# AQRAMI

Data: 11-01-2025 21:49:05

## Lista de Vídeos

1. [Addressing Behavioural Biases in Investing: Prof Nicholas Barberis | London Business School](https://www.youtube.com/watch?v=qO6A9q4OoNI)
2. [[Private video]](https://www.youtube.com/watch?v=6tpCbKD9e3w)
3. [Uncovering Behavioural Biases with Machine Learning | London Business School](https://www.youtube.com/watch?v=ncqnDl1VM-c)
4. [Investment Assessment, Personality, Decision-Making & Bias | London Business School](https://www.youtube.com/watch?v=2YYmw1w6-9Q)
5. [Psychological Drivers of Asset prices & Investor Behaviour | London Business School](https://www.youtube.com/watch?v=uzhuM47DE90)
6. [A view into what psychological literature says on debiasing judgements and decision making | LBS](https://www.youtube.com/watch?v=eXx8obhvrCs)
7. [Measuring Culture in Asset Managers | London Business School](https://www.youtube.com/watch?v=oupZyY2GoRA)
8. [Regulating (and Innovating) for the Real World | London Business School](https://www.youtube.com/watch?v=yu_FL0UwR-Q)
9. [An emerging economy perspective on the global economy and markets](https://www.youtube.com/watch?v=Jx4H-mwkggc)
10. [Future proofing pensions  integrating the wisdom of John Maynard Keynes and Peter Drucker](https://www.youtube.com/watch?v=-A91urE5zCE)
11. [Low rates: causes and consequences](https://www.youtube.com/watch?v=OMFV-3sJQhw)
12. [The dash for cash and the liquidity multiplier: lessons from March 2020](https://www.youtube.com/watch?v=9hUIdSgxovc)
13. [Presentation of AQR Fellowship Award 2020](https://www.youtube.com/watch?v=w0cHdguT7O8)
14. [ESG Investing Session Three - Stephen Schaefer and Martin Skancke](https://www.youtube.com/watch?v=Ey7pqWNdv64)
15. [Diego Kaenzig - Winner of the AQR Asset Management Institute Fellowship Award](https://www.youtube.com/watch?v=UgT2ky-nmEU)
16. [ESG Investing: evidence on opportunities and challenges](https://www.youtube.com/watch?v=yZr1sKKlEAE)
17. [ESG investing beyond traditional strategies](https://www.youtube.com/watch?v=pA77F_j2_Ac)
18. [Corporate responsibility in the age of automation, inequality and climate change](https://www.youtube.com/watch?v=rLZEnPZn1uk)
19. [Decentralisation in digital finance: possibilities and limits](https://www.youtube.com/watch?v=NWrCYBDG7XA)
20. [Decentralised finance : opportunities and risks](https://www.youtube.com/watch?v=6m8VNxWLUZs)
21. [Climate financial risk: portfolios and stress tests](https://www.youtube.com/watch?v=HCm2qAdrae0)

## Transcrições

### Addressing Behavioural Biases in Investing: Prof Nicholas Barberis | London Business School
URL: https://www.youtube.com/watch?v=qO6A9q4OoNI

Idioma: en

good morning everyone and thank you for
that very kind introduction I'm very
happy to be here for this very
interesting day on behavioral finance
I'm going to talk a little bit about the
topic not for very long I want to leave
lots of time for any questions or
comments or reactions that you might
have I apologize I'm sitting on this
stool I have a sort of minor leg injury
so just a little bit of background first
I think sort of many of you know that
the modern research era in finance
started in the 1950s and from the 1950s
to the 1990s research was really
dominated by a single paradigm known as
the rational agent framework and it was
cool that for good reason because it
basically assumes that everyone out
there is fully rational and it's a
useful and important framework but it's
become pretty clear over time that a lot
of important things in financial markets
don't fit very easily into this
framework so perhaps not surprisingly
starting in the 1990s a second framework
emerged the behavioral finance framework
and it argues that some financial
phenomena at least are the result of
less than fully rational thinking on the
part of some market pot participants and
it advocates the use of models that are
psychologically more realistic there's
just a sense that the frameworks we've
been using for decades to think about
financial markets just aren't
psychologically realistic enough to
capture a lot of what goes on in the
world we need more psychologically
realistic frameworks and so when I
started working in this area more than
20 years ago
it really wasn't clear how it was gonna
go how it was going to do but now I feel
confident saying to you that at least on
some dimensions behavioral finance has
been successful it explains many
observed facts in simple intuitive ways
it makes testable predictions at least
some of which have been confirmed in the
data and there's strong interest not
just among academics but among
practitioners and policymakers too
academic papers in behavioral finance
have been heavily cited and it's
received its share of awards as well not
least the Nobel Prizes to people like
Daniel Kahneman Robert Shiller and
Richard Thaler that said it still has
some
way to go for example one goal you might
have is that all finance researchers be
familiar with the core ideas in
behavioral finance and apply them as
appropriate and we're not there yet
so I think people who work in the field
like me one task we have is to
communicate our ideas more broadly and
so I thought it might be helpful if
today I try to do just that
in just a short amount of time so I
thought specifically what I could do is
try to pick out 3 ideas from behavioral
finance that I think are particularly
helpful for thinking about financial
markets and the three that I've picked
out are over extrapolation of the past
over confidence
and while I'm calling gain loss utility
coupled with elements from prospect
theory I'm going to try to show you
