Asset Pricing and Sports Betting

Asset Pricing and Sports Betting

“Economics Amplified,” the latest thinking on the
biggest issues from U Chicago’s Becker Friedman Institute. STEVEN D. LEVITT: I’ve
known Toby for a long time, and maybe the most
worrisome conversation I ever had with
Toby must have been about three or four years ago. So if you know
Toby’s academic work, he does asset pricing
and serious scholar. And he came to me and
he said, hey, you know, I know you wrote this
book Freakonomics. And I’ve decided
I’m going to write a popular book about sports. I said, OK. Great. That sounds good. And I was terrified. He said, and I’m going
to do all new research. I’m going to start from scratch
and I’m going to do all–and I thought, OK, that sounds
like a really bad idea. But I wasn’t going to say
anything, because everyone’s got their dream. And then I said, well, what
do you hope to get out of it? And he said, all I want is to
be able to go to the Super Bowl. And I thought, well,
that doesn’t seem like a very high aspiration. And I want to just
be at the Super Bowl. And I didn’t want
to tell Toby, there are other ways to
get to the Super Bowl besides writing
a bestselling book about the economic of sports. And then I said, well,
it’s hard to write well. And he said, well, I
got this other guy who’s going to write it. And he’s written three or
four bestselling books, and he writes for
Sports Illustrated. He’s my old doubles
partner from tennis. Then I got a little
more optimistic. And still, you
always dread it when people try to write popular
books when they’re academics. And then Toby sent me
his book, Scorecasting. And it was so good. Of all the books I’d
read by economists trying to do popular things,
it was by far the best. Beautifully written. Great research. And it’s fantastic. So today, we invited Toby. He said, well, what
should I talk about? Should I talk about my
work in asset pricing, or should I talk about
my work on sports? And I said, well, I
think for this audience, people would be
equally interested. So Toby decided once again
to try to confound us. And he says he’s going to talk
about both today, about sports and asset pricing. And he’s going to show
us how this going to be, in his own words, either
the best or the worst thing he’s ever done his entire life. We’ll find out soon. TOBIAS J. MOSKOWITZ: Thanks. [APPLAUSE] TOBIAS J. MOSKOWITZ:
Well, thanks, Steve, for that introduction. Last time Steve
spoke about me, it was in front of my alma
mater at Purdue University where he was talking about
his research on prostitutes and using me as
an example, which didn’t go all that well over
with my wife and my family. But in any case. So Steve’s right. I’m putting together these
two things, asset pricing and sports betting. Really, this is an
asset pricing paper more than it is a sports paper. And it’s certainly the
riskiest thing I’ve done. So we’ll see. It’s either going to be
really cool or really stupid, and we’ll find out shortly. Oh, right, right. So far, it’s not advancing. So we may not find out
what’s– that’s not– CREW: You can close that out. That’s the previous. TOBIAS J. MOSKOWITZ:
That’s the previous thing? Way over there. OK. There we go. So let me start
with– like I said, it’s an asset pricing paper. So let me start
by– you can hear me with the microphone’s on here? OK. I like to walk around. So two of the most studied asset
pricing phenomena out there are value and momentum. And those of you that have taken
some asset pricing courses, you’ve heard these. But let me just
define what they are. These are two things that
seem to predict returns in a very profound way. So the value effect
is really simple. Assets that have a high
fundamental value, whatever that is– we can define that. You can think of it
as a high book value, if we’re thinking of a
stock, like high earnings, relative to a market-based
measure of value like the price per
share of the stock– tend to predict
returns going forward. So if you buy low
earnings to price stocks and you sell high
earnings to price stocks, you tend to do very well
on average over time. DFS has made, what is it? They’ve got $320 billion under
management using that strategy. Pretty much, that’s
their main strategy. Then there’s the
momentum effect, which is assets that
have done relatively well in the past continue to
outperform on a relative basis. So very simply, you can
rank stocks, for instance, over the last year on
their past performance and go along the ones that have
done relatively well, short the ones or underweight
the ones that have been relatively poorly, and
that does very well over time. And just to show you how
ubiquitous these effects are, this is related to a paper I
wrote a few years ago called, very simply, “Value and
Momentum Everywhere.” That pretty much describes
all we did in this paper. If you apply that very simple
concept, buy value assets and buy momentum assets, and
under-weigh or short sell non-value and
non-momentum type assets, you can see in US stocks that
momentum effect here is in red, value’s in blue. Cumulatively, the returns
are quite positive. Much better for momentum
than value over this period. And that’s true. In the UK, you’ve got
the same sort of picture. In Europe and in Japan, you’ve
got pretty similar pictures. But there’s some variation. You’ll notice, for instance,
that in Japan, value– which is the blue
line– has done incredibly well historically. Momentum, not so much. In Europe, it’s kind
of the opposite. Momentum has been
terrific, value’s been OK. And same in the US. But I want you focus on
the green line as well. Because as you know when
you build portfolios, you don’t think of these
things as individuals. You think of them
as in combination in a broader portfolio. When you combine
value and momentum, you get this tremendously
profitable green line here that’s also very smooth. So it’s not just the height
of the line that matters here. Notice the number of wiggles. That expresses
volatility or risk. You can see, for instance,
here’s a great example. Go back to the height
of the tech episode. I won’t use the
word “bubble” here. But whatever you
want to call it. The tech bubble, tech episode. You can see that that was a
time when value strategies did very poorly. And why was that? Well, again, value says
you buy assets that have lower earnings to price. Well, during this time,
assets with very high earnings to price kept getting
better and better. So it was a terrible time
to be a value investor, but it was a great time
to be a momentum investor. And if you combine
the two, you barely notice that episode
even existed. Now, that seems to be
true in every market here. Here we’ve got
the US, in the UK, in Europe, and even in Japan. In Japan, the green line,
even though value’s great and momentum is
basically nonexistent, the green line is still way
better than the blue line. Now, you don’t have to
stick to stocks here. This is just stocks
internationally. If you do the same thing for
equity futures contracts, for currencies, for
bonds, government bonds, and even for commodities, you
get a very similar picture. And one of the things
that we show in this study is that when you see
these wiggles here, those wiggles tend to
occur at the same time in very different markets and
very different asset classes. More formally,
what that means is there’s correlation
structure among these things. That there’s a value
affect in markets, there’s a momentum
affect in markets, and they pervade all
sorts of markets. And they tend to
do the same things in those different
markets at the same time. Now, we have a big debate as
to what these things mean. First of all, when you look
at this evidence, you say, gee, what a great
trading strategy. Maybe I’ll go out there
and make tons of money. And certainly, some
firms have done that. But there’s a debate as
to what this all means. And by the way, if you do this
globally in all asset classes, all the pictures
become a lot smoother. But you can see right here just
how smooth that green line is. So there’s a big debate
as to what drives this. One view is that this
is all driven by risk. That all we’re really
picking up here are different risk
premia in the economy. That risk isn’t just about
equity risk or market beta, it’s about exposure to value. It’s about exposure to momentum. You can think of Gene Fama. That’s his camp. And there’s lots of flavors
of these risk-based stories. But somehow, value and momentum
