Trading Agriculture Commodities

Transcription

Apr-Jun 2009
The publication for trading and investment professionals
www.technicalanalyst.co.uk
Trading Agriculture
Commodities
with FourWinds Capital Management
Awards
Techniques
Interview
Highlights of the Technical
Analyst magazine’s
2009 awards
Taking a look at using the
200-day moving average
Perry Kaufman discusses
automated trading strategies
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Editorial Comment
Agriculture commodities
Agriculture commodity funds remain a rarity among fund
managers and hedge funds which is perhaps surprising given
the bull market in commodities seen over the past few years.
This may be a situation that is slowly changing as awareness
of the agriculture markets grows and investors look elsewhere for returns as stock markets remain rangebound.
In this issue we talk with FourWinds Capital Management
about some of the challenges involved in trading and investing in these markets.
Matthew Clements
– Editor
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Apr-Jun 2009
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Contents
Apr-Jun 2009
The publication for trading and investment professionals
COVER STORY
Agriculture Commodities
12
We talk to Chady Achkar of FourWinds Capital
Management about his approach to managing
their Ceres agricultural fund.
Trading Agriculture
Commodities
08
with FourWinds Capital Management
Awards
Techniques
Interview
Highlights of the Technical
Analyst magazine’s
2009 awards
Taking a look at using the
200-day moving average
Perry Kaufman discusses
automated trading strategies
MARKET VIEWS
S&P500: looking for a bottoming pattern
04
Indian stocks: what Elliott wave says about the Sensex
08
The euro: more strength to come
SPECIAL FEATURE
Trading the agriculturals
With Chady Achkar of Fourwinds Capital Management
AWARDS
07
12
A roundup of the 2009 Technical Analyst awards ceremony
19
The calendar effects: what to expect for the rest of 2009
22
Momentum and mean reversion in volatile markets
33
TECHNIQUES
Indicator Focus: using the 200-day moving average
INTERVIEW
37
Research Update
42
Training Diary
48
Books
12
Chady Achkar of
FourWinds Capital Management
28
Perry Kaufman discusses developing systematic
trading systems
REGULARS
Outlook for the Sensex
22
The calendar effects
46
19
Apr-Jun 2009
The Technical Analyst
Awards 2009
3
Market Views
4
Apr-Jun 2009
Market Views
S&P500:
LOOKING FOR A
BOTTOMING PATTERN
Art Huprich of Raymond James compares the low of 2008 with the
sell-off in March to see if the market may now have bottomed.
In the midst of March’s top side move by the stock market and
following my comment about the need for a Low-Rally-Retest
(LRR) stock market bottoming sequence, I was asked if the
stock market could record a “V” bottom, especially since everyone is looking for a retest. My response is sure; the stock market doesn’t operate for my benefit. I simply try to interpret what
it is saying. I did add however that, “My experience in studying
how bear markets end is that a majority are completed by an
LRR”. This doesn’t mean that every LRR has worked in stemming a major decline as we experienced in 2008. What it means
is that when a major decline has run its course, the majority have
ended with such a sequence of events. I think the odds favour the
same thing happening this time.
With that said, I think we are still in rally mode because:
1. The 6 March intra-day low of 666.76 that was recorded by the SPX coincided with an almost perfect 62%
retracement (one of the actual Fibonacci retracement
figures is 618) of its 1982 to 2007 advance (Figure 1).
With this retracement figure, the SPX has in essence
met a downside. This has produced a critical support
level and in turn, a stop loss point. A close below 665
will render this analysis wrong.
Apr-Jun 2009
“IN LOOKING AT THE SPX, I THINK
RESISTANCE AT THE 804 BREAK
DOWN LEVEL FROM LATE JANUARY
AND THE 50-DMA CURRENTLY AT 806
HOLD THE ANSWER.”
2. The DJIA tagged and held the lower trend line of a
downtrend sloping trading channel (Figure 2).
3. The NASDAQ and the S&P Small Cap 600 Index
record a weekly reversal. What this means is that they
recorded a new 52-week low in early March but closed
higher that week. This is also known as a selling climax.