today that these ideas can explain many
central facts about asset prices and
after I've done that I'll just end with
some broad remarks about progress in the
field all right so let's dive into the
first idea over extrapolation it's just
the idea that when people form beliefs
about the future they put too much
weight on the recent past and today I'm
gonna focus specifically on the idea
that people extrapolate past returns at
some level in a very basic idea it's
just the idea that if the recent returns
on an asset or asset class have been
good people are too quick to think that
the future returns will also be good if
the recent returns on an asset or asset
class have been poor people are too
quick to think that the future returns
will also be poor and this is an old
idea you can find it in qualitative
accounts of financial markets going back
decades the first wave of formal
academic research on the topic came
along in the 1990s and in just the past
five years or so there's been a second
wave of research on this topic spurred I
think in part by renewed attention to an
interesting kind of data specifically
survey data so there's a lot of surveys
that are conducted of investors both
individual investors and institutional
investors that simply ask people for
their forecasts of future stock market
returns how do you think the stock
market's going to do over the next
year and these data provides support for
the notion of over extrapolation of past
returns so let me take that in two steps
first of all the data point to
extrapolation of past returns if the
recent returns on the stock market have
been good people think that the future
returns will be good if they've recently
been bad people think that the future
returns will be bad and you can see this
in this graph here from one study of
this topic this is actually Gallup
survey data as I remember the graph runs
from 1996 through 2012 the red line is
in each quarter we're asking people to
forecast the stock market return over
the next year and the blue line is at
that very moment of time how did the
stock market how had the stock market
performed over the previous year and you
can see a close correlation between the
two lines which simply says if the stock
market had recently performed well
people thought that it would keep doing
well if it has recently performed poorly
people thought that it would keep doing
poorly
so people are extrapolating past returns
but also the author's show they seem to
be over extrapolating past returns in a
sense of their beliefs are incorrect
what I mean there is if you look at the
correlation between people's forecasted
return and what subsequent realized
returns actually are you'll see
something of a negative correlation
where people expect higher returns the
subsequent return is actually lower than
average when people expect low returns
the subsequent return is actually higher
than average so these data I think have
helped to spur this new wave of research
on return over extrapolation and what we
found in just the past sort of 5-10
years or so is the models in which some
investors over extrapolate past returns
can explain several of the most
important facts about asset prices for
example momentum and reversals and
individual assets time-series
predictability in aggregate asset
classes and financial bubbles and if you
look at that list that's a large chunk
of what we're trying to understand about
asset prices so
it's striking to me the a single simple
idea can shed light on all of them and
before I explain the intuition for how
this works I thought I'd just briefly
remind you of what these facts say
although I suspect that some of you are
well aware of them so first of all
momentum and reversals so in the
cross-section of stocks but also in
other asset classes is what's called
medium term momentum for example stocks
with high past six-month returns have
higher subsequent returns on average
than stocks with low past six-month
returns so to make that a little bit
clearer
imagine the at some point of time you
take your database of stocks and you
rank all the stocks based on how they've
performed over the past six months
so some stocks have performed very well
some stocks have performed very poorly
it's interesting to see how do these two
groups of stocks do subsequently and
what's been found over decades of data
in the United States but also most
international markets is that these
prior winners subsequently have a high
average return while the prior losers
subsequently have a low average return
but we also observe what's called long
term reversals for example stocks with
high past three year it turns have lower
subsequent returns on average than
stocks with low past three-year terms so
to be clear if we now take our database
of stocks and we rank stocks based on
how they've performed over the previous
three years so you've got some big
winners and some big losers it's
interesting to see how do these two
groups of stocks perform in the future
and what's been found over decades of us
data but most international markets - is
that these big prior winners
subsequently have a low average return
while the big prior losers subsequently
have a higher average return and a
long-standing challenge I think for
finance researchers is to explain both
of these patterns in a parsimonious one
second fact while I'm calling
time-series predictability this is in
aggregate asset classes so in aggregate
asset classes raise
of price to fundamentals predict
subsequent returns with a negative sign
for example in the stock market the
price to earnings ratio of the stock
market predicts the market subsequent
