually has testable implications that have some
very, very practical applications. INTERVIEWER: To return to your
graduate work just for a moment, you develop mathematical
models to sort of compare investments in stocks,
bonds, with goods or machinery, and so on. I’m no mathematician. But can you talk about
what kind of relationships you found? And again, if you use your
mathematical models it will not mean much to
me, I’m afraid. LO: Sure. Well actually, it’s pretty
straightforward. The results that I developed in
my thesis really focused on what kind of investment policies
of a corporation would be necessary in order to
support the kind of real business activities that
it engaged in. And the answer is actually
pretty simple, and hearkens back to some research that was
done by MIT economist, Franco Modigliani, years ago. In a market where there are no
frictions, there’s no cost to engaging in issuing stock
or issuing bonds– in a frictionless market and
a market with no taxes, the answer is that the real economy
and the financial economy are pretty
much separable. It doesn’t matter how you would
finance a purchase of a new machine– whether you use debt financing
or equity financing– because in a costless world
where markets are perfect all the time, you can shift
from one to the other pretty easily. And therefore, the financial
side is almost an afterthought. But the problem is that as soon
as you introduce market frictions, that changes
completely. And really, it’s the frictions
that make things interesting. You have to understand where the
frictions are coming from and how they relate to the different sources of financing. And with market frictions, with
taxes, it turns out that there actually is an optimal
combination of equity and debt financing that will support the
kind of growth that a real business activity entails. And so working out the
mathematics of it is really what I did in my thesis. And it was done in a dynamic
context, so it was not just a static, one-shot kind
of a perspective. It was really couched in the
framework of a company that was engaged in multiple
projects over the infinite future. And so that really gave me a
deeper understanding for how to integrate the real and
financial sides of the economy, and gave me an
appreciation for why it is that frictions really
are at the core of what we do in economics. So much of economic theory
is the frictionless case. And those are important cases,
because you have to understand the frictionless case before
you can start seeing how frictions matter. But we often forget that
frictions do matter– because we get so enthralled with a
frictionless case, given that the mathematics are
so beautiful– that we don’t go to the next
step, which is to say, let’s make it messy again by building
in these frictions. And frankly, that’s what I’ve
been working on ever since. INTERVIEWER: You mentioned
Robert Merton. Were there other significant
mentors, influences during your graduate years that
we should talk about? I think you’ve actually
mentioned a couple of others. LO: Absolutely. Jerry Hausman was a critical
figure in my intellectual development. Jerry is an economist at
MIT, an econometrician. And it turned out that I got to
know him because I took an econometrics course with
him at Harvard. He was on sabbatical from MIT,
and he decided to spend the year at Harvard. And so he taught a graduate
econometrics course that I took and I did well in. Well, enough that he hired me
as a research assistant that summer, and then hired
me to TA that course the following year. And I really enjoyed it and
enjoyed working with him. And ultimately, he
became one of my principal thesis advisors. He was the one who gave me the
idea that you could actually use rigorous econometric
techniques to apply financial concepts to the data and learn
a great deal about how these theories actually worked
in practice. And so the field that I
ultimately spent most of my early career on, financial
econometrics, grew out of my interactions with Jerry, and
countless conversations, and free lunches and dinners that
Jerry treated me to very generously, during the time
that I was his student. INTERVIEWER: We tend to think
of graduate students, particularly at places like
Harvard and MIT, as being involved within this
all-consuming research quest for knowledge. Were there other things going
on in your life that were really important to you
or significant? Or did you find yourself
getting really sucked into the work? LO: Well, no doubt, graduate
school was very intense. But it was a fun intense, in the
sense that it was, for the first time, an experience where
I was surrounded by people that were all interested
in the very same relatively narrow field
that I was. So that was a new experience
and a very enjoyable one. At the same time, I was also
working as a research assistant and as a tutor,
because financially, it was a bit challenging for my family. And so I learned about the real
economic life of making money for supporting myself. I was also dating a girl who
ultimately I married. My wife, who, at the
time, was an undergraduate at Yale still. And so we had a long distance
relationship for my days at Harvard. And I remember spending enormous
amounts of money on phone bills. And it was really then that I
got into the habit of staying up late at night, because
after 11 o’clock, the rates went down. And even so, had we been able
to avoid these long night phone calls, we probably could
have purchased two cars by the end of my graduate
school days. So that was probably the most
significant other activity that I was focused on
during that time. INTERVIEWER: So you’re
a newly minted PhD. What’s next? LO: Well, when I went on the
job market, my wife— my girlfriend at the time—
was a graduate student in a PhD program in finance at the
Wharton School at the University of Pennsylvania. So I was fortunate enough to
be interviewed by them. And they flew me out to give a
job talk, and made me an offer the next day. And I accepted the
next day after. So by the middle of January
I was actually done with recruiting because my girlfriend
was there. And so it was pretty easy. Also, the Wharton School is
renowned in the area of finance, and it was a bit of a
new thing at the time for an economist to be hired by
a business school. At the time, most business
school faculty were hired from business school PhD programs. And there was some crossover,
but not a lot, and certainly not a lot of crossover
in finance. Finance was really a field unto
itself at the time: that was really more a business
school activity. And economics departments were
only really beginning to start thinking about offering classes
in finance, never mind concentrations in that field. So when Wharton made
an offer, to me, it was an ideal situation. My girlfriend was there. It was a bona fide and very
well respected finance department. And my only fear was whether or
not I was going to be able to measure up to a finance
department where I was an economist, an outsider. INTERVIEWER: Did you ever think
twice about pursuing an academic career, as opposed to
Wall Street or other options? LO: Well, I did a little
consulting when I was a grad student because the summer
between college and grad school I was a summer
intern at a company called Data Resources. It’s a software company
started up by some Harvard faculty— Otto Eckstein, Dale Jorgensen,
and others— and at DRI, I actually was
working on developing software for engaging in a variety
of economic analysis. Maximum entropy spectral
analysis was my project that summer. And so I did a bit of consulting
for DRI during my years as a grad student. So I thought a little bit about
going into industry. But because I was so fascinated
by the kind of questions that came up in my
thesis and I wanted to continue on, and, I think,
because the family upbringing that I had clearly valued
the academic lifestyle– my brother and sister were both
academics at the time– it really was clear to
me that I wanted to follow an academic path. INTERVIEWER: Let’s talk
a little bit about your years at Wharton. What were your research
interests? What kinds of things were
you engaged in? And also talk a bit about
teaching as a young faculty member. LO: It was a very interesting
mix of experiences that I had, even in my first year
at Wharton. When I started in Wharton in
1984, I was 24 years old, which is relatively young
for a business school faculty member. In fact, I remember very clearly
my very first day of class, I was clearly younger
