1311 lines
47 KiB
Plaintext
Executable File
1311 lines
47 KiB
Plaintext
Executable File
# AQRAMI
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Data: 11-01-2025 21:49:05
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## Lista de Vídeos
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1. [Addressing Behavioural Biases in Investing: Prof Nicholas Barberis | London Business School](https://www.youtube.com/watch?v=qO6A9q4OoNI)
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2. [[Private video]](https://www.youtube.com/watch?v=6tpCbKD9e3w)
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3. [Uncovering Behavioural Biases with Machine Learning | London Business School](https://www.youtube.com/watch?v=ncqnDl1VM-c)
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4. [Investment Assessment, Personality, Decision-Making & Bias | London Business School](https://www.youtube.com/watch?v=2YYmw1w6-9Q)
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5. [Psychological Drivers of Asset prices & Investor Behaviour | London Business School](https://www.youtube.com/watch?v=uzhuM47DE90)
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6. [A view into what psychological literature says on debiasing judgements and decision making | LBS](https://www.youtube.com/watch?v=eXx8obhvrCs)
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7. [Measuring Culture in Asset Managers | London Business School](https://www.youtube.com/watch?v=oupZyY2GoRA)
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8. [Regulating (and Innovating) for the Real World | London Business School](https://www.youtube.com/watch?v=yu_FL0UwR-Q)
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9. [An emerging economy perspective on the global economy and markets](https://www.youtube.com/watch?v=Jx4H-mwkggc)
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10. [Future proofing pensions integrating the wisdom of John Maynard Keynes and Peter Drucker](https://www.youtube.com/watch?v=-A91urE5zCE)
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11. [Low rates: causes and consequences](https://www.youtube.com/watch?v=OMFV-3sJQhw)
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12. [The dash for cash and the liquidity multiplier: lessons from March 2020](https://www.youtube.com/watch?v=9hUIdSgxovc)
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13. [Presentation of AQR Fellowship Award 2020](https://www.youtube.com/watch?v=w0cHdguT7O8)
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14. [ESG Investing Session Three - Stephen Schaefer and Martin Skancke](https://www.youtube.com/watch?v=Ey7pqWNdv64)
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15. [Diego Kaenzig - Winner of the AQR Asset Management Institute Fellowship Award](https://www.youtube.com/watch?v=UgT2ky-nmEU)
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16. [ESG Investing: evidence on opportunities and challenges](https://www.youtube.com/watch?v=yZr1sKKlEAE)
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17. [ESG investing beyond traditional strategies](https://www.youtube.com/watch?v=pA77F_j2_Ac)
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18. [Corporate responsibility in the age of automation, inequality and climate change](https://www.youtube.com/watch?v=rLZEnPZn1uk)
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19. [Decentralisation in digital finance: possibilities and limits](https://www.youtube.com/watch?v=NWrCYBDG7XA)
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20. [Decentralised finance : opportunities and risks](https://www.youtube.com/watch?v=6m8VNxWLUZs)
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21. [Climate financial risk: portfolios and stress tests](https://www.youtube.com/watch?v=HCm2qAdrae0)
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## Transcrições
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### Addressing Behavioural Biases in Investing: Prof Nicholas Barberis | London Business School
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URL: https://www.youtube.com/watch?v=qO6A9q4OoNI
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Idioma: en
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good morning everyone and thank you for
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that very kind introduction I'm very
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happy to be here for this very
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interesting day on behavioral finance
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I'm going to talk a little bit about the
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topic not for very long I want to leave
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lots of time for any questions or
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comments or reactions that you might
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have I apologize I'm sitting on this
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stool I have a sort of minor leg injury
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so just a little bit of background first
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I think sort of many of you know that
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the modern research era in finance
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started in the 1950s and from the 1950s
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to the 1990s research was really
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dominated by a single paradigm known as
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the rational agent framework and it was
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cool that for good reason because it
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basically assumes that everyone out
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there is fully rational and it's a
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useful and important framework but it's
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become pretty clear over time that a lot
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of important things in financial markets
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don't fit very easily into this
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framework so perhaps not surprisingly
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starting in the 1990s a second framework
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emerged the behavioral finance framework
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and it argues that some financial
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phenomena at least are the result of
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less than fully rational thinking on the
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part of some market pot participants and
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it advocates the use of models that are
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psychologically more realistic there's
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just a sense that the frameworks we've
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been using for decades to think about
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financial markets just aren't
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psychologically realistic enough to
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capture a lot of what goes on in the
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world we need more psychologically
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realistic frameworks and so when I
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started working in this area more than
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20 years ago
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it really wasn't clear how it was gonna
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go how it was going to do but now I feel
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confident saying to you that at least on
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some dimensions behavioral finance has
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been successful it explains many
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observed facts in simple intuitive ways
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it makes testable predictions at least
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some of which have been confirmed in the
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data and there's strong interest not
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just among academics but among
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practitioners and policymakers too
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academic papers in behavioral finance
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have been heavily cited and it's
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received its share of awards as well not
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least the Nobel Prizes to people like
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Daniel Kahneman Robert Shiller and
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Richard Thaler that said it still has
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some
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way to go for example one goal you might
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have is that all finance researchers be