return with a negative sign and you can
see that in this picture from a study by
Campbell and Shiller so every number in
this picture is a year over the past
century the horizontal axis is the price
to earnings ratio of the market in that
year the vertical axis is over the next
ten years the performance of the stock
market specifically the ten-year price
growth and you can see you can run a
regression of course but even visually
you can see something of a negative
relationship whereby years with high p/e
ratios are followed by lower returns
while years with low p/e ratios are
followed by higher returns so the stock
market is not a random walk its future
return can be predicted at least a
little bit in advance using ratios like
this and many economists feel like this
may be the single most important fact
about stock market fluctuations that we
want to try to understand
and finally bubbles I hardly need to
explain what these are but basically
episodes where the price of an asset
rises dramatically and then collapses
and during the price rises a lot of talk
of possible overvaluation as well as -
and so coming back to our earlier theme
what we found in the past five or 10
years is the a framework or an economy
where some investors over extrapolate
past returns can shed light on all of
these basic facts about asset prices and
I wanted to explain how that works with
the help of this picture right here so
what this picture plots if you look at
the blue line what the blue line is
plotting is the price path of an asset
in an economy where some people
extrapolate past returns following a
good news about fundamentals here at
time - so again the price path is this
blue line sorry is the price path of an
asset in an economy where some people
extrapolate past returns following good
news at
- and the red line shows you what
happens if there are no extrapolate us
but rather all investors are fully
rational so why does the price path look
like this so it works as follows
following the good news at time - the
price of the asset naturally goes up
what happens next well here the return
extrapolate errs look back they see hi
past returns which makes them think that
the future return will be good as well
so they by aggressively pushing the
price even further on what happens next
well here the return extrapolate errs
look back they see two periods of high
returns that gets them even more excited
about the future so they buy even more
aggressively pushing the price even
further but around here they look back
and they see that the recent returns
while positive haven't been quite as
amazing as before so while remaining
excited they get a little bit less so
and the price comes down a little bit
and here they look back and they see a
negative return so they become a lot
less excited and the price collapses
further and so in that picture you can
see all of those basic asset pricing
facts we were trying to explain first of
all you can see the medium-term momentum
notice how the high past return is
followed by more good returns at least
in the short run and the intuition is if
an asset has a good past return that
gets extrapolate is excited about its
future return which leads them to buy it
pushing it further up at least in the
short term but you can also see the
long-run reversal so notice how the good
long-term past return is followed by
lower returns going forward if an asset
has had a long-term good past return
that's a sign that extrapolate errs have
become very excited about it later on
they become a little bit less excited
and returns are poor you can see the
time series predictability - it's around
here that the price to earnings ratio of
the asset is particularly high and you
can see that just as in the data that's
followed by lower returns again if an
asset has a high price to earnings ratio
that's a sign that extrapolate errs have
become very exciting
about it later their excitement abates
and the returns are low and finally if
the cash flow news here at time two is
particularly good you're going to get an
amplified version of this blue price
path that's going to begin to look
something like a bubble a big rise in
price followed by a collapse and that
leads to one of the main behavioral
finance theories of bubbles which is
that bubbles are triggered by good
fundamental news good news about the
assets fundamentals that push up the
price of the asset which then gets
extrapolate as excited who then buy the
asset aggressively pushing it further up
at least in the short term so it's
striking to me that this simple
assumption about return extrapolation
can explain a lot of these basic facts
about asset prices now if you have a
good theory it should be helpful for
investment purposes and I think that's
the case for return extrapolation as
well so let me explain what I mean
return extrapolation again is the idea
that people use past returns as a basis
for their forecasts of future returns
but the interesting thing is that the
past returns they look at or which past
returns they look at seems to vary over
time sometimes they're focused just on
the past few months of returns sometimes
they seem to look at the past few years
of returns and you can see that in this
picture here which goes from 1992
through to 2012 the Green Line is the
S&P 500 during that time and the blue
line shows you which returns investors
seem to be looking at when forming
forecasts about the future as judged by
the survey evidence so when this blue
line is high that means that people are
focusing on just the past few months of
returns when this blue line is low that
means they're actually looking at the
