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15 June 2010
Global Strategy

Alternative view

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Popular Delusions
What’s the point of macro?




Dylan Grice
(44) 20 7762 5872
dylan.grice@sgcib.com























Global Strategy Team
Albert Edwards
(44) 20 7762 5890
albert.edwards@sgcib.com

Dylan Grice
(44) 20 7762 5872
dylan.grice@sgcib.com



Most people would see the macro strategist’s role as timing macro events … switching
between defensives and cyclicals, adjusting duration, risk-on/risk-off trades, and so on ... the
only problem is that most of us are rubbish at seeing macro events coming, let alone timing
them, as our evolutionary programming blinds us to events which are forecastable (and many
are not even that). Perhaps we should embrace our limitations by accepting that ‘outlier
events’ are actually quite regular, and use macro research to aid in the search for appropriate
insurance strategies.
Q A few weeks ago I mapped out a strategy that was based on the idea that since global
banks’ solvency was so dependent on government bond holdings, central banks would
have no option but to quantitatively ease in the face of future government funding crises.
I argued that such funding crises could provide opportunities to buy cheap risk assets
before liquidity/QE-induced rallies and that some value was beginning to emerge, but also
that that value still wasn’t extreme enough to go all in.1
Q As usual, I received some interesting feedback – some favourable, some not (one pm
said my ‘deflation-begets-inflation’ view was a “dog’s leg” forecast). But one client asked
why I bothered looking at valuation at all. Surely my extreme macro views trumped such
considerations? “I just don't understand how you can separate your … economic research
from your stand-alone valuation tools.” I thought this was a brilliant question because it gets
to the heart of a permanent tension between macro and micro: what should the relationship
between top-down macro and bottom-up valuation be?
Q At the risk of oversimplifying what our more macro-focused clients do every day I’d
characterize pure macro-focused managers as being less concerned with valuation. For a
start, the traditional macro instruments such as commodities and currencies are difficult to
value. But by far the biggest macro market – the bond market – is largely priced off central
bank perceptions of what the economy is doing, and risk assets tend to be priced off those
bond markets. Since mispriced assets can become even more mispriced depending on the
macro climate and central banks’ reading of it, timing is everything and for such managers
an understanding of the ‘big picture’ is far more important than valuation.
Q But at the opposite end, where the pure value hunters reside, Warren Buffett has said
that even if he knew the Fed’s exact interest rate moves two years in advance it still
wouldn’t make any difference to how he would invest today. Indeed, most value investors
shun macro completely and focus entirely on bottom-up valuations. They view recessions
as good times to buy and have little confidence in anyone’s ability to predict them. But they
don’t really care because they know recessions occur frequently enough and they are
patient enough to wait. So why bother with macro?

1 “Print baby, print … emerging value and the quest to buy inflation”, Popular Delusions, 27/05/2010
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2
At last year’s Value Investing Congress, David Einhorn neatly reconciled the top-down versus
bottom-up investment philosophies. He was describing his Damascene conversion following a
foray into a high quality US homebuilder just before the housing bubble burst:
“At the May 2005 Ira Sohn Investment Research Conference in New York, I recommended
MDC Holdings, a homebuilder, at $67 per share. Two months later MDC reached $89 a
share, a nice quick return if you timed your sale perfectly. Then the stock collapsed with the
rest of the sector. Some of my MDC analysis was correct: it was less risky than its peers
and would hold up better in a down cycle because it had less leverage and held less land.
But this just meant that almost half a decade later, anyone who listened to me would have
lost about 40% of his investment, instead of the 70% that the homebuilding sector lost.
“I want to revisit this because the loss was not bad luck; it was bad analysis. I downplayed
the importance of what was then an ongoing housing bubble. On the very same day, at the
very same conference, a more experienced and wiser investor, Stanley Druckenmiller,
explained in gory detail the big picture problem the country faced from a growing housing
bubble fueled by a growing debt bubble. At the time, I wondered whether even if he were
correct it would be possible to convert such big picture macro-thinking into successful
portfolio management. I thought this was particularly tricky since getting both the timing of
big macro changes as well as the market’s recognition of them correct has proven at best a
difficult proposition. Smart investors have been complaining about the housing bubble
since at least 2001. I ignored Stan, rationalizing that even if he were right there was no way
to know when he would be right. This was an expensive error.
“The lesson that I have learned is that it isn’t reasonable to be agnostic about the big
picture. For years I had believed that I didn’t need to take a view on the market or the
economy because I considered myself to be a “bottom-up” investor. Having my eyes open
to the big picture doesn’t mean abandoning stock picking, but it does mean managing the
long-short exposure ratio more actively, worrying about what may be brewing in certain
industries, and when appropriate, buying some just-in-case insurance for foreseeable
macro risks even if they are hard to time.”
I think most people would agree with this very reasonable hybrid approach: use macro
analysis to avoid economic turbulence by managing your portfolio’s “long-short exposure
ratio” more, and bottom-up analysis to maintain a value bias to the holdings within your
portfolio.
But there is still a problem with the applicability of this philosophy: your ability to ‘actively
manage’ your portfolio’s beta is a function of your ability to accurately call the market’s short-
term direction correctly on average over time. But just because most of us think we are
reasonably competent at calling such short-term moves doesn’t mean we are. In fact, the
reality is that we’re appalling at it.
The future is wild, but our forecasts are mild
One problem is that many of the big moves we’re supposed to “trade around” are
fundamentally unpredictable (Taleb’s Black Swans) and no amount of research will predict
such events. Perhaps a more important thought is that we’re simply not hardwired to see and
act upon the big moves that are predictable (Taleb’s Grey Swans).
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3