than most of the students in that introductory
finance class. There must have been 100
people in the room. Wharton has quite a
large program, and they have big classes. And before I actually began
lecturing, literally the very first day of class, a student
raised his hand. And so not knowing any better,
I called on him and the student said, Professor Lo,
before you begin, we just have three questions. And I should have known right
away, when they use the royal we, this could not have
a good ending. He said, first, can you tell
us whether you have ever taught this course before? Second, can you tell us what
kind of consulting experience you have in this area? And third, can you tell
us how old you are? And not knowing any better,
I answered the questions. I said, no, this is
my first year. I’ve never taught this
course before. I have no consulting
experience, really, to speak of. And I’m 24, at which point literally half
the class got up and walked out of the room, because they
decided that they wanted to go to another section with more
experienced faculty. And I guess I can’t
really blame them. They’re paying a lot of money
for their tuition. But that was a sobering
experience. And it only went downhill
from there. So I was baptized in fire,
in terms of MBA teaching. And so that was a very important
experience, a formative experience for me. But on the bright side, the
experience at Wharton was tremendously productive for me
and the other junior faculty, because it turned out that in
that year Wharton hired nine assistant professors just in the
finance department, so I was one among nine. And the good thing about it was
that because we came in en masse, we became very close very
quickly, the nine of us, socializing with each other
after hours, basically hanging out all the time, because as
an assistant professor, there’s not much else
to do anyway. None of us had families
at the time. Some of us had girlfriends
or wives. But we didn’t have
any children. So we spent a lot of
time together. As a result, the department
had to get used to us more than we had to get used
to the department. And that was an incredibly
important experience because it allowed us to ask really
interesting research questions without the concern that some
tenured faculty member would disapprove. Because frankly, the tenured
faculty members weren’t even around to interact with us,
given their priorities and activities. So we spent a lot of time
interacting with each other, challenging each other, talking
with each other about ideas, and ultimately one
of the most fruitful collaborations that I had in my
career started in that year with Craig MacKinlay, who was
another assistant professor from the University
of Chicago. INTERVIEWER: Presumably, your
experience with the students got better from that
low point? LO: It did get better. And as I said, I learned very
quickly that MBA students are very demanding, and
with good cause. They are spending a lot of money
on their tuition, and they obviously have to start
thinking about paying it back in many cases with student
loans afterwards. It became clear to me that
relevance was really key. But, more importantly, that
there was a certain impatience among MBA students with respect
to abstract theories that may or may not lead to some
very specific practical implications. And in time I learned to
appreciate that perspective, and begin to take it more
seriously myself. Not to say that academic
theories are devoid of practical consideration. But there is a very
important divide between theory and practice. And I don’t think that
academics necessarily appreciate that as much as
perhaps they might or that they would if they, themselves,
were placed in kind of a practical
environment. INTERVIEWER: How did your
research interests evolve as a young faculty member? LO: That was a wonderful thing
about Wharton: it’s that we didn’t really have any
particular directions that we were expected to take
as junior faculty. And so we were pretty much free
to think about whatever it is that appealed to us. And my thoughts as a first
year faculty member were really in the direction of
this notion of market efficiency, and the ability
for the real and financial sides of the economy to engage
in pretty much separate kinds of directions. As an econometrics student in
Jerry Hausman’s econometrics course, one of the things that
I looked into was the ability of testing the random walk
hypothesis using a particular statistical procedure. Really, it was just an exercise
at the time to see whether or not one could use
fancier statistical methods to test the age-old idea of whether
or not you could use past stock market prices
to predict future stock market prices. One of the foundations of
efficient markets hypothesis is the notion that all the
information about the future of a company is actually
contained in its current price. And if that’s true, that means
you can’t use past price realizations to forecast future directions of the market. And as a student in Jerry
Hausman’s econometrics course, I developed an idea for using a
statistical test to capture that hypothesis. So when I got to Wharton,
I started talking with Craig MacKinlay. He and I had lunch pretty
much every day. And over lunch I would tell
him about these ideas. And he would say, well, we can
actually take it to the data. In my thesis, I spend a lot of
time testing various ideas, but I hadn’t really done
much with stock market data at the time. So he and I began
to work on this. And shortly thereafter, we
came up with a rather startling conclusion. Using the tests that we
developed and applied to the data, we came to the conclusion
that US stock market prices actually don’t
satisfy this random walk hypothesis. Stock prices aren’t actually
purely random. And at first, we thought
that we had made a programming error. But in fact, after several
repeated attempts to explain away these findings, we came to
the conclusion that this is exactly what the data
had to say. And so we tried to get the paper
published, and when we presented it at a conference we
were completely trashed by our much more senior and
well-respected discussant who simply didn’t believe
the results. His view was that markets could
not possibly be that inefficient, and that somehow
you must have made a programming error, which in our
business, is worse than calling your mother a
four-letter name. So we got very excited and
agitated, and went back to our computers and reprogrammed,
and looked at the results. And ultimately, we were
vindicated in the sense that this really was a feature of the
data and from that point on, for about a period of 10
years, Craig and I wrote a number of papers to try to
explain this anomaly, and ultimately published a book
that collected all of our papers to try to understand
this phenomenon. INTERVIEWER: How long
were you at Wharton? And then how do you ultimately
move on and come to MIT? LO: I was at Wharton for four
years, from 1984 to 1988. In 1988, my wife graduated. She finished her PhD; she got
a job in Boston, and so we were able to move. That same year, I gave a talk at
MIT and the faculty offered a position to me. Given my interests and the role
that Bob Merton played in my career, and Jerry Hausman,
it was a very easy decision. I accepted the offer pretty
much immediately. And we moved up to
Boston in ’88. INTERVIEWER: Let’s talk a little
about MIT in 1988 or the late 1980s. Your experience obviously
stretched back before that as a graduate student. What was it like then? Have you seen significant
changes– in either the study of finance
or in the student body– in the culture of the place? LO: Well, certainly some
things have changed. But I think a number of things
have stayed the same. And the things that have stayed
the same are really the reasons that drew me to MIT. I guess probably the most
important draw for me was that I believe that modern finance,
finance as a scientific endeavor— really began at MIT. It began decades ago with
Paul Samuelson. His interest in finance sparked
the interest of one of his most productive and most
talented students, Paul Samuelson’s student Bob Merton,
who joined as a graduate student in 1969, and
became immediately Paul’s close companion in rewriting the
entire corpus of finance theory from the ground up. To me, that was just a
tremendous draw and the MIT finance tradition that developed
subsequently. And the many other faculty
members that were drawn to this environment, including