past few years of returns so you can see
for example that in the late 1990s
people were increasingly looking at just
recent returns so that's interesting in
and of itself but it's also useful for
investment purposes and there's an
interesting recent paper by Casella and
Golan that sort of makes this point
specifically what they show is in the if
investors have currently have been
waiting recent returns heavily as judged
by the surveys this signals a market
inflection point specifically if the
stock market is overvalued and recent
returns are being heavily weighted by
investors then a short-term correction
is more likely so this helps to answer a
long-standing question that
practitioners that I think are
particularly interested in their
Laughton ask look if the price to
earnings ratio of an asset is high that
suggests that it might be overvalued and
due for a correction but when will the
correction occur what this study shows
is that survey data helps to provide an
answer if the price to earnings ratio is
high and investors seem to be very
heavily focused on just recent returns
then a correction is more likely in the
short term and there's good intuition
for that which is suppose investors are
very focused on just short-term past
returns and then they're supposed that
there's just a one piece of bad news
that brings the market down a little bit
then precisely because people are
focused on the very recent past that
could trigger a large change in market
sentiment and hence a large collapse in
the price of the asset so this idea of
return extrapolation not just helpful
for understanding basic facts but
potentially also for investment purposes
so that was the first idea I wanted to
discuss with you the second idea I've
decided to talk about is overconfidence
now overconfidence is a broad term and I
wanted to distinguish two kinds of
overconfidence so there's one kind
called over placement and this refers to
the idea that people seem to have overly
rosy views of their abilities relative
to other people and it's the kind of
thing that's most easily established in
surveys where these sort of 80% of
people declare themselves to be above
the median on various dimensions like
attractiveness sense of humor driving
ability ability to get along with people
at least 80% think they're above the
median obvious
that's not possible many people must be
deluding themselves that's one kind of
overconfidence another kind is over
precision this refers to the fact that
people seem to be too confident in the
accuracy of their beliefs this is
usually established by asking people for
confidence intervals so I could ask you
a question like how many petrol stations
are there in some region but ask you not
just for an estimate but also your 90%
confidence interval so your estimate
might be 200 petrol stations but your
90% confidence interval ranges from a
hundred to three hundred in the sense
that you're 90% sure that the right
answer falls into that interval so then
I can ask you many such questions and
then I can see how often does the right
answer fall into the intervals you gave
if you're correctly calibrated the right
answer should fall into the intervals
around 90% of the time but what dozens
of studies have found going back decades
is that the right answer falls into the
intervals only around 50% of the time so
people are giving in to those that are
too narrow which in turns suggests that
they are too confident about the
precision of their estimate so this idea
of overconfidence has several
applications in finance but perhaps the
principal motivation for invoking it in
the financial context is to understand
the very high trading volume we've
observed in financial markets for
decades non-speculative motives for
trading like liquidity needs or
rebalancing are unlikely to explain that
much of all this trading volume instead
it seems that most trading is likely
speculative in other words based on
people's differing beliefs about future
price changes and the key point is this
and this was only really figured out in
the 1980s is that it's hard to generate
a large amount of speculative trading in
an economy where everyone is fully
rational and I've written here that's
because each investor infers are those
information from prices or from them
Miyo
willingness to trade which reduces your
own willingness to trade so just to be
clear about that to really boil it down
if I go to buy a stock and then you are
willing to sell to me well if I'm fully
irrational and then gonna say wait why
are you willing to sell to me perhaps
you know something bad about the future
prospects of this company in which case
you know what I think I'd rather not buy
after all now that might sound extreme
to you but that is how a fully rational
person will think and so when we write
down models of fully rational investors
we find that they don't predict a lot of
volume for this reason nothing like the
amount of volume you observe in the real
world and I guess the point is the
overconfidence is a nice way of breaking
this logjam
so let me repeat the story with some
overconfidence thrown in so I go to buy
a stock you're willing to sell to me so
then I say to myself okay why are you
willing to sell to me oh okay you think
you know something bad about the future
prospects of the company but then I say
I don't care what you think
because I think I'm much better than you
at analyzing stocks and you feel exactly
the same about me and so we're both very
happy to trade with one another we both
think we're on the winning side of the
trade obviously we can't both be but
because of overconfidence we think we
