And now for something completely different.
As a financial economist, I study financial markets
and risk.
And if you've been watching financial markets over the last
few weeks, you've probably heard about the so-called fear
index--
the VIX.
This is an index that measures a forward-looking perspective
on stock market volatility.
And it's actually based on research that was
done at MIT many years ago--
Black-Scholes-Merton option pricing formula, and inverting
that to calculate what the market thinks volatility
is going forward.
That work was done by our former dean
at Sloan, Dick Schmalensee, with a student, Robert Trippi.
And if you've been watching the VIX,
you'll notice something rather strange
over the last few weeks.
In particular, you'll notice that, for the most of 2017,
the VIX was somewhere around 10%--
10% market volatility.
And then, over the course of the first few days of February,
it shot up to about 37--
a really striking phenomenon that scared many, many people.
And if you go back and look at what happened to the VIX,
not just over the last year, but over the last decade
since the financial crisis, you see something much more
reminiscent of fear at the very heart of financial markets.
In fact, for you Lord of the Rings fans,
this should look a little bit like the landscape
for the land of Mordor.
All we're missing is the Eye of Sauron.
And investors react to fear in the obvious way.
If you look at the S&P 500, a measure of the stock market,
you'll notice that, at the very beginning
of this period of the financial crisis,
the stock market dropped dramatically
in response and in tandem with this increase in the fear
index.
In particular, if you were holding equities in the stock
market early in 2007, you would have been right around 1,500,
1,600 at the S&P. And within a matter of a few months,
your 401K would have become, roughly, a 201K.
You would have lost half your wealth.
And of course, investors reacted as we expect they might.
They freaked out.
And they pulled money out of the stock market,
missing the rebound that took about four years to get you
back to where you were.
And investors took more money out and, ultimately,
missed the great bull market that occurred since then.
This roller coaster ride is a problem
that investors, financial economists, practitioners
have been working on for many, many years.
And so we've been focusing on applying
artificial intelligence methods to try
to solve this problem about what an investor is supposed to do.
So to begin, we have to ask the question, what
do investors want?
And I'm going to ask all of you to think about that
in the very specific context of four financial investments
that I'm going to show you.
I'm not going to tell you what they are
or over what time period they span.
All I'm going to do is to show you
what happens to a $1 investment during this investment period
and ask you to pick one of these four investments.
The green line is a very safe investment.
It turns $1 into $2 over this unspecified investment period.
The red line is quite a bit more risky.
It turns $1 into about five.
The blue line is even more risky, but it's more rewarding.
And the yellow line is somewhere in the middle.
And if you could pick one, and only one, of these investments
for your retirement or for your kid's college education
or for your grandparents' funds, which would you choose?
By a show of hands, how many people
would pick the green line?
Nobody?
Wow.
OK, a couple of people.
How about the red line?
Anybody take the red line?
Wow.
I want you to remember this moment.
Because after I tell you what that is, most of you
are going to have some rethinking to do.
How about the blue line?
Anybody want it?
There are the venture capitalists and the hedge fund
managers.
[LAUGHTER]
And now, the yellow line-- how many people--
yeah-- by far the most popular, because it
seems to have the best trade-off between risk and reward.
Well, let me tell you what you picked.
First of all, the time period is from 1990 to 2008.
The green line is US treasury bills,
the safest asset in the world--
not very interesting from a return perspective.
And if you put it in 2008, you would have not
done particularly well, but you wouldn't have lost much.
The red line that most of you did not pick,
well, that's the S&P 500.
Most of you already have that in your portfolio,
so you better rethink that decision
given what you just said.
If you put your money in the S&P in 2008,
you would have done just fine.
You would have done quite well.
The blue line is the single pharmaceutical company Pfizer--
much more volatile, much more risky,
but, also, quite a bit more rewarding,
and you would have done well as well.
What about the yellow line-- the one that most of you
did pick-- the optimal trade-off between risk and reward?