Stew Myers, Fischer Black, Myron Scholes, Franco
Modigliani, John Cox, any number of– Steve Ross. We have tremendous faculty here
that have really built the edifice of modern
finance theory. So for me, it was an
easy decision. What’s changed over time is
that we now have a deeper understanding of the kind of
limitations that the early theories exhibit, and are now
more and more aware of and open to alternatives to explain
those departures. The ’70s, ’80s, and ’90s were
a terribly exciting time for MIT for the traditional finance
paradigm: efficient markets, rational expectations,
and all of the various ideas and products. The multi-trillion dollar
derivatives industry really came out of ideas that
Samuelson, Merton, Black and Scholes, Cox and
Ross pioneered. And so that was an incredibly
exciting time for that literature. But over the course of the
last decade or so, we’re beginning to see the emergence
of some new ideas that really demonstrate that not only are
there limitations to the existing theories, but there are
ways of extending them so as to be able to develop a more
rational, more internally consistent perspective on how
markets succeed and fail in different circumstances. INTERVIEWER: Turning now to your
research interests, as I mentioned at the beginning,
there’s a very almost intimidating array of topics
that you’ve tackled. But there are also some
consistent themes throughout that we’ve already touched on. Thinking specifically about
the work that you’ve done since coming to MIT, what are
some of the most important themes, from your perspective,
that you’ve attempted to grapple with? LO: I think that there’s
actually a pretty clear direction and evolution in my
research agenda, which really grew out of the work that
I did as a grad student and at Wharton. And that’s really to try to
understand the dynamics of financial markets. When I got to MIT, I was still
very much in the midst of this notion of a random walk and
whether or not one could create profitable trading
strategies from historical information. And it took me quite a few years
here at MIT to try to understand exactly what all the
nuances are of the various different types of activities
that financial investors engage in. And ultimately, I came to the
conclusion that, really, you could not explain a way these
anomalies as simply being exceptions that prove
the rule. There were just too
many of them. And they were too stark and
significant from both a statistical and economic
perspective. And so that got me to try to
take a little bit more seriously the kind of departures
from rationality that people in the industry
observe all the time. What made it frustrating,
though, was that the alternative to the traditional
economic and financial paradigm of rational
expectations and market efficiency was so-called
behavioral biases that psychologists and experimental
economists documented. The problem is that, in my view,
it takes a theory to beat a theory and the anomalies literature,
which was really just getting off the ground at the time,
doesn’t constitute a theory. They’re a collection of
counterexamples, and very important, by the way, but
they’re not really an alternative to the traditional
paradigm. So really, much of my work after
concluding that markets really don’t follow random walks
and that you have to take these exceptions as very
serious challenges to the received wisdom, much of my
work has been trying to understand how to reconcile
these two contradictory schools of thought. INTERVIEWER: And how
do we do that? LO: INTERVIEWER: Well, it
actually took me a while to come up with the answer. In fact, at first I thought
you couldn’t. You just had to pick. Pick your favorite flavor,
and then stick with it. But ultimately, because I spent
more time thinking from a number of different
disciplines and perspectives– including psychology, the
cognitive neurosciences, and evolutionary biology– that I’ve actually
finally come to a reconciliation of the two. And in a way, it seems almost
simple to me now, even though to this day it’s certainly not
received wisdom, by any means. It’s still fairly
controversial. The reconciliation that I came
to is the recognition that economic phenomenon and economic
institutions are creations of human activity in
much the same way that an ant hill or a beaver dam are
creations of living creatures that are adapting to a
particular set of challenges in their environment. And viewed from a biological
perspective, everything is different, everything
looks different. Rather than arguing about
whether or not behavior is rational or irrational, a much
more productive perspective is to ask what kind of adaptations
have emerged in the face of certain societal,
cultural, economic, and social challenges. And so it’s really the
confluence of evolutionary biology with the revolution
that we’ve had in the cognitive neurosciences that has
been able to allow me to put together these different
pieces because, ultimately, we’re focusing on
human behavior. That, I think, is the key. It’s that all of the different
disciplines that I’ve ultimately ended up learning
about– in order to answer the question, why do people behave
the way they do in economic contexts?– are studying the same thing:
human behavior. We may be focusing on different
elements of it, but we’re all studying humans. And because of that, our
theories should be mutually consistent. They may not be focusing on
the same thing, but as the great evolutionary biologist,
E. O. Wilson, wrote in his book, Consilience, these facts
have to be mutually consistent with each other, because we’re
explaining the same phenomenon. And so, really, that’s what my
recent work has been about. It has been about using
different aspects of human behavior to try to understand
the whole, to create an integrated theory of human
behavior that spans the various different contexts and
activities that we are likely to engage in. INTERVIEWER: Let’s drill down
just a little bit and maybe focus on some examples
or an example or two. LO: Sure. INTERVIEWER: I’m an
economic actor. We all are. I make irrational decisions
all the time. I’ll confess. What’s the theoretical
explanation for that? Why would I make really, really
bad investments that can be sort of demonstratively
bad, or make bad financing decisions? LO: To answer that question, we
should first ask the prior question, which is, how
do decisions get made? Or how does behavior emerge? And obviously, we trace much
of behavior to the brain. So we need to spend a little
bit of time talking about neuroanatomy, and ask the
question, what are the components of the brain that
neuroscientists have been able to identify that are linked
to specific actions? Well, we know a few things
at this point. We know, for example, that there
is a part of the brain that is relatively primitive
from an evolutionary perspective, the so-called
midbrain or the amygdala, and the structures surrounding it. This part of the brain is really
focused on relatively instinctive kinds of activities,
so-called fight- or- flight response, fear,
greed, sexual attraction, and we know that this part of
the brain focuses on those activities through imaging
techniques that neuroscientists have
conducted. So that describes one
set of activities. Another set of activities that
neuroscientists have also deduced as focusing on a
different part of the brain is higher thought functions that
we would normally associate with humans uniquely; things
like language ability, mathematical ability, logical
deliberation. That part of the brain is,
from an evolutionary perspective, the newest part,
and it is given the name neocortex to indicate that. One of the things that we know
from the neuroscience literature is that these two
components, the amygdala versus the neocortex, in many
cases they work together. They’re obviously connected
in many different ways. But in other contexts they work
antagonistically, to the point where when an individual
is faced with very strong emotional response that will
actually physiologically restrict the flow of blood
to the neocortex. I illustrate this with my
students by asking them to think back to periods in their
lives when they were dating, and they were trying to meet
very attractive partners, that ultimately they concocted a
relatively staged kind of a scenario in which to talk with
them for the very first time and ask them out on a date. And when that accidental meeting
arrives, you would think that they’d be able to
charm this other individual into going out on a