are compensating investors because they’re exposing
them to more risk. Then you’ve got the
other side of that coin, the behavioral models, which
say these things aren’t risks at all. They’re driven by mispricing. That they really are
profit opportunities without that additional risk. And that’s where Bob
Schiller comes in. And hopefully, you
know– you certainly ought to know Fama’s name. And I hope you know
Schiller’s name as well. These two won the
Nobel Prize this year, 2013, for these kinds
of predictable patterns and returns, even though
they fundamentally disagree on what drives those patterns. So we all agree with the data. We just disagree
with the explanation. Now the risk models have lots
and lots of different stories often involving some exposure
to aggregate macroeconomic risk that’s related to these value
and momentum characteristics. Behavioral models typically
also have a lot of varieties, but typically of
one of two kinds. One is it’s a misreaction
to information, typically either overreaction
or under-reaction. And just as frustrating as
the risk-based stories are, we have models that say
overreaction drives it, models that say
under-reaction drives it. You might wonder how could
both of those be true? Well, that is a
bit of a problem. But can generate models that
can explain these patterns. Now, the real question
is how can we distinguish between these two camps? Or can we even
distinguish between them? It’s almost impossible
using financial market data because of what we call the
joint hypothesis problem, which is really one of the
most famous things that Fama contributed
to in the literature, is any model of
market, any explanation for market
efficiency, implicitly relies on a model for
prices or returns. So in other words, I
can’t tell whether markets are inefficient because of these
value and momentum effects, or whether I’ve got
the wrong model. And that’s why there’s a
host of risk-based models that try to fill that gap. But same with the
behavioral models. We have to take a stand
on what the model is. We don’t just get to conclude
or claim the residual, that since we don’t have a model
that explains all these facts, it must therefore be behavioral. We need some models to do both. So how can we test these models? OK This gets me to sports betting. So what’s the basic idea here? So I’m going to tell
a quick outline. I’m going to talk
about the basic idea and why I want to look at
sports betting markets to try to help resolve this conundrum. As you can see, it’s an asset
pricing paper, not really a sports paper. So 3/4 of you may want
to get up and leave, but I hope you won’t. Then I want to link this
back to financial markets. What can we learn
about financial markets from the sports betting market
that maybe can shed light on breaking this logjam between
behavioral and risk-based explanations? And then I’ll talk about the
tests that I’ve run so far and what we see in the data. And by the way, I should mention
I ran a lot of these things as of late this weekend. So this is certainly ongoing
and preliminary research. But that’s why we’re at Chicago. So here’s the basic idea. I want to look at a
market or a set of gambles where some explanations
can’t possibly matter. In particular,
I’m going to focus on sports betting markets. And the reason I chose those
markets– well, one of them is data reasons, but I’ll
get to that in a second– but the first reason
is that aggregate risk or macroeconomic
type stories related to Fama’s view of the world
can’t possibly matter here. These are purely
idiosyncratic gambles. There’s no notion of
macroeconomic risk affecting one game versus another. And I want to be clear here. Macroeconomics can affect the
betting industry as a whole, but they cannot have anything
to say about whether one particular game over this
weekend is priced differently than another particular game. That just can’t possibly matter. So the key here is I’m looking
at the cross-section of games. Now, the behavioral
explanations, they should apply equally
as well to these gambles as they do to financial markets. The behavioral
explanations are really about how do investors
as human beings react to information,
to uncertainty, and how do they
use probabilities to estimate expectations,
and all that stuff. All the evidence from
psychology points to experiments in the lab
that aren’t really related to financial markets. There’s simple risky gambles. So there’s no reason
that those explanations shouldn’t matter here as well. So the question is, can
we find the same patterns in this market where aggregate
risk can’t possibly matter, and would that lend
credence to some of these behavioral stories
that suggest the same things are going on in financial markets? There’s two key
features of this market that are important
for what I want to do. One is what I just mentioned. The bets are purely
idiosyncratic. So we can take the risk-based
stories off the table. The second thing,
which is kind of nice, is that the betting markets have
a finite and often very short terminal date where all
uncertainty is resolved. So what you’ll see in the
financial markets literature, when behavioral
explanations are tested, often researchers will assume
that there’s some finite period where prices get corrected. For instance, you might look
at an earnings announcement and say, well, six
months down the road, we figure all that information’s
embedded in the price. So we look at price
patterns until then. We like to pretend that we have
a real terminal value then, when in fact we don’t because
the stock could be going on forever. Could be a perpetuity. Here, it’s really nice. You have a game that ends. The payouts are determined
on the betting contracts. And that’s it. The other nice feature
is whatever investors are doing in betting markets
has absolutely no implications for the outcome of the game. I mean, unless you really
believe in conspiracy theories and game fixing. But in the sports that I’m
going to be looking at, that’s probably not going on. So it’s really
exogenous to the things that I want to look at,
to the betting behavior that I’m going to look at. Now, I’m going to run
these tests in a second. But I also want to think
about how I can link this back to financial markets. Can I really learn
something from looking at sports betting
markets that can tell me what I want to know
about financial markets, and in particular
value and momentum. I’ll look at size
as well and other of these cross-sectional
asset pricing effects. Well, first of all,
you could argue that sports markets
are just fundamentally different than
financial markets. In fact, Steve has
a paper on this. There are some similarities
and some differences. But for my purposes,
the differences are going to be important
in highlighting the theories that I want to test,
and the similarities are going to be
important, I think, for generalizing, perhaps,
to financial markets. For instance, if you
look at investors in sports betting markets,
there’s a lot of uncertainty. And most investors there,
yes, some of them bet for fun. But even when they bet for
fun, they still want to win. They still want to make money. It’s not like the incentives are
that different in that market. What’s different, of
course, is what I mentioned. They’re idiosyncratic and
there’s a short terminal date. But there’s also a third thing
that’s a little bit unknown here that may make it difficult
to generalize the results, or at least make me cautious
in generalizing the results, is that institutional
frictions, or for another way of saying this is
arbitrage activity, may be quite different
in these markets. So for instance, in
the financial markets, if there’s a mispricing
opportunity out there, we often view this as
Fama’s view of the world, that there is smart money ready
to take action and correct prices very, very quickly. In the sports betting
market, that’s true as well. There are professional gamblers. There are institutions. There are even hedge funds
that do this as well. But there’s a deeper
question as to how well does that work in sports betting
markets versus how well does it work in financial markets? That’s a question that I don’t
really know the answer to. So is that going to make
it difficult to generalize the results? Perhaps. For instance, suppose
I found nothing in sports betting markets
related to value or momentum or any of those things. Would that necessarily imply
that the behavioral models can’t possibly explain