To see why, it’s important first to understand the nature of those big “outlier” moves. Benoit
Mandlebrot, the inventor of fractal geometry, distinguished between uncertainty that is “mild”
and that which is “wild.”2 For in a sample from a population that is only mildly random,
extreme occurrences won’t change the estimated characteristics of the population. To use
Taleb and Mandlebrot’s example3, imagine taking 1000 men at random and calculating the
sample’s average weight. Now suppose we add the heaviest man we can find to the sample.
Even if he weighed 600kg – which would make him the heaviest man in the world – he’d hardly
change the estimated average. If the sample average weight was similar to the American
average of 86kg, the addition of the heaviest man in the world (probably the heaviest ever)
would only increase the average to 86.5kg.
With mild distributions, extreme outliers are insignificant to our understanding of the likely
weight of someone randomly chosen from the population. The insignificance of such outliers
makes the uncertainty around people’s height, blood pressure or IQ so mild that fairly
accurate probabilities can be judged using the well-known Gaussian “bell curve” distribution.
The bell curve is usually referred to as the normal distribution because it has known and
convenient properties, yielding safe and predictable probabilities, and we like to think that safe
and predictable is normal.
But it’s not actually that normal. There are plenty of very important variables which are more
“wildly” random and in which outliers make a transformative difference. For example, suppose
instead of taking the weight of our 1000 American men, we took their wealth. And now,
instead of adding to the sample the heaviest man in the world we took one of the wealthiest,
Bill Gates. Since he’d represent around 99.9% of all the wealth in the room he’d be massively
distorting the measured average so profoundly that our estimates of the population’s mean
and standard deviation would be meaningless. If wealth distribution was mild this would never
happen. But it’s not. So it does. If weight was wildly distributed, a person would have to weigh
30,000,000kg to have a similar effect!
The simplest way to see if something is mildly random – if it follows a Gaussian distribution –
is through a QQ plot. You plot the variables of the series you’re interested in on one axis with
manipulations of the same variable (which ensures it is Gaussian by construction) along the
other, and if the scatter plots a neat diagonal line, your distributions roughly match and your
variable is likely normally distributed. If it doesn’t you may have a “wildly” random variable on
your hands.
The following chart shows a QQ plot of three-month changes in various dollar exchange rates
from mid-2006 to the present. Simply eyeballing the data shows that we can’t draw a neat
straight line through the scatter plot, suggesting the data is non-Gaussian (more rigorous
statistical tests show this to be true) and the “fat tails” are clearly visible. It has been known
for some time that financial market variables were not mildly random, but LTCM demonstrated
just how dangerous assuming away wild randomness could be.

2 See “The (Mis)behaviour of markets: A Fractal View of Risk, Ruin and Reward” by Benoit Mandlebrot. In
the “Black Swan” Taleb drew a similar distinction between Mediocristan and Extremistan
3 See “Mild vs Wild Randomness: Focusing on those Risks that Matter” by Benoit Mandelbrot and Nassim
Taleb
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4

Wildly random exchange rate changes: a QQ plot of 3m % changes in various USD rates
-3
-2
-1
0
1
2
3
-20
-15
-10
-5
0
5
10
15
20
"Fat tails" suggest distribution
is non-Gaussian