date with them. But more often than not, when
the moment occurs, we end up becoming tongue tied, hopelessly
and embarrassingly inarticulate, and unable to
impress this individual. Why does that happen? Well, it happens because strong
emotional stimulus— which includes sexual
attraction— can actually reduce the flow
of blood to your neocortex. It makes it harder for you to
use that part of your brain. For all intents and purposes,
love makes you stupid! And that’s an example of a
constraint, a biological constraint, that has some very
reasonable evolutionary underpinnings. Obviously, when you are
getting chased by a saber-toothed tiger, it’s more
important for you be scared and run like heck than for
you to be able to solve differential equations,
even if you’re at MIT! And as a result, these kinds
of neurophysiological constraints have a very
strong implication for financial markets. When we are subjected to strong
emotional stimulus, we will react in predictable
ways. We will have difficulty in using
the logical faculties that, for the most part,
we’re able to make use of for other decisions. But under extraordinary
circumstances, those mental faculties are not
available to us. And this has to do with another
basic evolutionary principle about diversity. Typically, when we think about
markets in general– the journalist, James
Surowiecki, wrote a book describing them as the wisdom of
crowds, the idea that when you have a crowd, and if
the crowd is relatively independent in its thinking
and evaluations, then by pooling the collective
evaluations of this crowd, you get some very, very
wise decisions. For the most part, financial
markets, and most economic markets work in that manner. Two things can violate
this principle of the wisdom of crowds. One is if you don’t have a
crowd: small number of individuals. But second and most importantly,
if the crowd is not independent, if they all
think alike, if we all think exactly the same way we don’t
get the wisdom of crowds. We get the madness of mobs. And the distinction between
the two is really one of diversity— diversity of thought. If we are all thinking exactly
the same thoughts, if we all want to get out of a crowded
theater because of a fire, we know that the exits are going
to be a real constraint. That’s going to create
problems. If we are all thinking alike,
and we want to get out of the stock market at the same time,
that’s going to create a stock market crash. And so the key to understanding
periods of financial market dislocation and
so-called irrationality– and that’s a very loaded term. It’s not at all clear that it’s
irrational to get out of a crowded theater if
you smell smoke– the fact is that those periods,
when we all think alike, when we don’t have the
wisdom of crowds, but we have the madness of mobs, we react
very differently. And economic theory, the way it
has been developed, really goes out the window. We need to develop a better
theory that takes into account these periods of coordination
and correlation, and I think that that can be done through
understanding a bit more of the neurophysiology of decision
making and then some of the evolutionary dynamics
of diversity. This is one of the reasons why
biodiversity is such an important part of the
environmental movement. It’s because having a diverse
set of species will allow you to be much more resilient in
changes to the environment. That same principle,
literally— the same principle— applies to thought. By having a diverse group of
individuals, diverse in their thinking, we are much more
likely to survive changes in our economic environment and
be able to move on in a somewhat more rational manner. But without it, without that
kind of diversity, we are risking the same kind of
punctuated equilibrium that we see in evolutionary biology. INTERVIEWER: You mentioned a
couple of minutes ago that love can make you stupid. Is the idea that money can
make you stupid too? LO: In a different way,
yes, that’s right! So again, neuroscientists have
done experiments where they’ve imaged to individuals’ brains
while they receive certain kinds of monetary reward. They play certain games where
they win small cash prizes. And what they’ve identified is
that the neural mechanisms for financial gain are very
much the same as for drugs like cocaine. Your brain is stimulated to
releasing dopamine into the nucleus accumbens, the pleasure
center of the brain. Certainly not as much, and not
as intensely, as when you are on a drug like cocaine, but
nonetheless, the mechanisms are actually one and the same and so it’s easy to see how,
over periods of great prosperity, during bull markets,
people can get addicted to that kind
of an experience. The more money you make, the
more money you want. You would think that after
earning $10, $20, $30 million that should be enough. But in fact, it has nothing
to do with the amount. It has to do with the experience
and the kind of pleasure it generates
in the brain. And so all of these elements
actually come to play in developing ideas about how
economic decisions get made. It’s not just pure mathematical
deliberation that will guide individuals in their
decision making; it’s a much more complex amalgam of
different decision making components. And by understanding how the
components work together– sometimes in tandem, sometimes
antagonistically– we have a better chance of
coming up with a more realistic theory of financial
market dynamics. INTERVIEWER: I was very
interested in preparing for this interview, also, to read
some of the things that you’ve written about risk and
uncertainty and how that drives human behavior, or how
human behavior changes in those circumstances. Can you talk about that a bit? LO: Sure. First of all, let me explain
that by most dictionaries and thesauruses, risk
and uncertainty are considered synonyms. But in fact, from the
economist’s point of view, Frank Knight— the University
of Chicago economist— distinguished the two by calling
risk the kind of randomness that one can
parameterize, for example, mortality tables for life
insurance or the odds of winning in a lottery. What he called uncertainty was
the kind of randomness that you actually couldn’t
put numbers on, that you couldn’t quantify. And he argued originally that
uncertainty really explained why it was that certain
entrepreneurs, like Bill Gates would become multibillionaires,
whereas others who don’t take that kind
of bet, actually, only end up earning normal economic
profits, nothing nearly as outsized and grandiose. But there’s a very important
emotional underpinning to this distinction that Knight really
didn’t focus on, but now, with the benefit of decades of
research in the cognitive neurosciences, we understand
much better and that really has to do with fear. The fact is that the most potent
form of fear is the fear of the unknown and so if
we can’t put a statistical probability on certain events,
if we can’t quantify them in some manner, then we are
actually not dealing with well-defined randomness,
namely risk. We’re dealing with completely
unknown kinds of outcomes and so as a result, people tend
to be much more averse to uncertainty than they
are to risk. The case in point is the recent
experience that we’ve seen in the stock market. Clearly, people are happy to
take on the riskiness of the stock market because, for many
years, the stock market has done just fine with a certain
level of volatility and a certain level of expected
return. But over the last five years,
the stock market has been extraordinarily erratic. And erratic particularly in
terms of its level of volatility, because during the
fourth quarter of 2008, when Lehman Brothers went under, the
volatility of the US stock market hit a spike of something
like 60 percent per year, and on an intradaily
basis even higher. At that level of volatility,
most investors would say, cash me out, I really don’t want to
be part of this anymore! And the fact is that the
volatility of volatility in the US stock market has been
tremendous over the last few years to the point where we’re
seeing a lot of individual investors having taken most of
their life savings out of the stock market and putting them
into cash, which is ultimately not a very successful way to
plan for their retirement. But that’s an example of the
fear of the unknown. If you don’t know what the rules
are, if you don’t know whether the house is going
to confiscate all of your earnings at any point in time,
then your simple decision will be not to play. And I think we’re seeing that