anything in financial markets? No. Surely not. It could just be the case that
in sports betting markets, those things are
arbitraged away much faster than in financial markets. I would probably
think the opposite, but it’s certainly possible. On the other hand, if I find
something positive in sports betting markets,
does that necessarily mean that’s what’s going
on in financial markets? Not necessarily, but it would
be an awful coincidence. For instance, the key thing
that I’m going to look at here is I want to be
careful and look at the exact same characteristics
from financial markets. In other words, if
I just said, I just want to see how
sports betting markets react to any general
piece of information and say something about
financial markets, that’s much harder. Like for instance,
suppose I just took some random characteristic
in sports betting markets that really didn’t have anything
analogous to financial markets. I don’t really learn
much from doing that. What I want to do
specifically here is to find the exact
same– or as close as I can– the exact
same things that we see in financial markets
like momentum and value– I have to define those
for you in a second– and see if those same
predictors that we know work in financial markets also
work in sports betting markets. That’s the key. Because if I don’t have that
analogous characteristic, then I have to worry about the
generalizability of this stuff. Whereas here if I show you
that the exact same patterns, the exact same measures
used in financial markets work just as well in
sports betting markets, either you conclude
that maybe there is something useful in
these behavioral theories to explain the
patterns that we see or it’s just a really
lucky coincidence. And when I say really
lucky, I’m going to be looking at four
different sports markets, different times, and
three different contracts for each sports market. It’s a total of 12, essentially,
out of sample tests. It would be a very
lucky coincidence if that showed up in
those 12 independent tests and had no real content. So summing up, and then
I’ll get to the test and the results, if I
find a positive result, I think it can tell us
something about what’s going on in financial markets. If I find a negative
result, I think that’s a little bit less useful. In other words, if I find that
the characteristics that we look at in financial markets
don’t apply at all in sports betting markets,
those may just be the wrong– it
doesn’t necessarily mean that the risk-based
stories are right and the behavioral models
can’t matter at all. Maybe it moves your
prior a little bit. But for me at least, I
think my prior moves more if I find a positive
result because these are the exact same
characteristics that we already know work in financial markets. That’s a little bit
subtle, and I’m not sure I’ve got the logic
right, by the way, but that’s my current
thinking on it. But that’s the way
I’m approaching this. So I think the positive
results are more generalizable, negative results less so. But I still think it
might move people’s priors into one camp or the other. But here’s the problem. The better this test
is depends critically on how analogous I can
define these characteristics. Momentum’s kind of easy. It’s just past
performance, right? Seems like I ought to be able
to define that pretty easily in the sports betting context,
and I think that’s right. Value, which is a measure of
fundamental value to market value, that’s going
to be a little harder. I’m going to have to
do more work there. And I’m not sure I’m going to
convince people in this room that I’ve got it. But I’m working on it. That’s the idea. Something like size, which is
the third characteristic people often mention that
I glossed over, that’s a little easier as well. But I’ll get to that. So let me start with the data. First of all, the data I
have comes from two sources, SportsDirect and SportsInsight. I can tell you all the
details about these. They’re basically online
betting resources for betters, and they contain
historical spreads. SportsDirect has
spread contracts only, which I’ll tell you what
those are in a second. SportsInsight has the
spread, money line, and over under contracts. And for those of you
that don’t know anything about the betting markets, I’ll
come to this in just a second. Just to give you a
sense, though, there’s a 99.99 correlation
when the data overlaps. So the spreads,
the prices that are given from these
different book makers– by the way, the bet
lines come from Vegas. And the three top
online book makers, there’s very little
variation across them. I think there can be during
the week for certain bets, but I’m going to focus on
only three prices here. The opening line,
the closing line. So the opening line is
when betting starts. Let’s say it’s an NFL game. Monday morning, betting starts. And that betting continues
until right up until kick-off. That would be the closing line. And then you have the outcome. So if I’m just looking
at these prices, there’s really not
much variation across these different book makers. So just to give you a sense
of the three contracts I’m going to look at, it’s
little betting 101 here. The spread contract is
probably the one most people have heard of. You bet for a given team to win
by a certain amount of points. For instance, if I’m
betting on the home team and they’re a 3 and
1/2 point favorite, it means they’ve got to
win by more than 3 points. They’ve got to win
by 4 points or more for me to cover the bet. And what does the bet entail? It entails me putting $110 down. If they win, I win $100. That’s called covering. If they lose, I
lose my bet, $110. And if I push– in other
words, if they win exactly by 3 and 1/2 points– which is
impossible if it’s a half point spread– then I break even. So that only works for
full point spreads. Then there’s the money
line bet which is again a bet on who wins or
who loses, but instead of adjusting by the points by
which a team might win or lose, it’s a levered and a un-levered
on either side of the contract on how much you win. So for instance, the money line
would be quoted, let’s say, negative 180. That means you’re betting
on the favorite team $180 to only win $100. And if you want to take
the other side of that bet, the quote might be
plus $170, meaning you would bet $100 to win $170. So does everyone see here? Up here, essentially
what happens is the book makers are
adjusting the number of points by which the team
would win in order to make the betting odds roughly
50-50 on who wins or who loses. Not quite, as some of
Steve’s research has shown. The money line
basically says forget about adjusting the points. I’m just going to
adjust the payoffs. In other words, I
understand that this team is more likely to
win, so you’re going to win less money if they do. Whereas this team
is an underdog, you have a chance to win
a lot more percentage-wise if you bet on that team. So there are two bets. They’re going to be correlated. The payoffs on
these are about 0.69 correlated, which
makes some sense. And then finally, we’ve
got the over under, which is not betting
on a particular team, just betting on the total score. So for instance, say for an
NBA game, if the over under is 175, if I bet the over, that
means I win if both teams score more than 175 points combined. I lose if they score
less than that. And the payoffs are very
similar to the spread contract. I bet $110 to win $100. Now, that extra $10 or 10% is
known as the vig or vigorish, which is a trading cost. In the money line, you
can see it expressed more in the difference. You notice these prices
are typically not symmetric when you look at
the money line contracts. That’s a way for the
book maker to make their spread, their commission. So let me talk about
the data I have. I’ve got basically
14 years of data in the NBA, roughly 19,000
games or about 39,000 betting contracts. Because especially
post-2005, there are three contracts per game. The spread, the money
line, and the over under. For the NFL, I’ve got betting
contracts back to 1985 through 2013. It’s about 7,000 games
and about a little under 11,000 betting contracts. And again, it’s not
exactly three times because prior to 2005, I only
have the spread contract, just to make that clear. For baseball, it’s 2005 to
2013, about 24,000 games, and about 48,000
betting contracts. Just to be clear here, notice
I’ve got the summary statistics on each of the three