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familiar with the core ideas in
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behavioral finance and apply them as
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appropriate and we're not there yet
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so I think people who work in the field
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like me one task we have is to
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communicate our ideas more broadly and
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so I thought it might be helpful if
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today I try to do just that
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in just a short amount of time so I
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thought specifically what I could do is
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try to pick out 3 ideas from behavioral
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finance that I think are particularly
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helpful for thinking about financial
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markets and the three that I've picked
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out are over extrapolation of the past
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over confidence
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and while I'm calling gain loss utility
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coupled with elements from prospect
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theory I'm going to try to show you
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today that these ideas can explain many
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central facts about asset prices and
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after I've done that I'll just end with
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some broad remarks about progress in the
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field all right so let's dive into the
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first idea over extrapolation it's just
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the idea that when people form beliefs
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about the future they put too much
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weight on the recent past and today I'm
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gonna focus specifically on the idea
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that people extrapolate past returns at
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some level in a very basic idea it's
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just the idea that if the recent returns
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on an asset or asset class have been
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good people are too quick to think that
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the future returns will also be good if
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the recent returns on an asset or asset
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class have been poor people are too
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quick to think that the future returns
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will also be poor and this is an old
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idea you can find it in qualitative
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accounts of financial markets going back
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decades the first wave of formal
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academic research on the topic came
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along in the 1990s and in just the past
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five years or so there's been a second
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wave of research on this topic spurred I
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think in part by renewed attention to an
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interesting kind of data specifically
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survey data so there's a lot of surveys
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that are conducted of investors both
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individual investors and institutional
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investors that simply ask people for
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their forecasts of future stock market
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returns how do you think the stock
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market's going to do over the next
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year and these data provides support for
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the notion of over extrapolation of past
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returns so let me take that in two steps
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first of all the data point to
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extrapolation of past returns if the
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recent returns on the stock market have
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been good people think that the future
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returns will be good if they've recently
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been bad people think that the future
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returns will be bad and you can see this
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in this graph here from one study of
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this topic this is actually Gallup
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survey data as I remember the graph runs
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from 1996 through 2012 the red line is
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in each quarter we're asking people to
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forecast the stock market return over
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the next year and the blue line is at
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that very moment of time how did the
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stock market how had the stock market
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performed over the previous year and you
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can see a close correlation between the
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two lines which simply says if the stock
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market had recently performed well
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people thought that it would keep doing
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well if it has recently performed poorly
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people thought that it would keep doing
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poorly
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so people are extrapolating past returns
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but also the author's show they seem to
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be over extrapolating past returns in a
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sense of their beliefs are incorrect
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what I mean there is if you look at the
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correlation between people's forecasted
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return and what subsequent realized
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returns actually are you'll see
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something of a negative correlation
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where people expect higher returns the
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subsequent return is actually lower than
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average when people expect low returns
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the subsequent return is actually higher
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than average so these data I think have
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helped to spur this new wave of research
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on return over extrapolation and what we
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found in just the past sort of 5-10
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years or so is the models in which some
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investors over extrapolate past returns
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can explain several of the most
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important facts about asset prices for
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example momentum and reversals and
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individual assets time-series
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predictability in aggregate asset
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classes and financial bubbles and if you
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look at that list that's a large chunk