Source: SG Cross Asset Research
Now take a look at a similar plot below, only this time of forecast 3m changes (I calculated
these using Bloomberg’s history of quarterly forecasts relative to the 3m forward rates
prevailing at the forecast date). Contrast the near perfect diagonal straight line below to the
poorly fitting one above. Forecasts are mildly distributed along Gaussian lines (more formal
statistical tests show this to be true). Thus, even though we know changes in financial market
variables are non-Gaussian, our expectations of those changes remain Gaussian. We see the
world in which we live as mild even though we know it’s wild.
Mildly random expectations: QQ plot of expected 3m % exchange rate changes
-3
-2
-1
0
1
2
3
-5
-4
-3
-2
-1
0
1
2
3
4
5

Source: SG Cross Asset Research, Bloomberg
Why we all think we’re great traders
I was dumbstruck in a recent meeting I did with Albert when one of the attendees, who was a
well known tech bull in the late 1990s, dismissively claimed (with a completely straight face)
that Albert’s call to overweight bonds relative to equities in 1999 was “pretty consensus at the
time really.” This was a time when the tech mania was in full swing, when adding “dot com” to
the end of a company name was enough to double its share price that day, when the Nasdaq
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15 June 2010
5



was trading at a PE in excess of 50x, and when Jim Cramer was telling investors in his hedge
fund that PE ratios were redundant because none of his favourite stocks even had earnings
“so we won’t have to be constrained by that methodology for quarters to come.”4 Yet Albert’s
call to sell stocks and buy government bonds at this time was “pretty consensus”!
Niall Ferguson has given a brilliant example of how such “selective memory” can permeate the
collective psyche. In his book “War of the World”, his fascinating interpretation of the 20th
century’s rolling conflicts, he shows that pretty much any narrative of WW1 will painstakingly
show how all the warning signs of an impending conflict were there and how inevitable the war
must have seemed at the time. Germany and France almost came to blows in 1911 over
Morocco in the Agadir crisis, a European arms race was in full swing as Russia committed in
1912 to rebuilding its military, and the Kaiser’s determination to surpass Britain’s naval
supremacy was intensifying the armaments build-up in Germany and the UK. Indeed, at
Germany’s Imperial War council meeting of 1912, von Molkte even argued (now famously) for
an immediate attack on Russia since war with her was “unavoidable, and the sooner the
better.” The march to war was clearly on, wasn’t it ....
Well yes, with hindsight perhaps, but no-one realised it at the time. The now familiar historical
notion is that a tension was slowly building up between the European powers and that this
reached such a fever-pitch that by 1914, when Archduke Franz Ferdinand was assassinated in
the Balkans, Europe was a like a barrel of gunpowder just waiting for a spark. But as Ferguson
shows in his book, this notion is in fact highly dubious. It implies that everyone knew how
significant the above events were at the time, which they did not.
Or at least, the bond market didn’t. The chart below shows German bond prices (which were
considerably higher than they had been a year earlier) had barely budged following the
assassination of the Archduke, which ultimately triggered the “war to end all wars”!
The German bond market didn’t see the Archduke’s assassination as a war trigger
84.0
84.5
85.0
85.5
86.0
86.5
87.0
87.5
88.0
88.5
89.0
Jan-13Feb-13Mar-13Apr-13May-13Jun-13Jul-13Aug-13Sep-13Oct-13Nov-13Dec-13Jan-14Feb-14Mar-14Apr-14May-14Jun-14Jul-14Archduke Franz Ferdinand
assasinated ...
… exchanges closed
over one month later