contracts– spread, money line, and over under. You’ll notice in
baseball, the spread is always negative 1 and 1/2. It’s called a run line. It’s just not useful. Basically, who
wins and who loses. So the spread contracts
aren’t used in baseball. The scores are simply too low. There’s just a money line
bet and an over under. Same in hockey. There’s no real spread contract. It’s called the puck line. And it’s just a bet on
who wins or who loses, so it’s really the money
line and the over under that matter here. And again, for hockey,
about 10,000 games, about 20,000 betting contracts. So overall, there’s
120,000 gambles here, if you add up all the
contracts and all the sports. What’s nice is
they’re independent. They’re purely
idiosyncratic, not only to the market or
anything we care about in the macro-economy,
but also really to each other. It’s certainly across sports. Even within a sport,
it’s very rare that one game would
influence another game. Maybe a very rare
situation where someone plays in the morning,
has playoff implications for the team in the
afternoon, and then they rest their starters. But even so, that’s
extremely rare. Now, there’s a whole
bunch of things I need to do to convert
these payoffs into what we would consider financial
returns or price returns. So I’m not going to go
through the math on this. I have a paper that I’ll put
online that people can look at. But basically, all
I’m trying to do is back out what the
probabilities are of the different payoffs, what
that means for prices based on the way these
contracts pay off, and I’ve got to estimate
those probabilities somehow. So just to give you a quick
flavor of what I do here, here’s an example. This is just for the spread
contract, the closing spread. This is just for the NBA. The black marks here are
the actual probability of winning the bet
at various spreads. And the thickness of
them determines how many contracts are at that spread. So you can see for
spreads that are way negative or way positive,
there’s very few contracts. But in the middle
here, you have a lot. And then I’ve got a model of
what these probabilities are which is the red line
is a theoretical model. That just says, suppose
all bets are fair. 50-50. 50% are going to win,
50% are going to lose. As Steven Levitt’s research
shows, it’s not quite true. But that’s pretty
close to the truth because the blue line
here is basically a non-parametric
estimator of all this data to take into account what
book makers are actually doing when they set spreads. And it’s pretty
damn close to 50-50. Why do I have to do that? Well, you can see
here there’s so few contracts at the extremes. For instance, look way up here. You can barely see it. There’s a contract here
that at negative 14 and 1/2 point spread, those
contracts all paid off. But at positive 15– here it
is– none of them paid off. Well, that would
be a little crazy to assume that those
were real probabilities. So you have to use some model. You can get more sophisticated
and use logit and probit models and other things. But basically, it’s
what we try to do. We have to build a
mixture of model and data to estimate what these
things really look like. Now, it turns out– I’m
going to go back a second– the correlations across all
the different methods– there are four different ones. A theoretical approach, a
non-parametric approach, a logit, and a
probit type model. The correlations are high 99%
across these different methods. So it’s capturing the bulk of
the data no matter what I do. Let me move on. Real quick, once I calculate
all those returns for all those betting contracts, suppose
I was just going to compare, let’s say, what $1
bet would look like, bet in the stock
market, versus $1 bet in sports betting markets. It’s not quite a fair comparison
because in sports betting markets, there’s a
winner and loser here, whereas if I hold the market,
that’s $1 long investment. But you can see here
that here’s the stock market in blue over time. Here’s if I just bet on the
home team, bet on the favorite, or just bet on the over, I
typically have close to a 0 or even slightly
negative return. But more importantly, the
wiggles in these lines are completely unrelated. These are idiosyncratic. Another way of
saying this is they are devoid of any macroeconomic
or market conditions. The correlation between
the stock market and any of these bets
is essentially 0. So let me get to
the actual test. And I’ve got about 20 minutes,
I think, to get through this. But let’s see how far we get. Once I explain the tests,
the tests themselves are pretty straightforward. I’m going to be looking at
three horizons of returns. Here’s just a simple timeline
of what I’m going to do. Book makers set an opening
price on all these contracts and all these sports. Let’s say it’s the
spread, for instance. But whatever. I’ve converting
these all to prices. So they set a price for the bet. Then betting continues,
or betting takes place. And that betting takes place
right up until the game starts, which is the closing price. So I want to look at the return
from the opening to the close. And then once the betting
stops here at the close, the game takes place
and there’s an outcome. And that determines the
final payoffs on the bet. And so there’s a return here
from the close to the end as well. And then I can also look
at the return from the open all the way to the close. If I made a bet here on
the open and then held it, that would be my
total return, which is going to be the sum of these
two over those two horizons. Now, the behavioral
models I’m going to test have implications
for the patterns of these different returns. Let’s just think about it
intuitively for a second. So the first question
you ought to ask is why do prices move at all. Well, if there’s
new information, prices would move, for instance. Suppose a key
player gets injured. So you thought that the
spread out to be three points. Suddenly, Peyton
Manning’s not playing. The spread’s now going to drop. So if it’s information,
what would that imply? Well, that would imply you’d
get movement from here to here. But if markets are efficient
in the way Fama coined, then there’s no predictability
from this closing price to the final game outcome. If Peyton Manning’s
hurt, prices will adjust. And then there’s
no predictability. So what we would
see in that case is some return, positive or
negative, between P0 and P1, and then 0 on average
from P1 and beyond. Now, contrast that to
a pure noise story. Suppose prices move from here to
here because people are stupid. That there’s no
information content in it, but they get excited
about a team. They like the color
of the jersey. Whatever it might be, it
has no information content. Then when prices move
from here to here, whether it’s up or down,
what’s going to happen? Well, if prices move for
non-information reasons, they’ve got to get corrected
by the time the game ends, because the game
outcome is not affected at all by betting behavior. And so for instance, if people
get too excited about a team and they push this
price too high, then that’s going to predict
a negative return from here to here on average. Or if it pushes it too low, it’s
got to be a positive return. So to formalize
this– oh, sorry. I had a little graphic here. True prices are revealed. So the mispricing is
corrected if there is any. To operationalize this,
I’m going to run two really simple regressions. If I regress the total return
over the life of the contract from the open to the end on
any movement from the open to close, and then if I do
the same thing from the close to end return on the movement
from the open to close, an information story would
predict the following. That beta zero should be 1
and beta one should be 0. In other words, all of
the return information is coming from the
movement to open to close if it’s
just information that’s being released. And then there’s no
predictability after that. So that’ll be 1. These two things will
essentially be the same. And that’s got to be 0. A pure noise story
predicts the opposite. It says that nothing
actually changed. There was no information
content in prices being moved. People were just
doing it for whatever. Schiller calls it animal spirits
or irrational exuberance. Whatever it is, prices
are going to move but they have no information
content, meaning that there’s going to be a 0
beta for beta zero here, that the open to
close movement is not going to affect the
open to end return because that stays constant. But in order for