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of what we're trying to understand about
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asset prices so
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it's striking to me the a single simple
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idea can shed light on all of them and
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before I explain the intuition for how
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this works I thought I'd just briefly
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remind you of what these facts say
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although I suspect that some of you are
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well aware of them so first of all
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momentum and reversals so in the
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cross-section of stocks but also in
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other asset classes is what's called
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medium term momentum for example stocks
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with high past six-month returns have
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higher subsequent returns on average
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than stocks with low past six-month
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returns so to make that a little bit
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clearer
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imagine the at some point of time you
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take your database of stocks and you
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rank all the stocks based on how they've
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performed over the past six months
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so some stocks have performed very well
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some stocks have performed very poorly
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it's interesting to see how do these two
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groups of stocks do subsequently and
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what's been found over decades of data
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in the United States but also most
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international markets is that these
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prior winners subsequently have a high
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average return while the prior losers
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subsequently have a low average return
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but we also observe what's called long
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term reversals for example stocks with
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high past three year it turns have lower
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subsequent returns on average than
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stocks with low past three-year terms so
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to be clear if we now take our database
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of stocks and we rank stocks based on
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how they've performed over the previous
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three years so you've got some big
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winners and some big losers it's
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interesting to see how do these two
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groups of stocks perform in the future
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and what's been found over decades of us
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data but most international markets - is
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that these big prior winners
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subsequently have a low average return
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while the big prior losers subsequently
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have a higher average return and a
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long-standing challenge I think for
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finance researchers is to explain both
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of these patterns in a parsimonious one
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second fact while I'm calling
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time-series predictability this is in
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aggregate asset classes so in aggregate
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asset classes raise
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of price to fundamentals predict
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subsequent returns with a negative sign
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for example in the stock market the
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price to earnings ratio of the stock
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market predicts the market subsequent
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return with a negative sign and you can
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see that in this picture from a study by
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Campbell and Shiller so every number in
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this picture is a year over the past
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century the horizontal axis is the price
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to earnings ratio of the market in that
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year the vertical axis is over the next
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ten years the performance of the stock
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market specifically the ten-year price
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growth and you can see you can run a
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regression of course but even visually
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you can see something of a negative
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relationship whereby years with high p/e
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ratios are followed by lower returns
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while years with low p/e ratios are
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followed by higher returns so the stock
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market is not a random walk its future
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return can be predicted at least a
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little bit in advance using ratios like
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this and many economists feel like this
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may be the single most important fact
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about stock market fluctuations that we
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want to try to understand
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and finally bubbles I hardly need to
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explain what these are but basically
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episodes where the price of an asset
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rises dramatically and then collapses
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and during the price rises a lot of talk
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of possible overvaluation as well as -
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and so coming back to our earlier theme
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what we found in the past five or 10
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years is the a framework or an economy
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where some investors over extrapolate
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past returns can shed light on all of
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these basic facts about asset prices and
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I wanted to explain how that works with
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the help of this picture right here so
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what this picture plots if you look at
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the blue line what the blue line is
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plotting is the price path of an asset
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in an economy where some people
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extrapolate past returns following a
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good news about fundamentals here at
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time - so again the price path is this
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blue line sorry is the price path of an
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asset in an economy where some people