are and therefore we're happy to trade
and what's nice is in the past few years
we've seen some direct evidence that
overconfidence really plays a role in
trading so to test this hypothesis I
think one natural prediction is more
overconfident or more overconfident
people are going to trade more because
they're so sure they know what they're
doing and there's some nice studies that
have tested that prediction one of the
most compelling to me is by Greenblatt
and Keller hajdu
they use data from Finland a number of
these studies use data from Sweden or
Finland these countries keep close
detailed records of what their citizens
do so it's a good place to run these
studies the challenge of course is to
figure out who in Finland is more
overconfident and so the way the authors
did it is it turns out that at the age
of 18 or 19 every Finnish man has to go
into the
military for compulsory army service and
when you do that you take a bunch of
tests some psychological tests and some
aptitude test tests of math and verbal
ability one of the psychological tests
is a self-reported measure of confidence
how confident a person are you on a
scale from one to ten and so the measure
of overconfidence in this study is your
self-reported confidence - how confident
you should be based on your performance
in the aptitude tests so it's a bit
cruel but it's good in terms of a
measure of overconfidence and what the
author's showed it's a remarkable
finding is that that measure of
overconfidence measured at the age of 18
or 19 predicts the frequency with which
people trade stocks several years later
when they finally open a brokerage
account of their own so to me that's
nice evidence of a direct link between
overconfidence and trading and more
broadly notice how even to understand
something as basic as trading volume we
may need to appeal to ideas from
behavioral finance like overconfidence
and so the last idea I wanted to discuss
with you is gain loss utility with
prospect theory so up until now I
focused more on people's beliefs I'm now
going to turn to what economists often
refer to as people's preferences in
other words given people's beliefs about
the potential future outcomes of the
investment decision how do they evaluate
these outcomes and many psychologists
feel a theory called prospect theory by
Daniel Kahneman Amos Tversky might be
the best available answer to this
question and prospect theory has a
number of elements I'll just mention
three of them so the most basic is
reference dependence which just means
that when people are evaluating a risky
bet a risky gamble a risky opportunity
they think in terms of potential gains
and losses in other words they're very
focused on what could I gain what could
I lose but it's not just that they focus
on gains and losses they're also much
more sensitive to
losses and that's the concept of loss
aversion but also today I wanted to
emphasize another element probability
weighting which says that people process
probabilities in a nonlinear way in
particular over weighting low
probability outcomes and you can see
that in this picture so on the
horizontal axis here is the probability
of some outcome and on the vertical axis
is how much weight the brain puts on
that outcome when making a decision I've
inserted the green dashed line the
45-degree line
that's what traditional models assume
they assume that if something is gonna
happen with probability 0.3 the
decision-maker puts weight of 0.3 on it
we're making the decision if something's
gonna happen with probability 0.7 the
decision-maker puts away 2.7 on it when
making a decision but continent first
key from their experiments found that
the brain actually use nonlinear weights
captured by this blue line that in
particular overweight low probability
outcomes and there's a lot of evidence
for that one basic motivation is the
fact that many human beings like both
lottery tickets and insurance policies a
combination of behaviors that isn't easy
to understand under the traditional
economic framework but probability
weighting captures it by saying that
when you're thinking about a lottery
ticket the brain is over weighting the
states in which the unlikely event in
which you win the lottery and therefore
you find the lottery ticket appealing
and when you're thinking about an
insurance policy the brain is over
waiting the unlikely event of a
financial disaster and that makes you
lean towards the insurance policy
and while loss aversion is the
best-known element of prospect theory
probability weighting may have just as
many applications in finance it makes
the following very basic prediction
which is the the skewness of an assets
returns will be priced
even the idiosyncratic skewness a
prediction not made by the traditional
models more specifically positively
skewed assets will be overpriced and
earn low average returns negatively
skewed assets will be under priced and
earned high average returns
and let me before turning to
applications just make sure the
intuition is clear so a positively
skewed asset is one that offers you a
small chance of a really good outcome
so it's return distribution will look
like the picture at the top here the
long right tail indicating the positive
skewness I'm saying that under
probability weighting assets like that
will be overpriced and have a low
average return the reason is that when
you're thinking about such an asset the
brain will be over waiting these very
appealing right tail events so you find
that distribution very attractive you're
willing to pay a lot for the asset and
earn a low average return a negatively
