ineichen research & management the 4% rule applied · pdf file · 2016-11-24the...

Executive summary

The 4% rule was developed by Ned Davis and popularised by

Martin Zweig in Winning on Wall Street in the 1980s. The basic

idea is that if an index moves by 4%, it is worth our attention.

Something meaningful happened and we should pay attention. We

herein apply this rule more broadly and test whether a variant of

this rule is usable and/or applicable to risk management.

Two aspects about investment management in general and risk

management in particular are meaningfulness of a change in

circumstance and an investor’s conviction in a certain risk exposure.

First, a lot of information every investor is bombarded with on a

daily basis is simple not that important, i.e., meaningless. Second,

position sizing and/or hedging are often a function of an investor’s

conviction, i.e., his confidence in his analysis and reasoning. Large

swings in a variable can help with both, determining meaning and

importance of the information as well as helping the investor to

articulate or quantify the conviction in a certain idea or risk

exposure.

We test the 4% rule on the S&P 500 and were surprised to find

that there is after all a bad backtest.

We apply the idea behind the 4% rule to economic variables. We

find that large changes can mark important turning points but there

are false signals too and not all turning points are highlighted by

large moves. Paying attention to high standard deviation changes

makes sense but the Holy Grail of finance it is not. Large changes

should be treated as an indication of meaningfulness rather than a

solid signal one can blindly adhere to.

When assessing economic variables the most important thing is the

trend, i.e., the economic momentum. The momentum itself is—to a

large extent—a fact, not someone’s opinion or forecast. The second

most important thing to look for is the reversal of the trend. Only

then, third, should we look at the extent of the magnitude, i.e.,

standard deviation of a particular move.

Risk management research

The 4% rule applied

26 September 2014 Alexander Ineichen CFA, CAIA, FRM +41 41 511 2497 [email protected] www.ineichen-rm.com “I measure what's going on, and I adapt to it. I try to get my ego out of the way. The market is smarter than I am so I bend.” —Martin Zweig

Ineichen Research & Management (“IR&M”) is an independent research firm focusing on investment themes related to absolute returns and risk management.

mailto:[email protected]

http://www.ineichen-rm.com/

The 4% rule applied 26 September 2014

Ineichen Research and Management

Content

On a personal note .............................................................................................. 3

Introduction ..................................................................................................... 3

The 4% rule ...................................................................................................... 6

Applying the “4% rule” to economic variables .............................................. 11

Testing US PMI ........................................................................................... 11

Germany’s ZEW ......................................................................................... 18

Bottom line .................................................................................................... 19

References ......................................................................................................... 20

Publications ....................................................................................................... 21



On a personal note

“I can’t overemphasize the

importance of staying with the trend

in the market, being in gear with the

tape, and not fighting the major

movements. Fighting the tape is an

open invitation to disaster.”

—Martin Zweig (1942-2013),

US investor1

Introduction

I—like so many other market observers, investors, analysts, and practitioners—

have been making fun of the Efficient Market Hypothesis (EMH), rational

expectations, frictionless markets and the normal distributions for a long time.

Some daily market movements are so large, that if the normal distribution would

apply to security prices, the age of the universe would need to be far older for the

event to occur only once. This report excludes the nagging.

Nassim Taleb, in the Black Swan, makes the wonderful and very insightful case

that a normal distribution applies to people’s height but not their wealth. This

means that the normal distribution is applicable in the natural world but is not

applicable for economic or socio-economic phenomena. With height there are no

outliers. If the average height is, say, 1.7m then there is no one who is 1.7mm or

1.7km tall. The height of all people is normally distributed around a mean. Wealth

isn’t. Manuel Valls, France’s youthful PM, has an official wealth of, after 31 years

in “business”, EUR1589.88 and a small apartment.3 Some French people living in

South Kensington or Brussels or Switzerland have, by a factor in the thousands,

much more than that with the normal distribution not even close to being

applicable.

This report is not about all that. In this report I will assume that the normal

distribution actually has its uses when trying to understand what’s going on. I

believe two aspects about investment management in general and risk

management in particular are meaningfulness of a change in circumstance and an

investor’s conviction in a certain risk exposure. First, a lot of information every

investor is bombarded with on a daily basis is simple not that important, i.e.,

meaningless. Second, position sizing and/or hedging are often a function of an

investor’s conviction, i.e., his confidence in his analysis and reasoning. The normal

distribution can help with both, determining meaning and importance of

1 Zweig (1986), p 121.

2 Financial Times, 31 December 2012

3 NZZ am Sonntag, 31 August 2014

“Of all the ways of defining man, the

worst is the one which makes him

out to be a rational animal.”