played out now on a much bigger stage over the course
of the last few years. INTERVIEWER: So obviously here,
nearing the end of 2011, these kinds of conversations
are not just academic. We witnessed the financial
meltdown in 2008. We’re now seeing a really
significant crisis brewing in Europe. Talk about these kinds of
massive crises that sweep through the markets, through the
sort of global financial structure, in terms of some of
these things that we’ve been talking about. LO: Well, I think that’s part
of a much larger theme that one can only really see from
the perspective of evolutionary biology, and that
is that I think crises of all sorts are the manifestation
of the combination of technological advances
and human behavior. In particular, over the course
of the last 12,000 years, the human population has grown
really dramatically. If you take a look at typical
estimates of population during that time period, and you plot
it on a graph, you see that the prototypical hockey stick
exponential growth applies perfectly to the population
of humans. The way that we’ve been able to
reproduce so successfully in an otherwise hostile
environment is through technology— through the collective
intelligence that we’ve been able to develop over hundreds
of thousands of years of evolution to tame our
technology to our physical needs. And those technologies– whether they’re agricultural,
or information technologies, or manufacturing technologies,
or financial technologies– they often have unintended
consequences. For example, DDT was a
tremendous technological advance for agriculture, but
it led to birth defects. Automobiles were a wonderful
invention, but they led to air pollution. And of course, industrial
activity now seems to be responsible for climate change,
global warming. And I would argue financial
technologies– things like securitization,
insurance contracts, derivatives– are wonderful inventions,
wonderful advances in technologies that also can
lead to unintended consequences. And these unintended
consequences really are the result of the fact that the
technologies provide us with much greater power in
certain domains. But the greater power oftentimes
is not controlled properly because human behavior
has not changed that much in the last 60,000 years. We are still very much wired the
same way we were back at the time when we first became
fully sentient, and our neocortexes developed into
what they are today. And as a result, the fact that
we’re dealing with, in many cases, relatively ancient,
hard-wired brains, but we’re dealing with technologies that
allow us to do things that we were never intended to do in our
original environment, has led to some challenges. Those challenges can
be dealt with. But the way that we’re going
to deal with them is by developing smarter technologies,
more advanced technologies. Humans may not have been meant
to fly the way that we fly now, but air traffic control and
safety measures allow us to do so relatively
safely today. And I think that we are now
at a stage where financial technologies have become so
advanced that we can now do things that we were
never able to do. We need to develop the safety
mechanisms to prevent us from doing the things that we ought
not to do with those powerful technologies. INTERVIEWER: Are you saying that
it’s a bit– and I don’t know if this analogy
captures it, but– sort of this idea that we all
crave things like sugar, salt, saturated fats, that we’ve
evolved to crave. And they end up killing
us, because they’re no longer so scarce. And we just take too
much of them. LO: Exactly! So for example, certainly sugar
was present in the diets of humans 60,000 years ago. But they were few
and far between. Occasionally, you would
run into a fruit tree. And you would have a
pear or an apple. And that was enormously
attractive, but it came on occasion. That’s very different from being
able to eat deep fried Twinkies every other day, which
I don’t believe we were adapted to do. Now maybe if we keep doing that,
over the course of the next 50,000 or 100,000 years,
we will have the ability to process that kind of
a sugar intake. But our current biologies are
not wired to engage in these kinds of activities. And a good case in point
is the internet. The internet is really a
relatively new invention, just a matter of a couple
of decades. And if you think about what we
can do now on the internet– well, for one thing, at this
point in time we are able, with a click of a mouse, to
wipe away half of our retirement investment in a
bad investment decision. That has never been possible
in the history of financial markets; it is possible today. And so if you think about the
power that individuals have to do good and to do harm, they’re
both magnified by technology. And so we need to develop the
guardrails, the safety mechanisms that will prevent
us from doing the kind of damage that we are now able to
do with the very advanced technologies that we have
at our disposal. INTERVIEWER: So let’s talk a
bit about those guardrails, because I’m very interested
in asking you about the relationship between these kinds
of theories, this kind of thinking, and policy making,
and prescribing solutions for some of these
intractable problems. What do you see as your role? And how do we go
about doing it? LO: I have to admit that for
much of my early academic career I wasn’t interested
in policy at all. In fact, I was much more focused
on the dynamics of private markets, and really
never even thought about what was going on in the public
arena, simply because my presumption was that policy was
being formulated by the experts in policy making, and
that the challenges, the intellectual challenges, were
really in trying to understand the dynamics of private
markets. It didn’t occur to me until
relatively recently that there are challenges in the policy
arena that are at least as great and if not greater in
terms of affecting a much larger group of individuals. And so over the course of the
last 10 years, I’ve spent more time thinking about the
interplay between policy and private activity. The first thought that I’ve
been spending time on is really the underpinnings of
policy making to begin with. Economists and policy makers
formulated a number of policy prescriptions with the implicit
assumption that individuals, and therefore
institutions, are rational actors. The efficient markets hypothesis
or rational expectations have been applied
to macroprudential regulation as well as to financial
markets. And the first observation that
I think needs to be made is that the same limitations that
we found to the kind of investment theories that private
financial markets exhibit really have to
be applied to more general policy settings. In other words, the kind of
madness of mobs that we see in financial markets have to be
applied much more broadly to economic settings in
formulating policy. And there are a number of
policymakers now that do have that perspective, but
not nearly enough. And certainly not enough to
really affect yet the direction of policy. I think that’s the first
insight, to begin with. And from that point on, all
other policy implications will follow very readily. And changes in policy, more
importantly, will be suggested by this kind of a different
perspective. If we look at markets not as
static, stable, physical systems that have underlying
laws that are immutable, but are actually biological
institutions that can evolve over time and as a function of
market conditions, I think we’re much more likely to
formulate policy prescriptions that are themselves adaptive
and much more likely to succeed in a variety of
different environmental conditions. INTERVIEWER: It’s obviously a
very hot political issue right now, just what exactly the
role of governments are in all of this. What’s your view on that? LO: In my view, government is,
itself, an evolutionary adaptation. Without a doubt, the institution
of government is critical for resolving a number
of challenges that we face as a society because
markets cannot work perfectly by themselves. They do break down from
time to time. And actually, implicitly,
I think we already recognize that. For example, in this studio,
there are a number of laws governing the fact that there
have to be sprinkler systems, there have to be well-lit exit
signs, the fact that there have to be certain protections
for all of us, in case of fire. Why do we institute such
expensive features of this particular setting? It’s because we recognize that