A selling climax is defined as a new 12-month low early
in the week with the week closing higher with a gain.
4. A number of Bullish Percent indicators, based on
point and figure analysis, reversed up. This signifies
that within the context of managing stock specific risk
via a stop loss strategy, the offensive team should
→
slowly come back on the field.
5
Market Views
Figure 1
Figure 2
Other indicators
While all of this was going on in early March and the major
stock market indices undercut their November and October
2008 lows by a significant margin, the selling pressure during
these under-cut lows was less pressure ridden than that
recorded last autumn. Specifically, the number of new 52week lows on the NYSE at the October and November lows
were 2901 and 1894 respectively. The number of new 52week lows on the NYSE in early March was 827.
NYSE volume at the October and November lows was
2.85 billion and 2.20 billion shares respectively. NYSE volume on March 6 was 1.76 billion shares. The Oversold –
Overbought Oscillator readings were minus 20.4 and minus
14.5 in October and November respectively. The March reading stood at minus 13.3.
However, in terms of sentiment, the Investor’s Intelligence
Bull-Bear investment advisory newsletter showed 22.4%
Bulls and 54.4% Bears last autumn. The most recent readings
were 26.4% Bulls and 47.2% Bears. While these aren’t as
extreme as last fall, they have been moving in that direction.
Trading strategies
With all this being said, from a tactical perspective and within the context of further upside probing, I still recommend
that when reviewing your stock holdings:
6
Apr-Jun 2009
1. If the fundamental story is broken for a specific stock
and you are holding the stock, sell it.
2. If the technical attributes are not supportive for a specific stock(s) you need to decide if you want to sell,
reduce, or hedge your position.
Is this the start of another bear market rally?
In looking at the SPX, I think resistance at the 804 break
down level from late January and the 50-DMA currently at
806 hold the answer. A close above these resistance points for more than one day - would signal a minor pull back
and/or sideways action. A failure to get above that level or
once above, an immediate pull back below, would signify a
more pronounced or full retest.
Since I am thinking that any type of bear market rally
should stop at or before the January price peak, a close by the
SPX above the January high (934.70) would imply that the
current rally is more than just a bear market one but something that has a lot more life to it. This would especially be
the case if, at the same time, the Dow Transportation Index
closed above its January 2009 price peak, which at this point
would be a very tall order.
Art Huprich is technical analyst at Raymond James in the US.
Market Views
THE EURO:
MORE STRENGTH TO COME
By Valerie Gastaldy
Starting in the 1970s, the euro shows a
regular uptrend versus the USD. After
reaching a new high at 1.604 (based on
a synthetic calculation), it reversed. So
far that fall is a consolidation in the secular uptrend. No reversal pattern
appears on long term charts, just a fall
that is far from breaking the long term
support at 0.82 Figure 1). From here the
pair could move up to join the top of
the channel, or move down to join the
bottom.
before it resumes new highs.
Figure 5 shows the real effective
exchange rates of the US (black) and
the euro (blue) since 1964.
Figure 2. EUR/USD
To resume the uptrend in the coming
weeks, the pair would have to maintain
itself above the 50% retracement of
the latest rally at 1.307. The confirmation would come from the passing of
1.381/ 1.388 - the 62% retracement of
the fall since the summer and the 200day moving average (Figure 3). Between
those two levels, it is once again a consolidation of the recent volatile period.
Figure 1. Souce: CQG
Focusing on price action this decade,
a trendline has been broken and so has
the 2-year moving average, but the fall
is contained so far by the 38%
Fibonacci retracement ratio. The consolidation may be deeper to 1.17 (the
previous trough) and even to the euro
introduction rate. However, the trend
still shows higher peaks and troughs so
it could resume anytime. Here again,
the chart shows nothing but a consolidation pattern (Figure 2).
Figure 3. EUR/USD
Whatever the time horizon, the euro
is consolidating a bullish trend against
the dollar. It is only a matter of time
Apr-Jun 2009
Figure 5.
The USD hit its support area for the
third time in November and is now
bouncing back. Since 1964 the peaks
have been decreasing regularly (as
shown in red) and there is a high probability that this bounce will be the last
one before the support gives away. It is
indeed a classic descending triangle pattern. As such, the USD will show weakness again sometime in 2010.