—Anatole France (1844–1924),

French novelist and winner of the 1921

Nobel Prize for Literature

“France remains an attractive

country for investment. We have no

intention to make France

inhospitable, but we are in a period

of crisis. It is logical that the

wealthiest should make a

contribution.”

—Pierre Moscovici, then French

Minister of Finance2

“Confidence is what you have

before you understand the

problem.”

—Woody Allen



information as well as helping the investor to articulate or quantify the conviction

in a certain idea or risk exposure. Figure 1 shows one example of applying the

normal distribution to, in this case, economic variables.

Figure 1: Applying the normal distribution

Source: IR&M risk management research flash update, 1 September 2014

The presentation of the Purchasing Manager Indices (PMI) in the updates has

recently been changed to reveal more information on one page with less text.

This allows the quick reader to get a picture of what is going on within

seconds but also allows the analyst to examine detail. The red and green

arrows mark the direction of the most recent change. The red and green balls

show whether the change is further from the mean by more than 0.5 standard

deviations. (I probably need to increase that a bit to reduce the number of

balls. The balls should help the quick reader to see what’s going on in a

speedy and efficient fashion; guiding the eye to what is meaningful so to

speak.)

When arguing in standard deviation terms we implicitly assume that these

variables are normally distributed. They most likely are not. However, using

standard deviation allows comparing different reports that have different

methodologies and thereby different degrees of “wobbliness”. It allows

getting a sense for what is meaningful and what is not. It’s a practitioners’

approach.

“Keep your eyes on the ball.”

—Ball sport wisdom



In the update shown above, both Italy and Australia were mentioned. The PMI

indicators are from two different data providers. The former has a standard

deviation (sd) of 1.3 while the later has one of 4.0. This means Australia’s PMI

jumps about and can be essentially all over the place. In two out of three

months, the PMI is plus or minus four points of the previous month;

approximately.

On 1st September, Italy’s PMI fell by 2.1 points to 49.8 while Australia’s PMI

fell by 3.4 points to 47.3. One important aspect, of course, is that both fell

below 50 as these are diffusion indices and below 50 means the economy is

contracting. (Although 49.8 is obviously not meaningfully below 50.) But given

the difference in variability of these two measures, 1.3 and 4.0 standard

deviations, these changes are not as close. Italy fell by 1.6 standard deviation

(2.1 pts / 1.3 sd) while Australia only fell by 0.9.

The bottom line is that Italy’s fall was much more meaningful than the fall of

Australia’s. Furthermore, according to our nowcasting from the latest regular risk

management research update (29 August), economic momentum in Italy was

declining while rising in Australia. If the investor has a positive opinion on both

countries, it is more important to check one’s view on Italy. One’s conviction in

both bullish views must have fallen more in the case of Italy as the fall was more

meaningful, especially in light of all the other information on the country’s

economic and political prowess.

The motivation for this report came when viewing the following chart in May.

Figure 2: Apple

Source: stockcharts.com

Apple’s share price rose 8.2% on 24th April after the company announced it

would split its share price. This translates into a move of 5.3 standard

1 Handelsblatt, 13 March 2013

“Italy is de facto already out of the

euro. The country is on its knees...

The northern European countries

are only holding onto us until their

banks have recouped their

investments in Italian sovereign

bonds. Then they’ll drop us like a

hot potato.”

—Beppe Grillo1

“The crime is not in being wrong, it

is in staying wrong.”

—Martin Zweig

“There's no chance that the iPhone

is going to get any significant

market share. No chance. It's a $500

subsidized item. They may make a

lot of money. But if you actually

take a look at the 1.3 billion phones

that get sold, I'd prefer to have our

software in 60% or 70% or 80% of

them, than I would to have 2% or

3%, which is what Apple might get.”

—Steve Ballmer, April 2007

[Editor’s note: the iPhone’s market

share is currently around 41.4%.]

http://en.wikipedia.org/wiki/Nowcasting_(economics)



deviations.1 If the normal distribution were meaningful, such a daily event

should occur once every 6.6 million years.

With the benefit of hindsight the 5.3 standard deviation move was meaningful.

The funny thing is; it was meaningful not just with the benefit of hindsight. The

event was meaningful as it occurred. For better or worse; it prompted this report:

a revisit of 4%-rule.

The 4% rule

The 4% rule was developed by Ned Davis and popularised by Martin Zweig in

Winning on Wall Street in the 1980s. The basic idea is that if an index moves by

4% in a week, it is worth our attention. Something meaningful happened and we

should pay attention. The idea is that a move of more than 4% is a beginning of a

new trend or the continuation of an existing trend. Martin Zweig used the Value

Line Index which contains 1700 stocks and is equally weighted.3 One standard

deviation of weekly returns was roughly 2% at the end of the 1980s for a volatility

of 14.4%.