we can’t leave it to the discretion of the builders of
any institution to put these in place, because, left to the
choice of a real estate developer, they will almost
never choose the more expensive option if
they can avoid it. The probability of fire
seems relatively low. And if you allow people to
choose whether they want to build a building with fire
protection versus another building without, they will
choose the cheaper alternative, unless there’s an
absolute demand for the more expensive one. And for most days, there isn’t
an absolute demand. That absolute demand
occurs right about when there’s a fire. Of course, by that time
it’s a little late. We know this about ourselves. We know human nature. We know that we will not gladly
pay a higher rent for a facility that has fire
protection if we are given a choice. And so after a number of very
severe fires with many, many casualties, we as a society
decided to institute a law by government that says that all
buildings have to have fire protection if they
have a particular function to the public. And so this is an example of
how laws and how government takes into account human
frailties in a very explicit way. Well, if we understand this
about something as basic as fire protection, it has now come
time to make that same leap of understanding for all
sorts of contexts, including financial ones. We now have to build in
financial protections that prevent us from doing the things
that we know we’re going to want to do,
particularly after extended periods of prosperity. After many, many decades of the
economy growing rapidly and financial markets generating
lots of value added for all the market stakeholders,
at some point, we’re going to say to ourselves,
you know what, we don’t really need as
many protections as we had in the past. It’s time for us to perhaps
loosen up some of these protections that have been
reducing the growth rate of our economy. We don’t need seat
belts anymore. We don’t need leverage
restrictions anymore. We don’t have to have
constraints. That’s a natural reaction from
decades of no car accidents or no big financial meltdowns. And that kind of human tendency
is something that we have to recognize. We do recognize it in limited
context, like fire protection. We don’t yet recognize it
in economic settings. INTERVIEWER: Libertarians see
all of this, of course, in terms of maximizing individual
freedom, as opposed to restricting or regulating
that freedom. What’s your thinking or your
response to that kind of approach, which seems to be
very, very different? LO: Well, in fact, I would say
that individual freedoms are absolutely critical. And one of the unique aspects
of Homo sapiens is the fact that we can actually choose how
we wish to live our lives. And I think that that’s an
absolutely fundamental aspect of human society. There are very few societies
that are so regimented across the board that will
actually last– even totalitarian regimes will
ultimately fall, because humans want to be free. However, freedom doesn’t
necessarily mean that any action and activity should be
permitted; because of a very important aspect of human
society, which is what economists call externalities. If my activities have negative
consequences for you and your family, then we have
a problem. We have a challenge that
we have to resolve. And so because of the success
of population growth, we are now at a point where a number
of activities that we have previously engaged in
have externalities, or spillover effects. When the economy is relatively
small the kind of spillover effects that we’re talking about
today are really remote. But when the economy gets big
enough, when human society gets to where we are today–
seven billion people, as of the end of this year– these externalities become
much more significant. So we have to balance the human
drive for libertarianism with the acknowledgement that
individual actions very often have much broader
consequences. And ultimately, that’s really
the political process. That’s what we really rely on
politicians to resolve. They haven’t been doing such
a great job lately. But eventually, they will and
they must be able to come to terms with these very
difficult decisions. And ultimately, by understanding
the dynamics, the spillovers, the
externalities, we actually have a better chance of creating
a much more palatable solution for all stakeholders
involved. INTERVIEWER: I also wanted to
ask you about financial education for, I guess, what
I’ll call the layperson. I am a relatively
unsophisticated investor. How much education should a
person who’s not a business person, not particularly
interested, frankly, in those kinds of issues have? And what is the role of making
people more sophisticated and more aware of these things? LO: I have to confess that
to someone with a hammer everything looks like a nail. So if you’re going to ask an
academic how much education they should have, the answer
is, lots and lots. But I think that this is
actually part of a much broader trend that we see across
all aspects of our existence today. Life has gotten more complicated
in many ways. It’s obviously also gotten
simpler in yet other ways. But the fact is that, with
various advances in the sciences, we now know a lot more
about all aspects of our day- to- day existence. And therefore, we can make
more informed choices. Take for example, diet. In the 1950s, the advice that
most people received from their parents was eat to
your heart’s content. There was no discussion of
cholesterol, or carbohydrates, or various kinds of health
issues that we now understand to be quite significant and
that we can do quite a bit about by watching our diets. And so the advances that the
medical sciences have offered have, in many cases, made
our lives simpler. We now can take a flu shot and
be assured that there will be relatively minor consequences
of a bad flu season. But it has also made our lives
much more complex in that now you have to decide how much
fiber you’re going to have, how much protein, how much
carbohydrates and are you taking enough vitamins and
minerals each day? So we have each had to
become a little bit more expert in diet. By that same token, I believe
that we have been given tremendous opportunities to
engage in a variety of different investment activities
that would have been impossible to us
even 10 years ago. Now you can buy an ETF that
invests in many different countries, whereas in the past,
you would have had to go to various different brokers
to engage in those kinds of transactions. So in that respect,
our financial lives have gotten simpler. We have had more access to
investment opportunities. But those additional
opportunities mean that we have more complex decisions
that we have to make. So I think that in the short
run investors really should spend a lot more time thinking
about their investment portfolios. Typically, an investor will
think about their portfolio maybe four times a year, once
a quarter, certainly once a year when they have
to do their taxes. But if you think about that
level of attention to your financial health, that’s
actually a pretty limited amount of time that you spend
compared to how much time you spend on your physical health. You have a check-up of annually,
but you’ll have other visits that involve
various different specialties. So we spend more time now
thinking about our physical health than we do before,
because we know more. By that same token, we need to
spend more time thinking about our financial health than
we have before. I believe that over time as
economists, particularly financial economists, make
greater strides in taking into account human frailties in
various different financial products, eventually
we’ll be able to develop smarter products. In the same way that an iPhone
almost eerily knows what you want to do when you want to do
it and makes it simple for you to do, we need an investment-
kind of an iPhone device that will allow us to reach our
ultimate financial goals without having to spend too much
time thinking about it. We’re not there yet. And until we get there, we
need to spend more time understanding how financial
investments work and how they may or may not be consistent
with our ultimate goals. INTERVIEWER: I know that
teaching is, in fact, a very important part of what you do
and a big part of your life. Talk a little bit about that. And also, how do you
balance it all? LO: Teaching is important. And I have to say that early on,
because of my experiences in third grade with Mrs.