The euro area currency reversed a
bearish trend in 2003 (blue dotted line)
and came in November ‘08 against its
all-time high dating back to 1980.
Whether the 1.23/1.24 threshold will
contain the consolidation is undecided,
but the 1.17 support is strong enough
to fuel a rally to the top of the long
term channel at 1.75.
Valerie Gastaldy is Associate General
Manager at DaybyDay technical research
in Paris.
7
Market Views
8
Apr-Jun 2009
Market Views
A ROAD MAP TO
SENSEX 100,000
Mark Galasiewski of Elliott Wave International offers a bullish
outlook for Indian stocks
Prices in India's SENSEX have just broken above a downtrend line, imitating a pattern from 2004 that led to a strong
rally. This article updates the wave count for India, since its
wave pattern in particular may offer investors a rewarding
long-term opportunity.
In the March 2009 issue of Elliott Wave International’s The
Asian-Pacific Financial Forecast, we showed how pattern, price,
time and sentiment considerations were pointing to the end
of multi-month, five-wave declines in most major AsianPacific indexes by late March. In most cases, those lows have
likely been achieved.
three waves (Figure 1). A three-wave decline opens the possibility of a rally back to near the 2008 highs. But there is reason to set our sights even higher.
Perhaps the best argument for a bull market in Indian
stocks is the potential fractal relationship we identified in the
November 2008 issue of our newsletter, published just four
days after the October low. The weekly chart in Figure 2 is an
updated version of the one we showed at that time. Here is
our analysis from the November issue:
"The Wave Principle teaches that the stock market is a selfsimilar fractal. That means that some pieces of its price
record — which Ralph Nelson Elliott called waves —
resemble other pieces elsewhere in that record. The weekly
chart of India’s SENSEX shows just such an example.
Notice how the up-down sequence labeled Intermediate
waves (1) and (2) (in the small red box) is a microcosm of
the larger up-down sequence from the 2003 low to the →
Figure 1.
Although we have looked for a fifth wave down to below
the October low in the SENSEX, it has failed to materialize.
That failure plus the recent sharp reversal rally prompts our
return to an earlier wave count. The daily SENSEX chart
shows how the decline since the 2008 high is best counted as
Apr-Jun 2009
Figure 2.
9
Market Views
as then, prices have broken down from an apparent triangle,
and then reversed and broken out above the downtrend line
(see Figure 4). In 2004, prices never looked back after the
breakout. As long as prices do not fall back below the low of
today’s breakout bar, we will assume that the 2003-2008 bull
market will continue to provide a road map to the future of
India’s stock market.
Figure 3.
present (i.e., primary waves 1 and 2 (circled), in the large
black box). In both cases, the wave-two correction retraced
approximately 50% of the wave-one advance. (We have calculated those retracements using the same logarithmic scale
shown in the chart: logarithmic charting displays equal percentage moves proportionally).
"If we have identified this “nested fractal” relationship
correctly, it means that Indian stocks are about to begin
Primary wave 3 of the bull market that began in 2003....
Primary waves 1 and 2 lasted more than four times the
duration of waves (1) and (2). If that same proportion
holds going forward, the SENSEX may continue advancing for 15 years before reaching the end of Primary wave
5."
Since then, the analogy to the 2004 period (“The 2004
Analog” in Figure 3) has become even more interesting. Just
10
Apr-Jun 2009
Figure 4.
Mark Galasiewski is editor of Elliott Wave International’s
Asian-Pacific Financial Forecast.
This article was originally published as a special interim report of
Elliott Wave International’s Asian-Pacific Financial Forecast on
March 23, 2009. For further information please visit
www.elliottwave.com.
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Special Feature
12
Apr-Jun 2009
Special Feature
TRADING THE
AGRICULTURE
COMMODITIES
With FourWinds Capital Management
Chady Achkar is deputy head of funds group at FourWinds
Capital Management in London where he specialises in
manager research, portfolio optimisation modelling and risk
management. He has co-responsibility for the Ceres
Agriculture Fund, a closed-ended investment fund of
exchange traded agricultural commodity contracts and
derivatives. Before joining FourWinds, he worked in portfolio
and risk management at Deutsche Asset Management and
Dexia Asset Management.