Martin Zweig on the virtue of the 4% rule:

The virtue of this Four Percent Model, or any trend-following model, is that if

the market makes a very large move, you will be on the right side of the bulk

of it. But there is no free lunch in the stock market. Although you are

guaranteed of being on the right side of major moves, you may get

whipsawed over very short-term movements. If the market were to zig and

zag by moves only a little bit greater than 4%, you might be zagging when

you should be zigging and zigging when you should be zagging. That will cost

you some money, but in the long-run results of the Four Percent Model clearly

show that it is worth that cost. 4

This quote also applies to IR&M’s Momentum Monitor, an add-on to the risk

management research effort. No model or approach is the Holy Grail. It ought to

be helpful to the practitioner. That’s good enough.

Martin Zweig’s most famous quote is most likely his pacifist remark in relation to

the Fed pumping liquidity into the market:

Don’t fight the Fed.5

This means that, according to Zweig’s theory, if interest rates were going down,

stocks would go up, and vice versa. He also claimed the way to make money was

to be risk-averse, rather than taking chances on the upside. He said he was a big

poker player while at Wharton, but had stopped playing when he became a

money manager because he hated losing, even at cards. One of his major pieces of

advice was never to hold stocks, even of the best companies, in a bear market,

1 Volatility is roughly 25% which translates into a daily standard deviation of 1.55%. This means a 8.2% move is a 5.3

standard deviation event. (Mean daily return is roughly zero.)

2 Letter to Roy Harrod written in the summer of 1938, The Collected Writings of John Maynard Keynes, Vol. XIV, The

General Theory and After, Part II: Defence and Development, Macmillan Cambridge University Press 1973.

3 Zweig (1986), p. 92.

4 Ibid., p. 93

5 Ibid, p. 42, title to Chapter 4.

“To convert a model into a

quantitative formula is to destroy its

usefulness as an instrument of

thought.”

—John Maynard Keynes (1883-1946),

British economist2

“The Fed’s margin account is far

larger than is yours or ours.”

—Dennis Gartman



since even they could disappoint. Since the 2008 financial crises, “Don’t fight the

Fed” has been the most important rule for the US stock market. The equity market

only started to fall when monetary programs (QE1, QE2, etc.) expired. According

to this rule, one ought to remain long equities as long as the Fed keeps pumping

liquidity into the market. The Greenspan put turned into the Bernanke put which is

now, presumably, the Yellen put. Those who have been calling for the day of

reckoning have been waiting in vain.

Newton’s first law of motion, discovered by Galileo and formulised and

popularised by Newton, is often referred to in relation to trends: once they are on

their way, they keep on going until something stops them. Galileo (1564-1642),

who died in the year Newton was born, first discovered the importance of

acceleration in dynamics. He opposed to consensus view of moving bodies ceasing

to move if left alone; as Bertrand Russell put it in 1946:

Galileo held, as against this view, that every body, if left alone, will continue to

move in a straight line with uniform velocity; any change, either in the rapidity

or the direction of motion, requires to be explained as due to the action of

some ‘force’. This principle was enunciated by Newton as the ‘first law of

motion’.1

The 4% rule, one could argue, is the ‘force’. The EUR/USD spot, for example, rose

from 1.2 in July 2012 to 1.4 in May 2014; surprisingly to many (including this

author). However, that was the trend, a measurable fact at the time. In May there

were two high standard deviation moves on the downside. That was the ‘force’

that changed the direction of motion. The trend has been downward ever since.

The aforementioned strong downward moves occurred on 8th and 9th May as a

result of Draghi talking the EUR down. The two moves in standard deviation terms

were 1.1 and 1.9 standard deviation moves. In other words, the change in

direction was spread over two days. The numbers do not matter that much. It is

the violence, the ‘force’, that signals the change in direction. The practical

relevance is that one doesn’t need forecasting; nowcasting is sufficient.

The turn of the EUR against most currencies was flagged in our Momentum

Monitor from 12th May, as the swings were large enough for the trend reversal

being picked up by our measure for medium-term momentum. The reversal of

long-term momentum of the EUR/USD spot to negative was flagged on 7th July,

i.e., much later at around a level of 1.35. At the time of writing these lines, the

afternoon of 4th September, the EURUSD spot rate was in freefall to 1.2940, after

Draghi unleashed some more of his “magic”.

Figure 3 shows the Value Line Index with changes that are larger than 4% in both

directions shown as vertical bars.

1 Russell (2004), p. 489.

2 Reuters, 12 July 2014

“The more people who believe

something, the more apt it is to be

wrong. The person who's right often

has to stand alone.”

—Soren Kierkegaard (1813-1855),

Danish philosopher

“There is no safety in numbers, or

in anything else.”