Ficalora and throughout, I realized the benefits and
tremendous costs of having good and bad teachers. Having an inspiring teacher
can open your life to an amazing series of discoveries
and pleasures that would never have been possible. And similarly, a bad teacher
could close a mind forever to a subject, which I think
is just a crime. I think that teachers
are underpaid– particularly at the elementary,
middle school, and high school levels– because if we could measure
their impact on society, if we could really measure the impact
of having a good 11th grade trigonometry teacher on
what that individual does 15 years from that point on,
we would pay these teachers a lot more. We would be a lot more
careful about tenure. We would be a lot more focused
on developing good teachers at that level because that’s where
minds are opened and where minds are shut. So to me, teaching is one of the
most important activities that we can do because that’s
the way that we replenish the stock of intellectual capital
in our society, the way that we encourage collective
intelligence, and frankly, the way that we’re going to solve
the challenges of society for the next few decades. The future scientists, and
engineers, and economists that cure cancer, and fix global
warming, and find new sources of energy, they are our
students today. And so if we think in those
terms, I think we’d take teaching a lot more seriously
than we do now. INTERVIEWER: What’s it like
teaching MIT students? LO: That has just been
a real pleasure. One of the constants that drew
me and many of my colleagues to MIT is the quality of
our students here. And it’s really noticeable. I really view teaching our
students here as a privilege, not a burden. We all like teaching students
that are good students, that are excited to learn, and that
have a certain degree of creativity and energy. Well, MIT students have
that tremendously. They are not only creative,
but they have this entrepreneurial spirit that
really makes them extraordinary students to teach
because they don’t take concepts for granted. They will take an idea and
work out five different implications, and really
challenge the teacher to really think about these ideas
from a much deeper level. So I have to say that I’ve
learned more from the students here than I think I’ve taught
them because of the challenges that they’ve thrown out, the
creativity that they bring to the process, and ultimately
the energy that they have for learning. Our students are absolutely
hungry for knowledge. And they drink it up
at an amazing rate. So it’s really a tremendous
environment for faculty. INTERVIEWER: Speaking of
entrepreneurial spirit, I also wanted to shift gears a little
bit and ask you about some of your forays into that world. Talk about that a bit. And why has it been important
for to do it? LO: As I mentioned, when I
started in economics my interest was very much
on the applied side. I love seeing ideas
put to work. And one of the fascinating
things about Isaac Asimov and the Foundation series was that
some very abstract mathematics was applied to some very
practical situations. And so from the very beginning,
my interest was in seeing ideas implemented. And so during the period where I
was studying the dynamics of financial markets, the random
walk hypothesis, it nagged me to no end that here we were
teaching our students that markets are efficient, it’s
impossible to beat the market, you should focus on risk and
reward, and I had actually never taken any of these
ideas that I had developed into practice. I had never tried to see how
it is that these investment ideas did or did not work
in actual settings. And so in 1999, I decided
to start my own investment company. I took a two- year leave of
absence from MIT, and worked with some of my former students
and consulting colleagues, and created a
company to try to undertake some of these ideas from a
more practical setting. And over time, I’ve learned an
enormous amount about how markets really work from trying
to use these ideas in practice, and seeing how they
succeed and fail over various different kinds of market
environments. INTERVIEWER: Maybe just to
follow up, what kinds of things have you learned? I can imagine that that might
seem like an obvious question. LO: Well, there are few things
that I think I discovered quite to my surprise. One is that it’s very painful
to lose money. But it’s a lot more painful to
lose other people’s money than to lose your own. That aspect of human nature, I
think, is something that we don’t really spend enough time
talking about in our investment classes. I do now. But before I engaged in this
activity I didn’t realize just how important that emotional
aspect is, and why it’s so important for investors to
engage in third party financial advisors. In a way, it’s much like the
medical practice of never allowing a doctor to operate
on his or her own family members, because you’re too
emotionally invested. And in many respects, managing
your own wealth can be a very challenging activity, unless
you are trained to think somewhat more remotely, somewhat
more objectively about these kinds
of decisions. Managing other people’s
wealth has that very same feature to it. You don’t want to disappoint
others. You don’t want to engage in
practices that are too risky. But at the same time, you are
stewards of their wealth. And so they’re expecting you
to be able to make good investment decisions. So the processes by which you
can provide that type of stewardship and the emotional
toll that that can take on an individual is something that
I learned about firsthand. And I found it very valuable. And ultimately I think it’s
reflected in the research that I’ve done and what I teach my
students now about how they might engage in these kinds of
activities somewhat more productively and with somewhat
more preparation for the kind of challenges. INTERVIEWER: Thinking a bit more
about MIT, as we sort of start to wind down, the
MIT economics, finance departments– it’s obviously a legendary
a group of people. We’ve touched on some
of your colleagues over the last 20 years. Who are some of the other
luminaries that you would like to talk about? And are there any really
interesting stories that we could gather? LO: There are so many. One of the things about MIT that
I love– but it’s also something that one has
to get used to– is that there are so many
superstars that are here that nobody really feels like they
need to be catered to or treated any differently. This kind of an egalitarian
ethic is actually very different and unique,
I believe, to MIT. I have been a student at
Harvard, a student at Yale, a faculty member at Wharton. I have visited most of the major
business schools and economics departments over the
last few decades giving talks. And I have to say that MIT is
really unusual in the ethic that has developed over the
years, particularly in the economics department and
in the Sloan School. And the ethic is really that
we are all part of the same college of ideas and you’re judged by the clarity
of thought and the creativity of your research. It’s really that kind of
motivation that keeps us young, and it keeps
us thinking. And there’s very little in the
way of formalism that we engage in so we interact
in a very open and collegial manner. That’s one of the things that
I value most about MIT. In that setting, I have to say
that there are a number of individuals that I’ve learned a
great deal from in a variety of different departments. One of the things that I