that to be true, it has to be the case that
whatever movement happens between the open
to the close, it’s got to be fully corrected
by the close to end. In other words, beta one
has got to be negative 1. This is why I got
excited about this. It’s such a simple
test, and it’s got such opposite implications. I can really see
whether this is 1 and 0 versus 0 and negative 1. Now, notice this has nothing
to do with value or size or momentum yet. I’ll get to that
in just a second. There’s actually a third,
more complicated story, which is, I think, really
what the behavioral models are about, which is the following. Information moves prices,
but not fully rationally. There’s a misreaction
to that information. And here’s where it
gets a little tricky, but I’ll try to make
it pretty simple. Suppose that there is real
information coming out– Peyton Manning got hurt–
but that the market misreads to that. In other words, Peyton
Manning getting hurt should cost the Broncos,
let’s say, 4 points. But the market says,
eh, it’s only 2, because there’s a little
bit of under-reaction or conservativism to it. And then that
slowly trickles out. Well, if that’s the case,
you’ll get the following. First of all, the movement
in prices from open to close will be informative, but it
will continue to be informative. Another way of thinking
about under-reaction is that we’re getting closer
and closer to the truth, but prices keep getting
updated in the same direction. So all the betas are moving
in the same direction. It also means, though,
that the open to end return will have a
beta greater than 1 and this thing will be positive. Because remember, this
return is the sum of this and the sum of this. Another possibility,
though, is that people don’t under-react to the news of
Peyton Manning getting injured. They overreact to it,
meaning they thought it should be a 4 point
change in the spread, but actually
investors overreact. It’s a 6 point spread. In that case, you
get the opposite, which is this beta is going
to be between 0 and 1, and beta one’s going
to be negative. In other words, there’s
some offsetting here. Just to make it
really simple, all that’s going on– if I
go back to this graph– is under-reaction would
say prices move up and then they
continue to move up. They don’t move up
far enough here. Overreaction would
say prices move up, but they move up
too far, so they’ve got to come down a little bit. So it’s not as strong
as 1 or negative 1, but it should be
positive and continue to be positive, or
positive and then negative. Now, how does momentum, value,
and size fit into all these? Oh, so first of all, let me
start with the first test. Forgetting about
momentum, value, and size, suppose I just ran
this regression for all sports contracts. In other words,
all I’m asking here is on average, when price is
moved from the open to close, does it have any
predictive content? And if so, is it positive
or is it negative? Is it consistent with
under or overreaction? Well, here are the beta
zeroes and here are the beta ones for the NBA,
NFL, baseball, and the NHL. And you can see that
the beta zeroes are just about all positive. Sometimes here in the NHL
for the over under contract, it’s 0. But basically, they’re all
positive and significantly less than 1 except for
right here, which I’m going to look into, actually. I just noticed that. So the rest of these are
all quite a bit less than 1. And you can reject that
they are different from 1. And consistent with that,
when they’re less than 1, the beta ones are negative,
meaning that you’re getting this counter-reaction. So that’s very consistent
with the overreaction story. Now, I’ve never had a paper
where it works everywhere all the time perfectly. But on average, that
seems to be the case. And that’s true for both
the spread, the money line less so, and the
over under contract. What’s interesting, by the
way, about the money line contract is anecdotally,
at least, what I’m told is that that’s a market where
mostly professional betters play in, that your average
retail local better who’s betting not for their
career typically does the spread or the
over under contract. So it’s interesting–
you’re going to see this
throughout– the results are always stronger for the
spread and over under contract than they are for
the money line. I’m not sure if that’s
the right reason, but that’s certainly
consistent with that story. Panel B here just
does the same thing, but conditional on a price move. I’m just throwing out all the
times where the spread never moves. Because there are
lots of games where the opening and the closing
price are identical. But if I throw those out, I
get actually sharper results. So what does this tell you? Well, this tells you on average,
at least in these four sports, that when there’s price
movement, a good deal of the time it gets reversed. That’s interesting. Does it have to do with
value and momentum? Well not yet. So how do I do that? Well, now in order
to incorporate the financial market
anomalies that I’m really interested
in testing, I want to see if that reversal
effect that we see on average for a lot of these
contracts, is it at all related to
value, momentum, or size, for that matter? And the way I want to do this
is, the way to think about it, is there’s movement from the
open to close that we know is generating some predictability. Is that movement at all related
to past performance of the team or any of these
other value measures that I could come up with? So what I’m going
to essentially do is run a regression of the open
to close return on whatever characteristic I’m
interested in, whether it’s momentum or value. You can think of
this– those of you that know what an instrumental
variable is– this is basically an instrumental variable,
say momentum, for the price movement in the
betting contract. And then I want to see
if the movement caused by that characteristic
has the patterns either consistent
or inconsistent with the under-reaction or
overreaction type stories. Now, it could very
well be the case that sports betters
don’t care at all about these characteristics. Maybe prices don’t move at
all for value, momentum, size. If that’s the case, then
that’s the end of the story. And that’s the end of the
paper, that the financial market anomalies don’t really have
any relation here to the sports betting market. But if I can get this first
stage to work strongly, then I’m interested in
seeing what the patterns look like after that. And there’s all kinds
of possibilities. Could be that yes
indeed, past performance impacts betting behavior
which moves the open to close, but that has no predictive
content for the close to end, meaning that maybe there
really is information embedded in momentum. And maybe book makers aren’t the
ones being perfectly rational. So there’s lots of
possibilities here. So now let’s talk about
what measures I can use that can draw analogies
to financial markets. Momentum, as it’s
operationalized in financial markets,
is really simple. It’s typically the past 12 month
return, the one year return, on any asset. This is what we use for
currencies, commodities, stocks, futures contracts. It’s just a past performance
measure, the past return on any investment. That seems pretty
easy to define. And by the way, there’s
different kinds of momentum. That’s what I would
call price momentum. There’s also something known as
fundamental momentum, which is, say for a stock, it’s not just
the past price performance but the past
earnings performance relative to some consensus
analyst forecast. We could define the
same things in sports. Momentum can just be short
term past performance. A year is probably too long. But some measure
of past performance over the last certain
percentage of games or certain number of games. That could be in terms of
who wins and who loses. It could be in terms of points
scored and score differential. Or to make a direct analogy
to the financial markets, I could actually look at
the actual past returns on betting on this
team in sports markets. That’s like betting
on IBM’s share price. Here, I’m betting on the
Cowboys covering the spread in their last eight games. I can do that here as well. So what I’m going
to do is play around with all different kinds of
measures at various horizons, because theory doesn’t tell
us what horizon to look at. But very much similar to what’s
been done in financial markets. And then take an average
of all those things. I can compute a