Well, the yellow line is the returns to the Fairfield Sentry
Fund, which is a private fund that
was the feeder fund for the Bernie Madoff Ponzi scheme.
[LAUGHTER]
That's why I had to stop it at 2008.
Now you know how the Ponzi scheme got as big as it did.
It is absolutely innate human nature
for us to be drawn to investments
that are high-yielding, low risk investments.
In the finance parlance, we call that high Sharpe ratio
investments.
And we do this, sometimes, to our great detriment.
So that's what investors want.
What do investors need?
Well, it turns out that technology
has played a role in what we offer to investors.
A great revolution occurred in the 1970s
with the advent of index funds.
All sorts of indexes now exist that allow investors
to put money in various different assets
at relatively low cost to be able to capture
the broad returns of the market portfolio.
But over the course of the last few years--
particularly, the last few weeks--
we understand that that's not enough.
The future of investment technology,
thanks to AI and other forms of innovations,
have given us the possibility to create
what I call precision indexes, sort of like the personalized
medicine that you hear about nowadays,
being able to tailor a particular treatment
to an individual.
So instead of the Dow Jones 30 or the FTSE 100 or the S&P 500,
imagine creating the Rafael Reif 30 or the Rebecca Sachs
100 or the Daniela Rus 500.
And imagine using technology to tailor these indexes so
that they take into account things like your tax bracket,
your income level, your health, your age, your family--
all the various different hopes and dreams
that you want to accomplish over the course of your life.
And now imagine if you can automate all of that,
stick it into a black box, and put it on an app.
Well, that's fantastic.
But it doesn't exist.
And the question is, why not?
What's missing?
It turns out that it's not artificial intelligence.
We've got plenty of AI to be able to do this.
What's missing, in my view, is artificial stupidity.
We need to be able to model algorithmically
how investors actually behave, as opposed
to how we think they should behave.
And I think to call it artificial stupidity is
a little bit unkind.
I think it's really based on human nature.
We're reacting to threats--
fear and greed.
And so what we really need to do is
to develop artificial humanity.
And it turns out that the recent breakthroughs in AI
have given us a hint on how to go about doing that.
So let me give you one example that something
that I suspect all of you have been involved in.
A few years ago, I got interested
in the biomedical field.
And so I decided to purchase a book on the biotech industry.
And the best book that I knew of,
based on friends and families recommendation,
was a book about Genentech, one of the most successful
biotech companies in the history of the industry.
So I did what most of you will do.
I went to Amazon.
I looked for Genentech, and I clicked
Add to My Shopping Cart.
And as soon as I did that, Amazon
does this thing that I find incredibly annoying.
And you know what that is.
They tell me, well, people who bought your book bought
these other five.
And sure enough, I had to have two more of those books.
[LAUGHTER]
it's really nasty, nasty technology.
This is part of the new AI.
What Amazon does is something devilishly simple.
They simply take a look at their database of all the individuals
who purchase this book on Genentech,
and maybe they do something even more sophisticated
by stratifying based upon demographics
and try to compare people with my demographic
and then show me books that they bought.
The algorithm is really simple.
But the use of data is enormous.
And that's actually a very different way
of thinking about AI than we did in the 1970s and '80s.
Because in the early days of AI, while we had expert systems,
we had incredibly complicated algorithms
and virtually no data.
Because back then, storing data was a lot more expensive
than it is today.
And so the idea of focusing on using data
and detecting patterns using relatively simple algorithms
versus trying to figure out every possible use case you
would encounter in an expert system, that's
really what we do as humans.
So the current approach to AI is much closer
to human intelligence.
And I want to give you an example of that.
Because it's something that's really innate to us and makes
us make those decisions that we will later regret.
The example has to do with something that all of us
can do instinctively, which is threat identification--
friend or foe.
I'm going to give you an example that comes from a scene
that I suspect many of you have participated in,
which is a cocktail party.
You're at a cocktail party.
You're meeting lots of people.
And you're trying to figure out who's a friend and who's a foe.