discovered early on was that MIT is really like, for me,
the world’s biggest candy factory in that there are so
many different interesting things going on in different
departments. In particular, in addition to
the Sloan School and the finance group, I’ve interacted
with individuals in the economics department, with the
brain and cognitive sciences department, with electrical
engineering, and computer science, with the AI Lab, years
ago when it was called the AI Lab, and now the CSAIL,
the computer science and AI Lab together. All of these individuals, I
think, have really contributed to a better understanding of
human behavior from my perspective. So it’s hard for me to name any
one or two individuals. But certainly in the pantheon of
greats, Paul Samuelson, Bob Merton, Franco Modigliani,
Patrick Winston, Noam Chomsky, Marvin Minsky, and Tommy
Poggio, all of these individuals, I think, I’ve
learned tremendous amounts from over the years. And many more who have
contributed to my education. INTERVIEWER: That’s
quite a list. LO: There are a lot of
impressive people here at MIT. INTERVIEWER: So thinking ahead
just a little bit, maybe just give us an assessment, as we
wind down, on what you feel your impacts might have been as
a researcher, as a thinker in finance. And where do you want
to take your work? Obviously, the world is full
of challenges right now, in particular, in this area. And where do you want to go? LO: In terms of where I’ve been,
let me start with that, and then talk about where
I hope to go. The early phase of my career was
really focused on applying rigorous concepts of
econometrics and statistics to financial models to develop
the field of financial econometrics. And I believe that that
field is now very healthy and well- developed. And we now understand that
it’s critical to use the proper methods of inference
for understanding the empirical anomalies that we
identify in financial circumstances. Those empirical anomalies
leave a lot of room for innovation and creativity
because they illustrate that the traditional paradigm is
simply not satisfactory. There are missing pieces
of the puzzle. And over the last 10 years of my
career, I focused on trying to fill in those pieces of the
puzzle by bringing ideas from evolutionary biology and the
neurosciences to provide a deeper and more nuanced understanding for human behavior. Where I think I’d like to go
over the course of the next decade or so, if I’m lucky
enough to continue being active, is to take those ideas
and work out their implications for human
behavior writ large. In other words, I’d like to
develop a complete theory of human behavior. And by complete,
I mean one that applies across all contexts– social, cultural, political,
and economic. Because we are not so
compartmentalized that on one day you’re an economist, on
another day you are a psychologist, on another day
you’re a biologist, in terms of the way you act. We’re talking about humans. And human behavior cuts across
all of these siloed disciplines. And so what I’ve been spending
time on recently is trying to understand how to integrate all
of these different silos in a more complete theory of
human behavior to the point where we understand so much
about human behavior that we can actually create
it artificially. This might seem like a program
in artificial intelligence. But in fact, it’s much
broader than that. I think it was Marvin Minsky who
said that he didn’t want to create a computer that he
could be proud of; he wanted to create a computer that
could be proud of him. And I think that that’s a very
important insight because it says that the notion of behavior
is something much deeper than simple actions. We can now automate
lots of behaviors. There are wonderful factories
that have very few human actors and are extraordinarily
efficient. But somehow we don’t consider
them to be human. And the behaviors, frankly, are
very stupid in the sense that they’re highly stylized
and they are not robust to changing environments. Going forward, what I’d like to
be able to do, what I think we now have the wherewithal to
do, is to develop a theory of behavior that allows us to
replicate human interactions, human decision making in
the way that humans actually make them. So the Turing test is a
well-known idea, pioneered by Alan Turing, who said, that
if at some point by typing messages back and forth with
another party, you can’t distinguish between the
responses you get from that individual and the responses
that you get from a human, then that computer is for
all intents and purposes intelligent and human. I actually think that that’s a
relatively low threshold for what we need to achieve. I actually think that a much
more relevant test for whether we have achieved an
understanding of human behavior is to create a computer
that can engage in activities to such an extent
that seems to replicate humans that, at some point, if someone
were to decide to terminate that machine, a number
of us would object on moral and ethical grounds. That, to me, is the ultimate
Turing test of human behavior. If we can create behavior that
can actually mimic not just the look and feel of human
behavior, but actually the mechanisms by which behavior
adapts to new settings and can develop its own type of
creativity, at that point I think we will have achieved
something spectacular. INTERVIEWER: So this has
been really wonderful. Is there anything else you’d
like to add that we haven’t talked about? LO: It’s been very complete
and wide-ranging. Social responsibility? INTERVIEWER: Sure. We have a few minutes,
if you’d like to– LO: The financial industry has
obviously undergone a great amount of turmoil over
the last few years. And it has received many black
eyes at this point in terms of the behavior that we
have observed. But I think that we are at the
risk now of throwing out the baby with the bath water when
we start to engage in regulations that will ultimately
hamper financial market development. Because ultimately, while there
may have been excesses and even crimes that were
committed during the financial crisis, the fact is that
financial markets are critical for all aspects of society, and
that some of the biggest challenges that we have
in front of us– things like cancer or global
warming or the energy crisis– ultimately, those are going to
be challenges that we will face collectively. And we’re only going to be able
to overcome them if we can unite in some coordinated
fashion. And one of the most important
aspects of that kind of unity is to be able to create
proper incentives. The financial system is designed
to do just that. I believe that to some of the
biggest challenges of society can be addressed with the
right kind of financial structures, and that they will
be virtually impossible to address without them. So we need to have more and
smarter individuals involved in the financial sector. We certainly should not
relegate that to the financiers because we know what
they might do with it, and we know that excesses
will occur. Instead, we need to spend more
resources studying these issues, becoming much more
nuanced in how we construct the appropriate financial
structures to be able to support innovation. And I think that if we can do
that, if we can find the right structures to create the
wisdom of crowds and to support the kind of innovation
that we desperately need, we can face virtually any
societal challenge successfully with those
kind structures.

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