skewed asset exposes you to the small
chance of a very bad outcome so that
would have a return distribution like
the one at the bottom where the long
left tail is indicating the negative
skewness and I'm saying that under
probability weighting such assets will
be under priced and have high average
returns why because when you're thinking
about such an asset the brain is over
waiting these nasty left tail outcomes
so you find this asset very aversive you
pay only a low price for it and demand a
high average return and these very
simple predictions can be helpful for
understanding a range of facts
specifically about average returns so
some average returns are puzzling lehigh
some are puzzling Leeloo for example the
return on the overall stock market is
puzzling lehigh over the past two
centuries the US stock market has
outperformed Treasury bills by an
enormous margin of course we expect it
to outperform because it's riskier but
it's hard to understand the sheer
magnitude of the outperformance
and the same conclusion is reached even
if you look at the average stock market
around the world meanwhile other average
returns are puzzling Leeloo for example
the long-term average return on IPO
stocks is surprisingly low you can see
that in this classic picture from a
study by Loren and Ritter the columns in
the front row show you the average
returns of IPO stocks in the five years
after the IPO the columns in the back
row show you the average returns of a
control
group of stocks stocks that are similar
to the IPO stocks in terms of market
capitalization but happened to do their
IPO much further back in time and you
can see much lower average returns for
the IPO stocks and I just want to say
the probability weighting offers a
simple way of understanding both of
these facts the reason is that the
overall stock market is negatively
skewed it's subject to occasional large
crashes so then probability weighting
says indeed such an asset should have a
high average return the intuition is
that when people are thinking about the
overall stock market the brain is over
weighting the nasty left tail outcome
the possibility of a large crash so you
find this asset aversive and you demand
a high average return on it
by comparison IPO stocks have very
positively skewed returns most of them
don't really go anywhere but a small
handful like Google or Microsoft have
incredibly good performance after the
IPO so then probability weighting would
say that such an asset should have a low
average return and the intuition is that
when you're thinking about an IPO stock
the brain is over weighting the right
tail outcome where this stock turns out
to be the next Google so that makes you
excited you're willing to pay a high
price for the IPO stock and to accept a
low average rate return on it and this
idea that positively skewed assets
should have low average returns it has
many other applications for example if
you look at distress stocks or bankrupt
stocks or stocks traded off the main
exchanges out of the money options on
individual stocks or stocks with high
idiosyncratic volatility they all have
low average returns why is that well
we're not completely sure but one idea
is that they have that it's due to their
positive skewness all of these assets
have very positively skewed returns so
perhaps they have low average returns
because they're positively skewed so
today in an effort to be helpful I tried
to pick out three ideas from behavioral
finance that I think are particularly
useful for thinking about financial
markets and the three I picked out are
over extrapolation of past which
overconfidence and gain/loss utility
coupled with prospect theory you now try
to show you that these three ideas can
explain many central facts about markets
average returns time series
predictability momentum and reversals
bubbles and trading volume and they do
so I think in simple intuitive ways and
notice also that behavioral finance it
isn't about little quirky things it's
really trying to get at basic facts
about financial markets back in the
1990s when behavioral finance was
getting going there was a worry about
lack of discipline in the field so there
was a worry that what behavioral finance
people did is whenever there was any
puzzle in financial markets they would
sort of flip through the psychology book
until they found some kind of bias that
would seem to explain the puzzle they
were trying to explain and then they
would declare victory and go home and
there is a real danger here because
there are dozens of ways in which people
are irrational and so people were
worried that we were going to see a
profusion of psychological biases like
30 different biases to explain 30
different facts and what's striking to
me is that that concern has proven
unfounded in the 1990s the center of
gravity in behavioral finance was in
three ideas over extrapolation
overconfidence and prospect theory and
as I've been telling you today today the
field center of gravity remains in these
three concepts I'm not sure why that is
it's possible that we were scared by the
lack of discipline critique and
therefore we stayed close to a few
central ideas or perhaps these are the
ideas that are most relevant to
financial markets and we figured that
out early on and I think that
extrapolation and prospect view in
particular might be promising building
blocks for an eventual sort of unified
theory by unified theory I mean a model
that makes parsimonious psychologically
grounded assumptions about both beliefs
and preferences and explains a large
range of facts and so the way this would
work is people would use the past