—James Thurber (1894-1961),

American author

“It is clear that monetary policy,

when seen from a German

viewpoint, is too expansive for

Germany, too loose. If we pursued

our own monetary policy, which we

don’t, it would look different.”

—Jens Weisman2



Figure 3: Value Line Index with weekly returns larger than 4%

Source: IR&M, Bloomberg

In the 1980s there were fewer signals as the market make-up and volatility has

changed.

The most recent signal from January 2013 was a positive one.

Figure 4 shows the same chart with rolling standard deviation based on a rolling

one-year window.

Figure 4: Value Line Index with standard deviation


The standard deviation in the 1980s was 2.1. In the 1990s it was 1.7 and in

the 2000s it doubled to 3.4. In this decade to August 2014 the standard

deviation is 2.6. The standard deviation over the full period of the index since

1983 is 2.5. This means an absolute target, of say 4% in a week, will give

different signals in different volatility regimes.



The main purpose of this report is to elaborate on the thinking, the idea, behind

the 4% rule rather than making the case for using 4%. Nevertheless, a short test is

in order. I use the S&P 500 Index for this test as it is the more familiar index. Figure

5 shows the S&P 500 price index with returns larger than 4% represented as bars.

Figure 5: S&P 500 Index with weekly returns larger than 4%


4% returns in the S&P 500 Index are clustered as they are with the Value Line

Index and probably every other index. (Professor Robert Engle got the Nobel

for formulising this observation.)

The standard deviation of weekly returns since 1980 is 2.3 which annualised

translates into a volatility of 16.6%.

Figure 6 is the equivalent of Figure 4 but for the S&P Index.

Figure 6: S&P 500 Index with standard deviation


Note that the trough of volatility is always in the middle of the decade. Clearly,

this must be a coincident. Crash in 1987, Asian crisis in 1997, Subprime mess

starting in 2007; clearly we need not worry about 2017 approaching.

“Great minds discuss ideas.

Average minds discuss events.

Small minds discuss people.”

—Eleanor Roosevelt



Test

This is odd: a backtest that doesn’t look great. What happened to the saying:

“there’s no such thing as a bad backtest.”

Figure 7 shows two portfolios, a long-only strategy in the S&P 500 and a rule-

based strategy. The latter incorporates the 4%-rule. The rule was to go long when

the weekly return was larger than 4% and stay long until the next sell signal; and

to go short when the weekly return was worse than -4%.

Figure 7: Two portfolios (1980 – August 2014)


Based on weekly date. A 20bp fee per transaction was assumed for the rule-based strategy.

The rule worked for a while and then stopped.

I was surprised by how bad the results looked optically, prompting to check

whether I’ve made a mistake. However, there is an unanticipated lessen:

Something that seems working for a while can stop. Figure 8 shows the calendar

year returns of the two portfolios.

Figure 8: Calendar year returns and return differences of the two portfolios (1980 – August 2014)


Based on weekly date. A 20bp fee per transaction was assumed for the rule-based strategy.

Year Long-only Rule-based Difference Year Long-only Rule-based Difference

1980 28.2 25.4 -2.8 1998 30.9 -2.0 -33.0

1981 -10.3 15.3 25.5 1999 19.8 11.8 -8.0

1982 14.8 14.8 0.0 2000 -10.1 -24.5 -14.3

1983 17.3 17.3 0.0 2001 -12.1 42.6 54.6

1984 0.8 0.8 0.0 2002 -24.6 -26.9 -2.3

1985 26.1 26.1 0.0 2003 25.2 14.5 -10.7

1986 17.8 -13.6 -31.4 2004 10.6 10.6 0.0

1987 2.1 56.1 54.1 2005 3.0 3.0 0.0

1988 10.2 0.9 -9.3 2006 13.6 13.6 0.0

1989 27.3 16.8 -10.4 2007 4.2 -9.8 -14.1

1990 -7.0 -8.5 -1.5 2008 -41.0 -39.8 1.2

1991 23.6 23.6 0.0 2009 29.1 14.6 -14.5

1992 8.2 8.2 0.0 2010 11.6 19.1 7.5

1993 6.1 6.1 0.0 2011 0.0 -24.4 -24.4

1994 -1.5 -1.5 0.0 2012 11.5 -5.9 -17.4

1995 34.1 34.1 0.0 2013 31.3 19.6 -11.7

1996 22.9 22.9 0.0 2014 8.8 8.8 0.0

1997 23.7 23.7 0.0

“If something cannot go on forever,

it will stop.”

—Herbert Stein’s Law, Herbert Stein

(1916-1999)



The rule does not result in outperformance when executed passively. However, as

mentioned before, I believe it is the thinking behind the idea that is of value. One

ought to pay attention to large changes in direction.