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extrapolate past returns following good
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news at
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- and the red line shows you what
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happens if there are no extrapolate us
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but rather all investors are fully
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rational so why does the price path look
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like this so it works as follows
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following the good news at time - the
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price of the asset naturally goes up
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what happens next well here the return
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extrapolate errs look back they see hi
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past returns which makes them think that
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the future return will be good as well
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so they by aggressively pushing the
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price even further on what happens next
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well here the return extrapolate errs
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look back they see two periods of high
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returns that gets them even more excited
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about the future so they buy even more
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aggressively pushing the price even
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further but around here they look back
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and they see that the recent returns
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while positive haven't been quite as
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amazing as before so while remaining
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excited they get a little bit less so
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and the price comes down a little bit
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and here they look back and they see a
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negative return so they become a lot
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less excited and the price collapses
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further and so in that picture you can
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see all of those basic asset pricing
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facts we were trying to explain first of
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all you can see the medium-term momentum
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notice how the high past return is
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followed by more good returns at least
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in the short run and the intuition is if
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an asset has a good past return that
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gets extrapolate is excited about its
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future return which leads them to buy it
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pushing it further up at least in the
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short term but you can also see the
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long-run reversal so notice how the good
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long-term past return is followed by
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lower returns going forward if an asset
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has had a long-term good past return
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that's a sign that extrapolate errs have
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become very excited about it later on
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they become a little bit less excited
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and returns are poor you can see the
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time series predictability - it's around
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here that the price to earnings ratio of
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the asset is particularly high and you
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can see that just as in the data that's
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followed by lower returns again if an
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asset has a high price to earnings ratio
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that's a sign that extrapolate errs have
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become very exciting
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about it later their excitement abates
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and the returns are low and finally if
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the cash flow news here at time two is
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particularly good you're going to get an
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amplified version of this blue price
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path that's going to begin to look
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something like a bubble a big rise in
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price followed by a collapse and that
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leads to one of the main behavioral
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finance theories of bubbles which is
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that bubbles are triggered by good
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fundamental news good news about the
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assets fundamentals that push up the
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price of the asset which then gets
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extrapolate as excited who then buy the
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asset aggressively pushing it further up
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at least in the short term so it's
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striking to me that this simple
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assumption about return extrapolation
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can explain a lot of these basic facts
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about asset prices now if you have a
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good theory it should be helpful for
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investment purposes and I think that's
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the case for return extrapolation as
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well so let me explain what I mean
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return extrapolation again is the idea
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that people use past returns as a basis
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for their forecasts of future returns
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but the interesting thing is that the
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past returns they look at or which past
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returns they look at seems to vary over
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time sometimes they're focused just on
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the past few months of returns sometimes
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they seem to look at the past few years
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of returns and you can see that in this
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picture here which goes from 1992
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through to 2012 the Green Line is the
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S&P 500 during that time and the blue
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line shows you which returns investors
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seem to be looking at when forming
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forecasts about the future as judged by
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the survey evidence so when this blue
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line is high that means that people are
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focusing on just the past few months of
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returns when this blue line is low that
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means they're actually looking at the
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past few years of returns so you can see
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for example that in the late 1990s
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people were increasingly looking at just
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recent returns so that's interesting in
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and of itself but it's also useful for