particularly the recent past to make
forecasts about future potential gains
and losses and they would evaluate those
in the way described by prospect theory
with more sensitivity to losses than to
gains and with over weighting low
probability extreme
outcomes so back in the 1990s
conferences often staged debates between
the rational camp and the behavioral
camp these debates were fun but I'm not
sure that they really advanced the cause
of science instead if behavioral finance
has made progress I think that has
happened because the researchers came
home and they just started writing down
models of how they thought the world
worked making predictions using those
models and then testing those
predictions in the data in other words I
think behavioral finance has just made
progress by acting like a normal science
and I think that efforts going to
continue in the next few years with I
hope continued success so I'm just gonna
stop there and leave I think we have at
least ten minutes remaining for any
questions or reactions or comments that
you might have I did just want to
mention that if you are interested in
digging more deeply into any of the
ideas I talked about this morning or
you're curious about some other ideas in
behavioral finance that I haven't had
time to cover I've recently completed a
comprehensive survey of behavioral
finance approaches to understanding
asset prices it's called psychology
based models of asset prices and trading
volume it's available on my website and
I think also on the conference website
so let me stop there and take any
questions or comments you might have
thank you
[Applause]
Nick I'm casinos online Roy from State
Street and I'd like to compliment you as
an exact Demick you're the best
behavioral finance person of our
generation for a masterful talk but
thank you so much being a surprise so
now I have two questions the first one
is as a practitioner something that you
can contribute a lot to particularly the
NBR behavioral finance there's a big
huge gap between what you talked about
what we as a surprising people see
people like Schiller and Andy Lowe etc
talk about on behavioral finance from an
academic viewpoint but I sit in the
industry and I see clients across 70
countries and it's a fad to mention
behavioral finance when people can't
even figure out lots of times you know
volatility ratios the kind of things
that can be low McKinley etcetera talk
so how do you square the circle between
the so-called experts and the really
people trying to understand in research
what the way markets operate versus the
practitioners who just want to gloss
over the things and my second one is a
bit more technical a lot of what you've
said could be couched in the sense of
zelner and Lars Hanson etc in Bayesian
models where you could take your priors
and then peak them in ranges of crowds
with a likelihood function and then
arrive at estimates of overconfidence
etc so the second one is I'd like your
reaction on the second one but the first
one I feel is a danger where you can
contribute a lot well thank you very
much for these questions on the second
point of yes I mean I think this is a
useful reminder that while I think
behavioral finance has useful frameworks
to offer
there are obviously complete
frameworks not just on the behavioral
side but on the rational side as well
and I cannot claim that we've completely
ruled out the rational models not at all
but the reason I felt comfortable
discussing these frameworks with you is
that behavioral frameworks have been
tested it's their predictions have been
tested and it's been support so I think
they capture some of what's going on but
you're right there is a class of
rational learning models that might be
able to address some of this evidence
and absolutely one of the things we
fight about in academic conferences is
okay which model seems to be supported
by the evidence and so on but there's no
it's not like one model has one out
there are many competing frameworks that
we need to stay on top of on your first
question well I very much understand
that there might be a gap and that's why
I'm very grateful to have an opportunity
like this where I could bring some of
the latest ideas to a practitioner
audience for you to think about and
thereby create a little bit of a bridge
if there are particular topics that
practitioners care a lot about I'm
interested in hearing about that because
those might be good research questions
for us but overall I think I'd like to
say that one thing that one thing I'm
proud of I think about behavioral
finance is it is an area of finance that
I think has resonated with practitioners
including sophisticated practitioners
and I take that as a sign that we might
be on to something we might be doing
something along the right lines so I
agree with you there's need for more
communication but I also think that this
might be the branch of finance that
might be most relevant and useful to you
Nick two questions you mentioned sort of
the periodicity of under-reaction and
overreaction sort of the six to twelve
month window being that intermediate
term and the de bon Taylor 3-year window
in the longer term there's also this
very short term one month overreaction
stuff so question one is how does that
if at all relate to intermediate term or
longer term overreaction and in that
within that second window then the
periodicity you focused a lot in
extrapolation rather than under-reaction
those two coexist as drivers for this
six to 12-month phenomenon or are you
much more of an extrapolation guy versus
a reaction guy yes thank you so let's
see on short-term reversals I didn't