Applying the “4% rule” to economic variables

Testing US PMI

The following analysis is based on the PMI (Purchasing Manager Index) from the

Institute for Supply Management (ISM) in the United States. It’s a diffusion index

which means it oscillates around 50 without a long-term drift. A PMI reading over

50 indicates growth or expansion as compared to the previous month, while a

reading under 50 suggests contraction.

Another key number to watch in relation to the PMI, according to Investopedia, is

42.2, since a PMI index above this level over a period of time indicates an

expansion of the overall economy. The August 2014 PMI reading of 59.0 indicates

that the U.S. economy expanded for the 63rd consecutive month, as the PMI first

surpassed the 42.2 level in that expansion in June 2009, which—coincidentally or

not—also marked the start of the post-credit crisis U.S. recovery as determined by

the National Bureau of Economic Research.

Figure 9 shows the PMI since its inception, January 1948.

Figure 9: ISM PMI (1948 – August 2014) with monthly changes


70% of observations are at or above 50 and 91% are above 42.2.

http://www.investopedia.com/articles/investing/010914/importance-purchasing-managers-index-pmi.asp



Standard deviation for the whole time series is 2.7 points. However, the two-

year standard deviation ranges between 5.2 (summer 1951) and 1.2 (autumn

2007).

It is large changes that are of interest in this report. Figure 10 shows the PMI with

monthly changes that are higher than two standard deviations based on the

previous three years. (I use a rolling standard deviation because the range of

variability over this long period is so large.)

Figure 10: US PMI (1948 – August 2014) with monthly changes >= 2 standard deviation


Judging by the graph alone suggests that, yes, large changes can mark

important turning points but there are false signals too and not all turning

points are highlighted by large moves.

This observation is probably consistent with most investor’s intuition, i.e., it might

be helpful but a fool-proof system it is not. The following analysis gives a bit more

colour, literally, to the signals by decade. In the following tables the signal larger

than two standard deviations is highlighted, the change (in points, not standard

deviation) is shown, as well as the PMI after one month, two, three, six and 12

months. As an example: the first signal in the first table worked well: a sharp,

high-standard-deviation fall indicated that things were about to get worse. In a

nutshell, this is what I try to detect when not just highlighting change in the

updates but also try to get a feel for the significance. A large change to the worse

is a red flag; a minor change isn’t. (And I often hope it’s as simple as that.)

1 Schumpeter, Joseph A. (2003) “Capitalism, Socialism and Democracy,” Taylor & Francis, p. 76, footnote 3 in relation to

Karl Marx being silly. First published in 1943.

“It is [however] always important to

remember that the ability to see

things in their correct perspective

may be, and often is, divorced from

the ability to reason correctly and

vice versa. That is why a man may

be a very good theorist and yet talk

absolute nonsense…”

—Joseph A. Schumpeter (1883-1950),

Austrian economist1

http://digamo.free.fr/capisoc.pdf



1950s

Figure 11 shows PMI in the 1950s. Official recessions were from July 1953 to May

1954 and August 1957 to April 1958.

Figure 11: PMI in the 1950s


The strong move in April 1951 worked very well. The PMI was more than ten

points lower three months later.

The negative signal in June 1955 was a bit early.

The positive signal in May 1958 was spot on. It marked the end of the

recession reasonably precise. It was also the move that cut through 42.2 from

below. However, the three other positive signals were a mixed blessing.

1960s

Figure 12 shows PMI in the 1960s. Official recessions were from April 1960 to

February 1961 and from November 1969 to November 1970.



The 2.2 standard deviation negative move from February 1960 was spot on,

two months prior to the start of the recession which always is made official

long after it started.

The positive April 1961 signal was spot on too, informing the investor that the

recession is over before it was made official.

The May 1966 signal was weird as the PMI was only lower than May 1966 six

and twelve months after the signal. However, 1966 was a difficult stock

market year. The two worst returns of the S&P 500 were in May and August

where the market was down 5.4% and 7.8% respectively for an annual loss

of 13.1%. (The report was probably released in the first few days of June

1966, i.e., too late to avoid the May fall. This is a good example of correlation

not proving causality. Economic surveys do not only influence the stock

market; market moves influence the survey participants too. It’s reflexive, as

George Soros might say.)

Date Change Change to

1M 2M 3M 6M 12M

30/04/1951 -12.0 53.5 50.7 45.5 42.1 49.6 36.7

31/08/1952 12.1 60.4 56.1 56.2 56.8 55.4 43.5

30/06/1955 -6.2 63.3 66.2 64.8 62.4 65.6 47.7

31/08/1956 7.3 51.5 55.5 52.7 55.0 51.0 45.3

31/05/1958 7.5 46.6 51.4 54.7 57.3 62.7 68.2

31/12/1959 7.6 58.2 61.5 52.3 47.8 44.4 44.3

PMI after x months


1M 2M 3M 6M 12M

29/02/1960 -9.2 52.3 47.8 45.3 42.6 47.6 43.6

30/04/1961 8.5 57.6 58.9 58.1 58.2 62.2 55.1

31/05/1966 -6.5 57.7 59.0 60.3 58.5 53.7 44.5

PMI after x months

“Market prices have a notorious

habit of fluctuating.”