He talks to the Technical Analyst about some of the challenges of trading and investing in agriculture commodities in
today’s market conditions.
Apr-Jun 2009
13
Special Feature
TA: How old is the Ceres fund and what was your motivation for setting up the fund?
CA: Ceres was launched in November 2007. The objective of
the fund was to give investors an access to actively traded
strategies in the agriculture commodities space.
TA: What is the size of the fund?
CA: USD 140M (Jan 2009)
TA: Who are the main investors in the fund?
CA: 80% of our investors are institutions and pensions
funds.
TA: How many running traders/investment managers
do you have on the fund?
CA: We currently have 17 traders in the fund.
TA: How has it performed since its inception?
CA: The fund was up 2% in 2007 (only one month of trading) and was positive in 2008 (+1.98%).
corn volatility and buying lower volatility in the soy complex.
TA: Do you ever trade the physical commodity and if so,
why?
CA: No. There is so many alpha to be extracted by trading
futures and options. However, understanding the relationship
between physical and futures is key.
TA: What performance benchmark do you use?
CA: We typically use the Dow Jones AIG Agriculture TR
index
TA: Why has your fund’s performance declined since
mid 2008?
CA: Our fund ended up 2008 in positive territory. However,
our fund's performance declined since June 2008. The main
reason has to do with the fact that fundamentals were no
longer driving commodities prices. Most of our traders are
relying on fundamentals as supply and demand factors,
weather pattern, seasonality etc to implement a strategy.
TA: What trading strategies do you employ?
CA: Our traders use a wide range of non-directional strategies like calendar spreads, geographical spreads, volatility
arbitrage, inter-commodity, and systematic (trend-following).
TA: Over what time scale do you generally trade/invest?
CA: About 50% of our trades are medium term (1 to 3
months), 30% are short term trades (less than a month), and
the remaining 20% are long term trades (over 3 months).
TA: Which markets (specific commodities) do you most
heavily/frequently trade?
CA: We tend to replicate the open interest in the agriculture
markets. To that extent, our fund will be exposed on average
to 55% grains (corn, soybean, wheat, rapeseed), 25% softs
(cocoa, sugar, coffee, orange juice), 15% Livestock (live cattle, lean hogs, feeder cattle) and 5% fibers (cotton).
TA: Do you generally trade futures? If you trade options,
what do you use them for?
CA: Generally speaking, we trade about 70% futures and 30%
options. Options can be used for many different purposes.
These include the hedging of a futures directional position, limiting the risk of a long or short conviction, (i.e. buy a put or a call
instead of being long or short a future). Options can also be
used for volatility arbitrage by, for example, selling relatively high
14
Apr-Jun 2009
TA: Why did you decide to set up an agricultural fund
rather than one based on other commodities such as
metals or energy?
CA: If you think about it, the correlation between heating oil,
crude oil, natural gas or other energy commodities is pretty
high. Their historical correlation has been close to 0.8.
Similarly, the correlation between base metals (copper, aluminium, zinc etc) has been near 0.7.
Now if you look at the correlation between lean hogs and
sugar or cocoa and cotton, it is pretty low, if not sometimes
negative. This creates a natural diversification in a pure agri→
culture commodities portfolio.
Special Feature
Chady Achkar
Apr-Jun 2009
15
Special Feature
TA: What are the main difficulties in the short term trading of agriculturals?
CA: With higher volatility in agriculture commodities, short
term trading now offers increased returns. This aspect is
inherently challenging because it makes the assessment of
risk/reward objectives and the amount of risk budget utilized
more critical than ever. Psychologically, short term trading
requires immense discipline due to the limited amount of any
given time frame you are allowing yourself. Therefore timing
is key in order to have success in short trading frames. This is
even more apparent in agricultures due to liquidity issues in
deferred contracts.