talk about that it's certainly a
powerful phenomenon has been over the
decades honestly I don't think
behavioral finance has had anything
really good to say about them I mean
there was a natural hypothesis that
perhaps short-term reversals are about
overreaction to a piece of news that
then sort of rapidly corrected but
honestly there's not a lot of evidence
to support that view instead people go
more for sort of a liquidity story where
you know if someone has to sell a large
amount of an asset they often have to
give a price discount because other
people I haven't yet had time to analyze
the asset in questions of the price
discount which is then reversed so
honestly I don't think behavioral
finance as yet has had great things to
say about short-term reversals you're
absolutely right to bring up under
reaction if I was doing a longer talk I
would mention that as another topic it
might be if I had to pick four things it
might be the fourth thing so today I
talked about momentum as a sort of
overreaction phenomenon involving
extrapolation but certainly an important
alternative hypothesis is that it
reflects under-reaction there's a piece
of news a good news people don't react
enough to it so there's a positive price
move but because of the insufficient
reaction there's another positive price
move later and there's certainly
evidence to support that as well there
can be both going on the reason I picked
on the ideas I did extrapolation
overconfidence is I think the most
important facts in financial markets do
have to do with sort of extreme
movements with prices getting too high
with them getting too low that can have
some of the bigger effects on the real
economy and things like extrapolation
and overconfidence have been implicated
in those so that's why I focused more on
those but I very much agree that if I
did a fourth thing it would be something
related to under reaction
hi Nick my name is Aditya I'm pursuing
the masters in financial analysis at
London Business School my question to
you is does behavioral finance reinforce
the idea that technical analysis
actually works in the financial markets
because technical analysis also in a way
tries to predict the future price based
on past data and charting involves in a
way gorging fear and greed human
emotions that go into it and if we am
algorithm runs technical analysis and
behavioral finance you think it will
better help a person to predict the
future yeah so I think that behavioral
finance does to some extent provide a
foundation for some of the strategies
used in technical analysis because some
of the behavioral theories say their
past patterns in both prices and volume
will have some predictive power for
future returns so in that sense I do
think it can provide a foundation for
some technical analysis trading
strategies but technical analysis can
get really quite sophisticated and it
can involve very complex patterns in the
data I can't say that behavioral finance
has provided a foundation for those it
probably provides a foundation for some
simple strategies based on simple
patterns in past prices and volume but
hasn't provided a foundation for some of
the more intricate patterns there may be
such a foundation but behavioral finance
hasn't provided as yet
oh hi will mode and lbs alone thank you
very much as the presentation so my
question is where do you see that end
point for behavioral finance do you see
an end point where if all institutional
investors were educates enough you could
have rational market sorry
rational market and then I guess this is
a difficult question to answer but is it
in an institution like a QRS interest to
be educating people about behavioral
finance given that that such a huge
amount of alpha from investment comes
from well I'll stay away from the second
question because I I don't feel I'm in a
position to speak for four AQR I think
there may be some AQR representatives
here they can tackle that question or on
your first one of where behavioral
finance goes I mean there is a somewhat
depressed call it depressing scenario
for those who work in behavioral finance
which is if all of this becomes very
well known very well accepted if
everyone learns all about the buyer
season learns to sort of neutralize them
in their own thinking then perhaps you
could say that we would end up with more
rational markets and then the efficient
markets framework would describe the
world after all so you could say that if
behavioral finance is successful it will
almost sort of defeat itself in some way
I will just bring the world back to
rational markets but my own view is that
that's not likely to happen I think that
a lot of these biases are very deeply
rooted in the human brain I think they
probably developed during the human
evolutionary period they're very deep
deeply rooted they're very hard to get
rid of and therefore I think they're
going to be playing a role in financial
markets for a very long time I just have
this basic intuition there when people
when an asset performs very well a lot
of people just are very drawn to that
they get very excited about it and they
want to buy the asset and that can cause
large dislocations in prices even
bubbles and I think we are going to see
such phenomena for a long period of time
it's just hard to neutralize these
biases in our thinking that's my
prediction but we won't know I guess for
for a while longer what really happens
thank you maybe oh I think we have just
eight seconds left I'm told to stick
strictly on
so thank you very much for your
attention thank you
[Applause]

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