—George Soros

http://www.ft.com/intl/cms/s/2/0ca06172-bfe9-11de-aed2-00144feab49a.html#axzz3EQtFUiV4



1970s

Figure 13 shows PMI in the 1970s. Official recessions were from November 1969

to November 1970, as mentioned before, and from November 1973 to March

1975.



The December 1970 signal worked very well as it was very close with the end

of the recession that is made official typically long after it ended, i.e., the PMI

is faster than the gatekeepers to “official recessionness”. It was also a move

which took the aforementioned 42.2 level from below.

The February 1971 signal, one could argue, suggest continuation of the

expansion.

The negative July 1973 signal was too early. The recession started in

November of that year.

The negative September 1974 signal below 50 was a good signal, implying

continuation of a difficult market environment, i.e., recession.

The negative December 1974 signal to 31 marked either continuation or the

trough of the recession.

1980s

Figure 14 shows PMI in the 1980s. Official recessions were from January 1980 to

July 1980 and July 1981 to November 1981.



The February 1980 jump to 50.2 was a misfire, an outlier. The PMI fell below

50 in August 1979 and stayed below 50, ignoring the outlier, until September


1M 2M 3M 6M 12M

31/12/1970 5.7 45.4 47.9 54.8 51.2 53.8 57.6

28/02/1971 6.9 54.8 51.2 54.5 54.2 53.6 60.6

31/07/1973 -7.2 57.8 62.7 63.5 66.2 62.1 54.8

30/09/1974 -6.7 46.2 42.7 37.9 30.9 31.6 54.4

31/12/1974 -7.0 30.9 30.7 34.4 31.6 45.1 54.9

PMI after x months


1M 2M 3M 6M 12M

29/02/1980 4.0 50.2 43.6 37.4 29.4 45.5 48.8

31/03/1980 -6.6 43.6 37.4 29.4 30.3 50.1 49.6

30/04/1980 -6.2 37.4 29.4 30.3 35.0 55.5 51.6

31/05/1980 -8.0 29.4 30.3 35.0 45.5 58.2 53.5

31/08/1980 10.5 45.5 50.1 55.5 58.2 48.8 48.3

28/02/1983 8.4 54.4 53.9 54.2 56.1 63.1 61.3

31/01/1984 -9.4 60.5 61.3 58.9 61.0 56.1 50.3

31/05/1986 3.7 53.4 50.5 48.0 52.6 51.2 57.2

31/08/1986 4.6 52.6 52.4 51.2 51.2 52.6 59.3

31/01/1987 4.4 54.9 52.6 55.0 55.5 57.5 57.5

PMI after x months



1980. The fall below 50 in March 1980 was meaningful and followed by

further falls.

The other signals were worth paying attention to but not much more; they fell

under the nice-to-know category rather belong to the essential-investor-

survival kit. More often than not did the trend follow the direction of the

sharp move.

1990s

Figure 15 shows PMI in the 1990s. The only official recession in the good old

1990s was from July 1990 to March 1991. The Tequila crisis was in December

1994 and the Asian crisis with all its ripple effects started to unfold in July 1997.



The negative July 1990 signal was spot on, implying recession long before it

became official and was helpful in holding up a red flag ahead of the S&P

500’s fall from 400 to 300.

The June 1991 and February 1992 signals informed about trend continuation

and were probably not that meaningful. The end of the recession was in

March 1991.

The December 1994 signal marked the Tequila crises but was best ignored by

equity investors as the stock market went up in a straight line during 1995.

The same is true for the negative signal in May 1995, a misfire.

The last three signals larger than two standard deviations in the 1990s were

positive but not very meaningful. There was no hint ahead of the 10%

correction in 1997 and the 25% correction in the S&P 500 in 1998. The Asian

crisis was not related to the US economy and the LTCM episode was

endogenous risk, i.e., also not (directly) related to the business cycle of the US

economy.

2000s

Figure 16 shows PMI in the 2000s. Official recessions were from March to

November 2001 and December 2007 to June 2009. Early in the decade, the TMT

bubble burst. Enron filing for bankruptcy and 9/11 were in 2001. The post-TMT

low in the S&P 500 was in October 2002.