composite momentum index, because hopefully these
things are correlated. Hopefully they’re all picking
up the same phenomenon. And then we’ll see what happens. Another thing I’ve done–
you worry about data mining– is I chose these measures
using alternate years of the different
contracts, and then tried to test them on, say,
on even years, then test it on odd years, and vice versa. Because there’s always a
danger that if I stood up here and said, well, the only
one you really want to look at is the past four game
measure, nothing else works, you’d be concerned I
just cherry picked that. So trying not to do that. I’m going to try to present
as many things as possible. And in the paper, I’ve got these
massive tables that do that. But that’s the idea here. For value, it gets a lot harder. How do I measure fundamental
value relative to market value? Well, this is also a
problem, by the way, when we go outside of
the equity world, when we do this in financial markets. For instance, like
in commodities. What’s a natural fundamental
measure of a commodity? There’s not really
a good measure. So what people often
use is a long term past performance measure. This is actually Dupont and
Thaler created this in 1985. The idea, say for
a commodity, is look at what the average
price was five years ago relative to the price today. And that’s a measure of a
long term fundamental measure relative to today’s
circumstances. That tells you whether something
looks cheap or expensive. Cliff Asness at AQR,
they use this a lot. He often refers to this
as the poor man’s value. When you’ve got
nothing else to use, long term past return
measures are pretty good. Just to give you a sense, if I
use, say, a five year long term past return measure and form
portfolios and equities, versus, say, like the
book to market or earnings to price ratio, the correlation
between those two portfolios is about 0.86 globally. So it is picking up a
lot of the same stuff. So I can define past
performance over longer periods. I can also look for this idea of
book value or fundamental value relative to market value
by doing the following. I’ll pick my favorite here. My favorite– not that
it works that well– but my favorite one is
take player payroll. That is, a team that on paper
looks better than another team. And why is payroll a good one? Well, if you think that
the labor market is pretty efficient here, then
you’ve got to expect the ones that are paying
their players more often have a better team. But look at that
relative to what the market currently
thinks in terms of the spread on the game
they’re about to play. That would encompass other
information that maybe matters. So here’s the idea. Two teams, let’s say. One has a much bigger
payroll than the other. But let’s say the
spread is really narrow. That would look
like a cheap bet. You don’t have to pay
much to actually bet on the better team, because they
don’t have to win by that much. That’s the idea. And you can do the same thing
not just with player payroll, but revenue, franchise value,
all these other things. And then size is pretty simple. Just the size of the
market would work or the size of the franchise. So let me get to the results. By the way, before I use these,
I have– for each of these, I tried to come up with
dozens of different measures and take average of
all these measures. But before anyone
saw the results, I asked Fama and Thaler what
they thought of these measures. Now, both of them–
especially Dick was funny. Dick said, I want to know
what the results are first. I said, no, no. You don’t get to do that. So I said, look. You’re welcome to say
to me, I don’t think any of these measures are good. They’re crap and I don’t think
we’ll learn anything from this. That’s fine. But you’re not allowed
to bitch ex post if you don’t like the results. So I sent these to Fama. His response was the following. “Most of these
makes sense to me. I like past team record,
longer term for value, shorter term for momentum. But the rest seemed OK.” And the reason he’s
saying that is we use these same measures
in financial markets and they do a decent job. Thaler was a bit funnier. Momentum’s easier, he agreed. And I would agree as well. For value, he conceded he
has to like the long term past performance because
it’s his measure with Dupont. So he really can’t
argue with that one. So when I want all
these regressions– and I’m going to
wrap up here, leave some Q&A– this is an average
of the t statistics of all the coefficients that I find
on all the different momentum regressors. So I haven’t cherry
picked the best ones. Some work much
better than others. But I don’t have a
good theoretical reason to believe why they should. It’s just really an average of
all those different momentum measures. Oh, and I should be clear here. When two teams are playing each
other for the spread or money line, the momentum
measure is the difference in momentum measures
between the two teams. That’s the idea. For the over under, it’s
the sum of those measures, because it’s the total points. So just to make sure we’re
measuring this right. But here’s what’s interesting. It’s hard to read, I’m sorry. The blue line is
the spread contract. The money line is red. And the green is over under. And these are the
momentum beta patterns. So this is just looking
at momentum for open to end returns, open to
close, and close to end. And what’s really
interesting is you find this pretty strong
pattern that momentum predicts positive price
movement from open to end to open to close. In other words, when
the betting line opens, people chase returns and
they push up the price. Very much like the
behavioral theories suggest is occurring
in financial markets. Perhaps more
interestingly, it reverses by the time the game ends. In other words, there’s
no information content in chasing returns. The game outcome is
unrelated to that. So what that means is these
contracts are overpriced right here and that the
true return would’ve been just betting at the open. Your return is essentially flat. That’s also true, by the way,
for the over under contract. And again, I want
to emphasize this. The over under
contract is completely uncorrelated to the spread
contract on the same game. One is a bet on who
wins by how much. The other is a bet by
the total points scored. And I know sports enthusiasts
think that offensive teams are more likely to win if
there’s more points scored and defensive teams
are more likely to win if there’s fewer points scored. There’s no evidence
of that in the data that I’m looking at here. That’s true in the NBA. And then look at the NFL. Completely independent
sample period and completely
independent contracts, different sport,
similar pattern. By the way, notice the money
line again, not much going on. So it doesn’t work
for the money line. Even though the money line
is pretty highly correlated with the spread contract, it’s
different on this dimension. For the NFL, pretty
similar picture. The spread contract has
this up and down movement which is very consistent
with an overreaction story of chasing returns. Over under, a little
less so, but still there. The money line, not much. And then for
baseball and the NHL, notice the money line– there’s
no spread contract here– you get a very similar pattern. But just to be cautious
here, the t statistics here are pretty strong
for the NBA and NFL. Much weaker for
baseball and the NHL, though still significant
for baseball. NHL’s weird. There’s not a lot going
on, although the pattern looks very much the same. And then we can flip
this over to value. Value has the opposite–
not always opposite– but somewhat opposite pattern. So assets that look cheap–
that’s what you do for value. You go long, cheap assets,
you short expensive ones. They get cheaper between
the open to close, but they get too cheap. And so there’s predictability
when the game resumes. That’s also true in the
NFL, baseball, and NHL to a certain degree. Oh, that shouldn’t be spread. The blue here
shouldn’t be spread. It should be the over under. The over under results
are a bit more mixed. And again, I ran this
actually on Friday. So I got to look at
this a little bit more. But there’s some
consistent patterns here opposite of momentum,
which is exactly what you’d expect because value and
momentum are negatively correlated. And you see that
a little bit here. That’s very consistent
with the behavioral stories that are being told