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investment purposes and there's an
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interesting recent paper by Casella and
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Golan that sort of makes this point
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specifically what they show is in the if
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investors have currently have been
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waiting recent returns heavily as judged
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by the surveys this signals a market
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inflection point specifically if the
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stock market is overvalued and recent
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returns are being heavily weighted by
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investors then a short-term correction
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is more likely so this helps to answer a
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long-standing question that
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practitioners that I think are
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particularly interested in their
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Laughton ask look if the price to
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earnings ratio of an asset is high that
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suggests that it might be overvalued and
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due for a correction but when will the
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correction occur what this study shows
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is that survey data helps to provide an
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answer if the price to earnings ratio is
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high and investors seem to be very
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heavily focused on just recent returns
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then a correction is more likely in the
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short term and there's good intuition
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for that which is suppose investors are
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very focused on just short-term past
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returns and then they're supposed that
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there's just a one piece of bad news
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that brings the market down a little bit
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then precisely because people are
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focused on the very recent past that
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could trigger a large change in market
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sentiment and hence a large collapse in
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the price of the asset so this idea of
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return extrapolation not just helpful
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for understanding basic facts but
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potentially also for investment purposes
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so that was the first idea I wanted to
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discuss with you the second idea I've
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decided to talk about is overconfidence
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now overconfidence is a broad term and I
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wanted to distinguish two kinds of
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overconfidence so there's one kind
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called over placement and this refers to
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the idea that people seem to have overly
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rosy views of their abilities relative
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to other people and it's the kind of
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thing that's most easily established in
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surveys where these sort of 80% of
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people declare themselves to be above
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the median on various dimensions like
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attractiveness sense of humor driving
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ability ability to get along with people
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at least 80% think they're above the
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median obvious
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that's not possible many people must be
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deluding themselves that's one kind of
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overconfidence another kind is over
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precision this refers to the fact that
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people seem to be too confident in the
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accuracy of their beliefs this is
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usually established by asking people for
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confidence intervals so I could ask you
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a question like how many petrol stations
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are there in some region but ask you not
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just for an estimate but also your 90%
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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]
|
|
|
|
---
|
|
|
|
### [Private video]
|
|
URL: https://www.youtube.com/watch?v=6tpCbKD9e3w
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Uncovering Behavioural Biases with Machine Learning | London Business School
|
|
URL: https://www.youtube.com/watch?v=ncqnDl1VM-c
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Investment Assessment, Personality, Decision-Making & Bias | London Business School
|
|
URL: https://www.youtube.com/watch?v=2YYmw1w6-9Q
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Psychological Drivers of Asset prices & Investor Behaviour | London Business School
|
|
URL: https://www.youtube.com/watch?v=uzhuM47DE90
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### A view into what psychological literature says on debiasing judgements and decision making | LBS
|
|
URL: https://www.youtube.com/watch?v=eXx8obhvrCs
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Measuring Culture in Asset Managers | London Business School
|
|
URL: https://www.youtube.com/watch?v=oupZyY2GoRA
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Regulating (and Innovating) for the Real World | London Business School
|
|
URL: https://www.youtube.com/watch?v=yu_FL0UwR-Q
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### An emerging economy perspective on the global economy and markets
|
|
URL: https://www.youtube.com/watch?v=Jx4H-mwkggc
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Future proofing pensions integrating the wisdom of John Maynard Keynes and Peter Drucker
|
|
URL: https://www.youtube.com/watch?v=-A91urE5zCE
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Low rates: causes and consequences
|
|
URL: https://www.youtube.com/watch?v=OMFV-3sJQhw
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### The dash for cash and the liquidity multiplier: lessons from March 2020
|
|
URL: https://www.youtube.com/watch?v=9hUIdSgxovc
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Presentation of AQR Fellowship Award 2020
|
|
URL: https://www.youtube.com/watch?v=w0cHdguT7O8
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### ESG Investing Session Three - Stephen Schaefer and Martin Skancke
|
|
URL: https://www.youtube.com/watch?v=Ey7pqWNdv64
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Diego Kaenzig - Winner of the AQR Asset Management Institute Fellowship Award
|
|
URL: https://www.youtube.com/watch?v=UgT2ky-nmEU
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### ESG Investing: evidence on opportunities and challenges
|
|
URL: https://www.youtube.com/watch?v=yZr1sKKlEAE
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### ESG investing beyond traditional strategies
|
|
URL: https://www.youtube.com/watch?v=pA77F_j2_Ac
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Corporate responsibility in the age of automation, inequality and climate change
|
|
URL: https://www.youtube.com/watch?v=rLZEnPZn1uk
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Decentralisation in digital finance: possibilities and limits
|
|
URL: https://www.youtube.com/watch?v=NWrCYBDG7XA
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Decentralised finance : opportunities and risks
|
|
URL: https://www.youtube.com/watch?v=6m8VNxWLUZs
|
|
|
|
Transcrição não disponível
|
|
|
|
---
|
|
|
|
### Climate financial risk: portfolios and stress tests
|
|
URL: https://www.youtube.com/watch?v=HCm2qAdrae0
|
|
|
|
Transcrição não disponível
|
|
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|
---
|
|
|