And so at the course of the evening's conversation,
you will talk about various different kinds of things
and learn things about the other participants at this event.
For example, you'll learn about an individual's gender, perhaps
their sexual orientation.
And if you think that there are two major genders and two
major sexual orientations, that's
four possible identities for that individual
in that category.
You might find out about their race, ethnicity,
their age group, educational background,
and so on and so forth.
So over the course of the evening,
you'll learn various things about the individual
and put them into various buckets.
So I want to tell you about two particular individuals
that you might encounter at such a cocktail party.
And then I'm going to ask you to make decisions
about these individuals.
So I want to introduce you to Jose and Susan.
Jose is a gay Latino male.
He's a young professional from California--
no religious affiliation, Democrat, middle class,
with an MBA.
That's Jose.
Susan, on the other hand, is a middle-aged heterosexual white
female from Texas--
Christian, Republican, affluent, and with a bachelor's--
no MBA.
And so now that I've introduced you to Jose and Susan,
I'm going to ask you three questions about them.
And just tell me what you think in terms of how you would
make the following decision.
Imagine you're doing a startup, and you need to hire somebody
to help you with that startup.
Who would you rather hire--
Jose or Susan?
How many people would hire Jose for the startup?
OK, how about Susan?
All right.
Most of you would hire Jose for that startup.
Fine.
Second question-- you are organizing
a fundraiser for breast cancer, and you
need to hire somebody to help you plan that fundraiser.
Who would you hire--
Jose or Susan?
How many people would hire Jose?
OK, how many people would hire Susan?
OK, most of you would say Susan.
Fine.
Third question-- you're an auditor at the IRS,
and you're looking to try to find who's cheating
on his or her tax returns.
But you can only audit one of these two individuals.
Who would you audit--
Jose or Susan?
How many people would audit Jose?
OK, how about Susan?
Most of you picked Susan.
Wow.
That's amazing.
I can't believe how judgmental you people are.
[LAUGHTER]
Now, I know I asked you.
I was the one who asked you.
But you didn't hesitate to make a decision.
And it's because all of us are wired
to make these snap judgments.
From an evolutionary perspective,
that's what's kept us around for the last 100,000 years.
It's part of our human cognitive faculties
to make quick decisions.
And we do it the way Amazon does it.
This is machine learning via humans.
What we're doing is looking back in our database of all sorts
of experiences we've had in doing cancer fundraisers
or in doing startups and asking the question,
the people that were successful in those roles,
did they look more like Jose or more like Susan?
In fact, if you go through the different characteristics
that I listed on this page and you calculated
the number of different personality types
that you would be able to come to,
it turns out that there are about 350,000 unique categories
if you just do the combinatorics.
That's more pixels than in a 600 by 800 photograph.
The problem, though, is that our data set is very, very sparse.
Unlike Amazon's data set of people who bought books
on Genentech, how many people here have
met more than 345,600 people in their lifetimes?
Show of hands.
I actually met a marketing person who said yes, they did.
[LAUGHTER]
So most of our data is empty.
We don't have observations on a lot of these things.
And by the way, this is part of the problem with fake news.
It doesn't take a lot for me to change the entries
in your very sparse matrix of data that can completely
change how you behave.
And this is the challenge with financial decision making.
We have very sparse data about experiences of bull and bear
markets.
And we're influenced by very small things,
like stories about somebody who lost all their money
because they invested in the wrong stock or somebody who
made a ton of money because they happened
to pick the right stock at the right time.
And so what we're doing in the Laboratory
for Financial Engineering is to try to come up with algorithms
using large data sets that we've obtained from brokerage firms--
anonymized data sets of individual household accounts--
using machine learning to understand
how people make mistakes, how they freak out
at the wrong times, and what kinds
of financial strategies and products and services
can actually help them make better decisions,
so that, ultimately, we are going
to be able to have the algorithms to create precision
indexes.
Thank you.
[APPLAUSE]