in financial markets. Now, just a caveat here. The results for momentum are
statistically much stronger than they are for value. For value here–
this is where I’m going to fight with
Fama and Thaler– it’s a question of whether
you think the glass is half full or half empty. The patterns look
somewhat compelling, but the statistical
significance is really slight. In fact, borderline not there. So Fama may look at
this and say, ah, there’s nothing there for value. I can believe there’s
something there for momentum. Thaler may be happy with
the momentum result, but maybe want to
get greedy and say the value result’s there too. But this is certainly
a lot less compelling. It’s also the case,
as we talked about, measuring value is
a lot harder, too. So one of the reasons this
might be less compelling is it’s just harder for me
to come up with an analogous measure in financial markets. So that’s just a summary of
the patterns that I find. There’s continuation
or sort of pushing up of returns in the momentum
direction that gets reversed. And for value, it goes
in the opposite direction because it’s essentially
pushing prices down further for those chief assets. Both are consistent
with what people call the delayed overreaction story. And we all agree, risk
can’t explain this. There’s no changing risk
factor or anything like that. That’s off the table here. Finally, the last
thing I want to do is cut the– I haven’t
done this yet– but I have some interesting data
on high volume and low volume games where there’s
a lot of betting activity versus a little. I also have games that
involve a parlay, which are known as sucker bets. A lot of retail investors
like to do these. So one interesting
question would be whether the results
are much stronger when I cut the data along
these dimensions, and I haven’t done that yet. And then finally, can
you make money off this? Well, if there were no vig,
the answer would be yes. So that 10% transaction cost
that sports betting book makers charge is pretty
hefty to overcome. So if you run a trading
strategy using momentum, value, or size– by the
way, there’s nothing going on for size, that’s
why I skipped over that. But kind of makes sense. It’s a very slow
moving variable. You could make money
trading momentum in sports betting markets
until you have to pay that 10%. and then you lose money
pretty consistently. In other words, you make a few
percent betting on momentum or even betting on
momentum with value, but it’s not enough to cover
the vig, which in some sense is a nice story because if there
were lots of money on the table here, you’d expect someone
to jump in and take advantage of this. Well, you can’t because
that spread’s pretty wide. What’s interesting is in
sports betting markets, that spread’s come down. A 10% vig has come down now
a lot on online book makers. It’s more like 7%. That’s still too
high to make money, but at least you’re getting
a little bit closer. So let me stop there and let me
open it up to some questions. It’s just a conclusion
slide, but it just reiterates the points
that I’m hoping to make. So let me open it up. AUDIENCE: [INAUDIBLE]. TOBIAS J. MOSKOWITZ:
Any questions? AUDIENCE: I have one
question for you. TOBIAS J. MOSKOWITZ:
Figured you might. AUDIENCE: So another
way of getting at this would be in game trading. It seems like that would
actually have more parallels, if you thought about it,
to financial markets, if you thought about it. Maybe explain those markets
and then talk about it. TOBIAS J. MOSKOWITZ: Yeah. So what Steve’s talking about
is once the game starts, you can start betting on that
same game within the game, as the score changes,
as people move. I don’t have that data. I think that would be very
cool data to look at this, and much more micro. So I think there’s things I
could get at with that data that I couldn’t get at here. The one thing I do like
about this broader view, though, is it’s more analogous
to financial markets in terms of selecting across contracts,
the cross-section of returns. So I think there’s two things
that are interesting here. One is if you just wanted to
test the behavioral theories in and of themselves, looking
at the within game stuff, I think, would be a more
powerful way to do that, as Steve’s suggesting. But if you want to link this
to the cross-sectional asset pricing anomalies
that I’m looking at, which is why do some
stocks outperform others– and in this case, why
is betting on some games better than betting
on others– then I think you want to look
at that broader view. Both would be very
cool to look at. So here, I’m just exploiting
the cross-section. But looking within
a game, I think, would be very neat
if I had that data. I’m actually working on
getting something like that. So I think that’s interesting. Yes? AUDIENCE: Thank you for
speaking with us today. Warren Buffett is
offering $1 billion to anyone who completes a
perfect March Madness bracket. TOBIAS J. MOSKOWITZ: Yeah. AUDIENCE: The odds are
about one in 4.3 billion. My question is, what do
you think your odds are, and if you’re planning
on entering, and if so, can I see your data and
your bracket before we do? TOBIAS J. MOSKOWITZ: Exactly. Let me put it this way. So I think my odds are no
better than the average person. In fact, I do a NCAA
auction with a bunch of other professors
at Harvard and UCLA and across the country. And I had my buddy Nate
Silver prepare a cheat sheet. He and I put one
together where we had all the numbers
you could possibly want from all the
sports betting markets, from all of his own
analysis, because he was writing a column for The
New York Times, now Grant Land. Or now ESPN, I should say. And so we put it all together. And I thought I made great bets
And I was the biggest loser in the entire thing. So I lost it by far, like
by a factor of two or three to the next person
who lost money. So it’s tough. If you think these markets
are pretty efficient, just looking at the point
spreads is your best bet. Here’s the problem. To get a perfect bracket,
you got to bet on volatility. So you just really
have to get lucky. You’ve got to pick whichever
15 seed might win and go from there. Yeah. Buffett should be
putting up $4.3 billion, actually, if he’s
really meaning to pay. AUDIENCE: Well said. TOBIAS J. MOSKOWITZ:
So we’ll see. Yeah. AUDIENCE: Thank you,
Professor Moskowitz. Could factors that
universally affect player health be
considered a case of macroeconomic variables? TOBIAS J. MOSKOWITZ:
Well, they could. But remember, it’s
a zero sum game. You got two teams
playing each other. So suppose there was a
break out of the flu, right, and the starters on
all teams can’t play. That’s true for both teams. So in terms of the
difference, I don’t think that that’s going to matter. And that’s the key about
looking at the cross-section of returns. Not only am I looking at the
difference between two teams, looking at the difference
between two games. And there’s nothing
that I can think of in a macroeconomic
sense that would tell me that the Giants-Eagles
game is priced differently than the Cowboys-Redskins
game at the same time. So that that’s kind of the
way we’re approaching it– I’m approaching it. Any other questions? You guys were hoping to
hear about home field advantage or something, right. Go ahead. AUDIENCE: You ever consider
looking at sub-samples where [INAUDIBLE]
might be occupied, like just post-season games? TOBIAS J. MOSKOWITZ: Yeah. So that’s a good question. So I’ve tried to cut it. There’s not that many
post-season games, so you lose a ton of power. But if I put in like
a post-season dummy and tried to interact
it, I don’t see anything incredibly different. One way– that’s why
I was talking about it at the end– cutting it by
trading volume, which actually post-season games are
much more heavily betted than regular season, is
one way to look at it. So that’s something
I’m planning to do. At least for the initial bounce
back effect, the reversal effect, I don’t see big
differences between highly bet games versus not so
highly bet games. One thing I could do, but
it’s a big pain in the ass, is I actually have
all the college sports as well, which are
very illiquid, many of them. I could see if
those are different. The problem is some of
those are illiquid games are so illiquid, the prices are
just a little crazy in general, which means you probably
couldn’t transact that, then. That’s the problem. They’re not real prices. So it’s something that
I’m considering doing. That’s one thing to look at it. I thought of also