1M 2M 3M 6M 12M

31/07/1990 -2.6 46.6 46.1 44.5 43.2 39.2 50.6

30/06/1991 5.8 50.3 50.6 52.9 54.9 46.8 53.6

29/02/1992 5.4 52.7 54.6 52.6 55.7 53.4 55.2

31/12/1994 -3.1 56.1 57.4 55.1 52.1 45.9 46.2

31/05/1995 -4.8 46.7 45.9 50.7 47.1 45.9 49.1

31/07/1995 4.8 50.7 47.1 48.1 46.7 45.5 49.7

30/06/1996 4.5 53.6 49.7 51.6 51.1 55.2 54.9

31/01/1999 3.8 50.6 51.7 52.4 52.3 53.6 56.7

PMI after x months





The negative December 2000 signal was worth one’s attention but a bit late.

The S&P 500 had peaked above 1500 once in March and then in September

of that year. With the wonderful benefit of hindsight it was more important to

pay attention to the PMI falling below 50 which it did in August 2000. The

PMI stayed below 50 until February 2002. If there is a lesson to be learnt from

this, it is that three things are worth the investor’s attention: the trend of

economic momentum (improving/declining), above or below 50 (positive

growth/ negative growth), as well as extreme moves. The December 2000

signal confirmed the negative sentiment well.

The negative October 2001 signal marked the trough. The economy was

already officially in recession and everyone knew it, so the signal was not very

meaningful.

The positive September 2005 signal was one of continuation and nothing to

write home about.

The negative February 2008 signal, a fall from 50.3 to 47.6, a two standard

deviation move at the time, was valuable. The PMI was hovering between 49.0

and 51.1 since August 2007 and then broke in February 2008. China was

already in free-fall by then. The other two high-standard deviation signals in

2008 marked the continuation of the decay. They too were most likely

examples of a reflexive relationship, i.e., the PMI affecting the stock market as

well as the stock market moves affecting the responses of the survey

participants behind the PMI report.

2010s

Figure 17 shows PMI in this decade, the 2010s. There have not been any official

recessions; yet.



The strong May 2011 fall market the green shoots turning into brown shoots

as QE2 was about to end in June. IR&M’s US economic model, for what it’s

worth, which includes various PMIs turned in June. The S&P 500 fell from

around 1350 to around 1100 by August. In hindsight, the signal and changing

economic momentum was worth paying attention to.


1M 2M 3M 6M 12M

31/12/2000 -4.6 43.9 42.3 42.1 43.1 43.2 45.3

31/10/2001 -5.4 40.8 44.1 45.3 47.5 52.4 49.0

30/09/2005 4.4 56.8 57.2 56.7 55.1 54.3 52.2

29/02/2008 -2.7 47.6 48.3 48.8 48.8 49.2 35.5

30/09/2008 -4.4 44.8 38.9 36.5 33.1 36.0 54.4

31/10/2008 -5.9 38.9 36.5 33.1 34.9 39.5 56.0

PMI after x months


1M 2M 3M 6M 12M

31/05/2011 -5.2 53.7 56.6 52.9 53.0 52.1 53.2

31/01/2014 -5.2 51.3 53.2 53.7 54.9 57.1

PMI after x months



The January 2014 signal was a misfire, as the PMI bounced back in the

following months. But then, perhaps it wasn’t. Markets are being manipulated

in a sense that the authorities want inflation, resulting in asset price inflation,

i.e., equities as well as bonds doing reasonably well. Potentially, Herbert

Stein’s Law from page 10 applies.

Summary

Figure 18 shows the PMI will all the signals that are larger than two standard

deviations.

Figure 18: PMI with signals (January 1948 – August 2014)


Not all high standard deviation signals are meaningful and/or important, as

outlined in the previous, more detailed section.

There were 21 negative signals (red dots). 20 (95%) of those signals happened

when the PMI was falling. This is not true for only one, the last one, the sharp

fall from January 2014.

There were 19 positive signals (green dots). Only 12 (63%) occurred when the

PMI was in a rising trend.

This means a sharp fall in the PMI is probably more significant than a sharp rise.

However, we need to make a mental note that a high or low standard deviation

rise crossing 42.2 from below is worth paying attention to.

In the following section, a brief analysis of Germany’s ZEW economic indicator is

shown.



Germany’s ZEW

In our update from 14 August 2014 we showed the following slide:

Figure 19: Germany’s ZEW

Source: IR&M risk management update from 14 August 2014

The changes in mid-August were not only in the wrong direction, towards the

lower left hand corner, but they were also material, i.e., standard deviation was

high which warranted a red flag. Now Newton’s first law of motion applies: the

trend is negative until there is a ‘force’ that reverses the trend. The practical

relevance is that the wind has turned and stopped blowing from behind. One

ought to trim the sails accordingly.

Figure 20 shows the vertical axis from the previous graph in chart format, i.e., the

ZEW Germany Assessment of Current Situations Index since inception in 1992. The

moves that are larger than two standard deviations (based on three-year rolling

standard deviation) are highlighted; green positive, red negative. The shaded areas

show the periods where long-term momentum (as defined in the momentum

monitor publication) of the DAX was negative.