Source: SG Cross Asset Research

4“Winners of the New World” by Jim Cramer, speech given at the 6th Annual Internet and Electronic
Commerce Conference and Exposition, 29 Feb 2000
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6
The classic study on hindsight bias was done by Fischhoff and Beyth5 who asked their
students to estimate probabilities for possible outcomes during Nixon’s visit to China in 1972
before it occurred (for example, “what is the probability that the USA will establish a
permanent diplomatic mission in Peking, but not grant diplomatic recognition”). What the
subjects didn’t know was that they would be later asked to recall those probabilities. But
when they did, between two weeks and six months after the visit, they recalled that their
estimate of events that did happen was much higher than their actual estimate had been, and
likewise that their estimate of events that had not transpired had been much lower.
This hindsight bias helps explain our inability to see outliers. If you were perfectly
rational in forecasting returns, an unexpectedly positive or negative number would
widen your volatility estimate. But if you “knew it all along” you wouldn’t accept that the
return was unexpected. Your estimate of potential price volatility would be unchanged and
you’d continue to disregard the possible outlier events as too unlikely because your forecast
range would remain too narrow. This was actually evident when I put the data together for the
QQ charts above. The standard deviation of the actual exchange rate changes was 5.7%; that
for the forecasts was only 1.8%.
This over-confidence bias is well documented. In spelling tests, subjects who mark answers
they’re “100% certain” of are only correct 80% of the time. Where an error of 0% is expected,
one of 20% occurs. But hindsight bias contributes to this natural pre-disposition towards
overconfidence because if you think you predicted past events accurately, you’ll think you can
predict future events too.
If you ask a class of students how many expect to finish in the top 50%, come the end of the
year around 80% will put their hand up. Similarly, I’ve been amazed at the number of clients
who’ve told me they think “buy and hold is dead” and that “these markets have to be traded.”
For traders in the market, the odds are considerably worse than students in exams.
Performance data typically show that around 70-80% of active managers underperform the
indices6 while studies of brokerage accounts show similar odds for individual traders.
There is absolutely nothing wrong with “trading these markets” if that’s what you love doing
and it’s what you’re good at. But the evidence clearly shows that the vast majority of us aren’t.
Worse, the vast majority of those who think they are good at it aren’t either, and they will be
competing against traders who are. So my advice to anyone about to embark upon Einhorn’s
path of using macro to “actively manage your long-short exposure.” is to think long, hard and
honestly about what your sphere of competence actually is. Otherwise, the chances are that
you’ll be making your broker far happier than your investors.
So what is the point of macro research?
So if our confidence in our forecasting ability is for most of us more likely to be reflecting a
cruel trick of our evolutionary development than any real ability, is macro research completely
redundant? I don’t think so. In fact, I agree with the second part of David Einhorn’s conclusion

5 See “I knew it would happen” Baruch Fischhoff and Ruth Beyth (1975)
6 See for example Standard and Poors SPIVA Scorecard results here
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in the excerpt above, of “when appropriate, buying some just-in-case insurance for
foreseeable macro risk”.
At this year’s Berkshire Hathaway conference, Charlie Munger said that while most people
and firms do whatever they can to avoid large losses, Berkshire Hathaway is designed to take
them. “That’s our edge”, he said. When asked about his successor at the helm of Berkshire,
Buffett said that the most important thing his successor at Berkshire must be able to do is “to
think about things which haven’t happened before.” Most insurance companies lose money on
their underwriting operations but make money on the float. Berkshire Hathaway makes profits
on both. They haven’t been able to do this because they’ve been better at predicting the
future than the competition – they openly admit to not even trying – but because their whole
approach is grounded in a) the understanding that “outlier” events happen every few years,
and b) being patient enough to hold capital in preparation for deployment when such “outliers”
inevitably arise.
There are two broad approaches to a more insurance-based approach. The first and most
simple is the avoidance of the purchase of overvalued assets. Ensuring an adequate margin of
safety against the unknown and unknowable future – rather than trying to predict it – is the
central philosophy behind Ben Graham’s concept of value investing and one of the simplest
differences between investment and speculation. It’s as important today as it has always been
and is why a careful and prudent analysis of valuation is so important. This is why I spend
what some might think is an unusual amount of time on equity valuation for a macro strategist.
The second approach is to focus on the “grey swans” - the tail risks which are predictable - by
devoting time to thinking about them and to finding effective and efficient protective insurance
should they happen. Most of the research Albert and I write aims in this direction. It is for most
of us, I believe, a more fruitful use of macro research than trying to predict various markets’
short-term moves. There is a very big difference. Some have interpreted my work on
government solvency as a reason to short government bonds, and JGBs in particular. I’ve
actually never suggested doing this. To get it right you have to get your timing right, and …
well … see the above on how confident most of us (myself included) should be about that.
But just because you can’t predict when something will happen doesn’t mean you should
act as though it won’t happen. If, for example, you are as worried about the implications of
what appears to be widespread public sector insolvency in developed markets as I am, there are
numerous insurance products worth considering. Popular Delusions is of course a strategy
product, and regulatory boundaries preclude me from making too specific recommendations, so
I’m going to end by doing something I very rarely do: shamelessly plug my colleagues (look
away now if you don’t want to be soiled by such unbridled commercialism!).
I think it’s fair to say that derivatives is one of SocGen’s genuine competitive strengths. We
have a world class and award-winning derivatives operation and, as far as I know, compete
favourably with any other house on the street. So if you’re like most of the clients I talk to and
are interested in insuring against any of the scenarios Albert and I have explored in our
research (whether an inflation crisis in Japan, the break-up of the euro, a funding crisis in the
US, a Chinese hard landing) or indeed any that we haven’t, but don’t know the best way to do
so, let me know and I will put you in contact with the appropriate members of the derivatives
team here. Having sat down with them in recent months, seen them work and seen the tail-
event hedges that can be squeezed out of the options market I’d be surprised if you weren’t
impressed at what can be done. I certainly have been.
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8
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