looking at televised versus non-televised games,
or nationally televised games, that is. That kind of thing. Or looking at big events,
Super Bowl, whatever. The BCS championship,
all that stuff. But again, you lose
data really fast. Yes? AUDIENCE: When
looking at momentum, would you cut off [INAUDIBLE]
a little tricky. So momentum is pretty
easy because I just looked at things like– and I
went over this too quickly– but like in football,
I’m looking at momentum, it’s like 4 game streaks, 5 game
streaks, that kind of thing. In basketball, I look as much
as 8 to 10 games for momentum, because obviously it’s the
same percentage of the season. And baseball, similarly. When I start looking at
the longer term performance measures like past performance
over 40 games in the NBA, or even multiple seasons, yeah. You have to be careful
about those season cut-offs. So none of the momentum
measures– for instance, if I’m using, say, the past 10
games, then the season would start with the 11th game. I don’t want to spill over
into the previous season. The team was different. There’s been a huge
lag between them. So I try to do that. It actually does
make a difference, because I think the first time
we ran it, we didn’t do that. And the results were a
lot weaker, which is good. They should have been, right? There’s less information,
you would think, when you do that for momentum. So these are all within season
for the short term measures. Yeah. AUDIENCE: How big is
the momentum effect relative to whatever the
baseline return is, compared to the financial market? TOBIAS J. MOSKOWITZ:
That’s a great question, and I don’t know
how to answer it. I really want to
answer that question. You know, I can do it terms
of like– so getting back to one way think about it. Where’s this? Go back. Oh, I don’t have it on here. That’s stupid. Oh, yeah. I do. The Sharpe ratios. One way to think about is
looking at the Sharpe ratios. These Sharpe ratios
are way smaller than what you get in
financial markets. Maybe that just means
that what you’re seeing in financial markets
is a bit of behavioral stuff plus a lot of risk premia. I don’t know what
the right story is. But even Sharpe ratios aren’t
necessarily the best measure. Just to give you a sense,
the return distribution here, because it’s so
0-1, the volatility of these sports betting
contracts on average can be something like 100% per
year on an annualized basis. It’s like five or six
times market volatility. Sharpe ratios should
adjust for that. But there’s no natural
compensation for risk here because it’s idiosyncratic. It’s really hard to compare. Another thing I tried
to do was think about it in an r squared context. Actually Steve and Jesse,
if you have any ideas, this would be great. But thinking about how much of
the cross-sectional variation in returns is
momentum explaining relative to how much
cross-section variation there actually is, that’s
even a really hard number to come by in financial markets. I would love to get that
number, because that would be the exclamation point on this. So if anybody here
has any ideas, I’m still thinking
about how to do that. I don’t know how
to do that right. That’s an excellent question. Yeah. AUDIENCE: Have you looked into,
I guess, the closing parts and the result at the end,
those are widening out? But access to the opening lines
likely to be really limited. Have you looked at
what percentage of that run-off takes place right
away, whereas you may not have been able to have
access to the opening lines? TOBIAS J. MOSKOWITZ: So I
don’t have that data yet, but I’m working on it from the
same vendor that gave me this. He’s actually got time
stamped price movements between the open to
close as well as betting volume tied to that. He won’t disaggregate it
for me, but I don’t care. I just need to see the
total number of contracts or the total number of dollars
bet, would be even better. That would be a really
cool thing to do, and I haven’t done that yet. It’d also allow me, getting back
to related to what Steve was saying about the
within game, this would be before the game starts. But I’d also be able to see
the price patterns themselves. So for momentum,
it’d be interesting if you saw a steady increase
in prices as people piled on, or whether this was
just one big jump and maybe that has different
pricing implications going forward. I’d love to. I don’t have that data yet. Yeah. Now for some of these contracts,
like in the NBA and baseball and hockey, often
there’s only a few hours between the open and the close. So there’s not going to be huge
movements, I would think there. But for something
like the NFL, that could be really interesting. We have a week of betting. So it’s a great idea. Yeah. Other questions? All right. Well, that’s what
I’m doing currently. We’ll see. I got a lot of work
to do still, though. Thanks. [APPLAUSE]

18 Replies to “Asset Pricing and Sports Betting”

  1. Baseball is the only game to bet for one very simple reason:It is the only game on which you can wager-in most cases-before knowledge out of the outcome of the previous game and what transpired in that game.

    You have now learned more about sports betting than you have ever learned in entire life to this point.

  2. Tobias mentions in his speech that the results are independent of each other.  I would argue that this is a false statement.  The results of one game highly effect the results of the next on two separate psychological planes.  A loss affects a team's psychology.  A playoff bound team has the heart of a lion and takes losses very personally.  For example, look at how many times the New England Patriots (NFL) have lost 2 games in a row in the last 10 years.  It is a very rare occurrence.  Now contrast that with a team like the Jacksonville Jaguars who are awful and they know it.  A team like this will lose several in a row.  However, since they know they have absolutely no shot of making the playoffs their psychology changes when playing a playoff bound team.  That game becomes more meaningful for them.  The spreads are usually very large in favor of the favorite team and the underdog plays the game much harder and usually covers the spread.

    The second plane is the psychology of the public.  The public inherently likes to follow the favorites. This pushes the spreads until they get burned.  They then back off and the bookies must lower the spreads to get them to back them again.  This causes bubble patterns.  They are so fast most of the time that they can not be recognized for what they are, but every once in a while a team comes along that demonstrates this idea perfectly.  Look at the 2007 Patriots and you will witness the perfect bubble.  The team was crushing all opponents early in the season.  The public jumped on the bandwagon and were rewarded during the first half of the season.  With each consecutive win the spreads got larger.  Meanwhile the other teams are working diligently to figure out how to bring this juggernaut down.  The bubble officially burst in week 12 when they face the Eagles as -24 point favorites.  They only won by 3.  The Giants get the credit for finally taking them down in the Superbowl, but it was the Eagles that cracked the code and laid out the model that all other teams would follow to try and beat them.  The public was very slow to back off from the Patriots so the point spread were slow to come down.  The results against the spread were that the Patriots only covered one more game for the rest of the season.  These results were hardly independent from each other.  They were highly correlated.  The psychology of the teams and the public being slow to react created the perfect bubble.

    I posit that this is not an anomaly but actually how the spreads and odds operate.  They depend on these bubbles to maintain balance between favorites and underdogs.  The belief that these are independent trials like spinning a roulette wheel or rolling dice is completely false.  They are the exact opposite…   …highly correlated.

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  4. i watched this a couple times and just want to understand if i am correct, so the momentum factor causes assets to be over valued and has little predictibility on actual returns, which means shorting mometum assets are profitable?  while value is the exact opposite and buying value assets are profitable?

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