For example, a short term trade in the 6th option of
Chicago wheat may look good on paper but dried up liquidity, as a result of a pending crop report, could cause wide
bid/ offer spreads making it impossible to implement or exit
the strategy. In summary, the difficulties that can exist in
short term trading agriculture commodities are created by
timing, lack of discipline, and varying degrees of liquidity.
“THE CORRELATION BETWEEN
LEAN HOGS AND SUGAR OR
COCOA AND COTTON IS PRETTY
LOW, IF NOT SOMETIMES NEGATIVE. THIS CREATES A NATURAL
DIVERSIFICATION IN A PURE
AGRICULTURE COMMODITIES
PORTFOLIO”.
TA: What are the main differences between the individual markets from a trading/return perspective?
CA: One main difference across agriculture markets is identifying what strategies are best suited for them. For example,
we have found relative value strategies in livestock that focus
more on pricing anomalies across the curve and less on
absolute direction work extremely well. While in the grains,
more of a mix in options volatility and flat price strategies
can offer better returns. This didn’t always use to be the case
but over the course of 2008 and early 2009, we have seen
minimal changes in inter-commodity grain spreads compared
to that of highly volatile flat price movements.
TA: Which markets have performed best and worst over
the past few years?
CA: In 2008 the Dow Jones Agriculture Index was down
27.48%. Alternatively, cocoa and sugar gained on the year.
16
Apr-Jun 2009
Specifically ICE cocoa increased 31% due to bullish supply driven fundamentals, while ICE No.11 Sugar gained just over 9%.
However, over the past 3 years, only the OJ market reached
an all-time high.
TA: How much attention do you pay to market
cycles/seasonality? Do you they still present trading
opportunities? If so, why haven’t these opportunities
been traded away?
CA: Yes, cycles and seasonality do offer up great trading
opportunities. However, finding success in doing so cannot
be found in a “how to guide” for commodities trading. We
focus on the ever changing influence of market participants,
such as commercials and index funds, and how they impact
seasonality and contribute to short/ long term cycles. For
example, Index funds are known to “roll” long positions on
the 5th to 9th business day of the month; however over time
the manipulation of this practice by other participants caused
Index funds to “roll” long positions earlier and later than
expected. The point is to identify which agriculture commodities are being skewed by such activity, which has pushed
prices out of line, and then placing complimentary strategies
when the market corrects itself.
From a seasonal point of view, the underlying physical
commodities have productions cycles, weather events, and
seasonal demand tendencies to contend with. Those factors
in normal environments have created historical price activity
that has produced consistent patterns over the years. For
example, corn and soybean volatility seasonally strengthen
during the U.S. spring planting season. Also, increased
demand for pork coupled with less production has historically made for higher cash prices and subsequent higher lean
hog futures during the summer months. Conversely, larger
market ready supplies and lower cash prices seasonally push
pork prices lower during the fall/winter months. These
effects no not get traded away because the size of these markets remains quite small, and so money flows have much less
affect than in other markets.
TA: To what extent have the markets been driven in
recent years by underlying fundamentals and to what
extent by speculative trading?
CA: Over the past 18 months, and especially during the past
6 months, commodity volatility and directional price movements have been highly correlated with the unprecedented
volatility in equity and currency markets. All of the softs have
declined in price since last July due to the unfolding recession,
with cotton dropping the most [from 90 cents to 40 cents per
pound]. Cotton is clearly the most economically sensitive of
the softs, although sugar’s correlation to the energy markets,
due to its role as the feedstock for Brazilian ethanol, has also
created an increased sensitivity to economic conditions.
Having said that, trading fibres such as cotton using →
Special Feature
fundamental data and market expectations has been extremely challenging considering the depths of the economic slowdown and worsening demand numbers. Trading cotton supply and demand has been a moving target so much so that
even the most talented traders have had difficulty timing
profitable strategies.
TA: Are there any sentiment indicators that you use?
CA: Yes. First it is important to filter which indicators best
compliment your strategy and also which ones provide nonbiased signals that help the risk management purposes.