Figure 20: ZEW Germany Assessment of Current Situations with high standard deviation signals


There are 13 red dots, i.e., negative signals. 11 of those signals occurred when

long-term price momentum in the DAX was already negative.

The 2011 signal was a bit late. The DAX went from around 7500 in July to a

low close to 5000 in August. The 2.8 standard deviation fall of the economic

indicator was released on 16th August when the DAX already had fallen to

6000. Long-term momentum flipped to negative on Friday 12th August, i.e.,

earlier than the signal from the ZEW indicator. The 100-day moving average of

the IR&M Germany Model peaked in May of that year, i.e., gave, in this case,

ample warning.

Bottom line

Paying attention to high standard deviation changes makes sense but the Holy

Grail of finance it is not. Large changes should be treated as an indication of

meaningfulness rather than a solid signal one can blindly adhere to.

When assessing economic variables I believe the most important thing is the trend,

i.e., the economic momentum. The momentum itself is—to a large extent—a fact,

not someone’s opinion or forecast. The second most important thing to look for is

the reversal of the trend. Only then, third, should we look at the extent of the

magnitude, i.e., standard deviation of a particular move.



References Russell, Betrand (2004) “History of Western Philosophy,” Oxon: Routledge Classics. First published 1946; London:

George Allen & Unwin Ltd.

Taleb, Nassim Nicholas (2007) “The Black Swan – The Impact of the Highly Improbable,” New York: Random House.

Zweig, Martin (1986) “Winning on Wall Street,” New York: Warner Business Books.



Publications

Risk management research (subscription based)

We’re done 12 September 2014

No reform 29 August 2014

Time for caution 14 August 2014

Bad equilibrium 18 July 2014

Make no mistake 4 July 2014

Rekindled financial romance 18 June 2014

Economic World Cup 2014 (report) 6 June 2014

Illegibly incomprehensible 4 June 2014

Seriously concerned 16 May 2014

Inflationary complacent 1 May 2014

Awaiting further accommodation 15 April 2014

Sleeper pins (report) 11 April 2014

Underpricing geopolitical risk 28 March 2014

Small cushion 14 March 2014

Fragile improvement 28 February 2014

Recovery far from complete 14 February 2014

Boring middle years 31 January 2014

Boldly going where others have gone 17 January 2014

Trends remain in place far longer 6 January 2014

Everything mean reverts 20 December 2013

Positive momentum mode 4 December 2013

Blue skies and red flags 19 November 2013

Walking a tightrope (report) 6 November 2013

Tapering off the table 21 October 2013

Uncertainty matters 8 October 2013

No dope no hope 26 September 2013

Change spotting (report) 19 September 2013

More fragile than it looks 13 September 2013

Fragile growth 30 August 2013

In remembrance of caution 15 August 2013

Growth surprises, not taper timing 2 August 2013

Highly accommodative (report) 17 July 2013

Tapering-off talk tapering off 1 July 2013

Mispriced bubbles 18 June 2013

All they know 3 June 2013

Pretty shocking 17 May 2013

No understanding – no assurance 3 May 2013

IR&M momentum monitor (inaugural issue and tutorial) 3 May 2013

A new dimension (report) 19 April 2013

A bit of friction here and there 4 April 2013

As long as it takes 22 March 2013

Keep it coming 12 March 2013

The end of a system 1 March 2013

Great Unrecovery continues 22 February 2013

Showing mettle 15 February 2013

Currency wars 1 February 2013

Repressionomics (report) 18 January 2013

Far from over 3 January 2013

Wriston’s Law of Capital still at work 19 December 2012

A very long process 5 December 2012

…

No risk (report) 26 October 2012

Wriston’s Law of Capital (report) 10 July 2012

What makes bears blush (report) 11 April 2012

Europe doubling down (inaugural report) 3 October 2011

IR&M’s risk management research

consists of 25-30 risk management

updates, 20-30 flash updates, 45-50

momentum monitors and 4-8

thematic reports per year.

http://www.ineichen-rm.com/images/stories/pdf/change%20spotting%202013.pdf

http://www.ineichen-rm.com/images/stories/pdf/irm%20update%202013%20may%2017%20sample.pdf

http://www.ineichen-rm.com/images/stories/pdf/irm%20momentum%20monitor%20tutorial%20may%202013.pdf

http://www.ineichen-rm.com/images/stories/pdf/rmr%20q3%202012.pdf

http://www.ineichen-rm.com/images/stories/pdf/rmr%20q4%202011.pdf

http://www.ineichen-rm.com/images/stories/pdf/irm%20intro.pdf



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