Recently, monitoring the price activity of the Dow, US dollar
and crude oil futures in a macro-sense has been a helpful
overlay to trading agriculture commodities, both in terms of
risk management and trade implementation. Fundamentally,
the quarterly and monthly USDA (United States Department
of Agriculture) reports for commodities such as corn, cotton,
and live cattle give critical indication to factors such as ending
stocks, seeded acres, and CoF (cattle on feed).
vastly outperform energy commodities. This is because the
energy bucket is highly correlated; if crude returns 20%, we
feel natural gas and heating oil, etc will closely follow. The
agriculture bucket consists of commodities ranging from
cocoa to feeder cattle, all of which have starkly different fundamentals impacting production and demand.
For industrial metals, the current recessionary environment
will likely stunt its ability to outperform relative to agriculture. Precious metals (i.e. gold and silver) do have potential to
outperform as another economic downturn could produce a
second round of “flight to safety” by investors. Also, recent
action by the U.S. Federal Reserve to buy long term debt is
viewed as being provocative to long term inflation. We have
seen short term run ups in precious metals prices caused by
this inflationary fervour in the recent past and look for it in
the future.
TA: What specific agriculturals (e.g. sugar, coffee etc)
offer the best opportunities in the short and long term?
CA: If we had to highlight specific agriculture commodities
to outperform we would focus on livestock, tropicals and
fibres markets. Some agriculture sectors that have the potential to underperform are grains, due to demand destruction
led by the faltering ethanol industry and decreased livestock
feed utilization.
TA: Is volatility generally good for you?
For more consumer sensitive commodities such as live cattle and cotton, we track economic indicators such as the
monthly jobs number as well as the CCI (consumer confidence index). On a more inconsistent basis, global policy
news regarding import tariffs and alternative energy can have
strong longer term implications on the supply/ demand levels of some agriculture commodities.
TA: How do you expect agriculturals to perform relative to
other commodities such as metals and energy this year?
CA: We feel the agriculture market as a whole has the potential to underperform energy prices this year; however we
strongly feel there will be select agriculture commodities that
Apr-Jun 2009
CA: Yes. Volatility is our friend. Volatility always offers more
opportunities for multi-strategy and relative value driven
traders. High volatility and wide trading ranges frequently distort prices on a relative value basis and on a perceived fundamental basis which creates unique opportunities. It is also
known and we acknowledge that volatility is sometimes generated by adverse market conditions; as such, it’s important to
realize the difference between an event that is normally anticipated (seasonal), and one that is rare and unexpected. This
recognition is critical in making risk management decisions
surrounding volatility.
TA: How do you adjust your risk management strategy
in very volatility markets?
CA: In general, given the higher volatility across agriculture
commodities, it is prudent that traders put less money to
work than they have in the past as they can still achieve similar returns.
TA: Do you generally look for rising prices in your markets as the basis for your trading strategies?
CA: No, because the main driver of our performance comes
from the structural shifts from contango to backwardation on
the term structure, the volatility of these movements and the
price relationship between sport and deferred contracts. →
17
Special Feature
is important because your fundamental conviction about the
commodity can be strong but if the price action cannot sustain itself, you will have to be patient in expressing that conviction. Obviously, there are a variety of technicals that can
be used in assessing the agriculture markets and most importantly, they offer a non-biased overlay to discretionary decision making.
TA: What technical analysis do you use/find most effective in trading agriculturals?
CA: Technicals such as Fibonacci retracements, RSI, and a
variety of moving averages help in deciding what price levels
make sense. Studying open interest and volume as well as utilizing different chart views (daily, weekly, and monthly) help
put medium to long term strategies into perspective.
TA: To what extent are the agricultural markets correlated?
Do any correlations present trading opportunities to you?
TA: To what extent does technical analysis form part of
your trading strategy?
CA: For many fundamental discretionary traders technical
indicators do not necessarily generate trade ideas, but rather
gives more of a confirmation to the process. An example
would be having an underlying bullish directional bias in a
market based on supply concerns, and at the same time recognizing that the RSI (relative strength index) is coming out
of oversold territory; just above this price level you notice
that if price strength can sustain itself, it will be able to rise
above both the 20 and 60 day moving averages.
This confluence of signals helps confirm a potential entry
point for the bullish strategy you would like to entertain. It
also helps in adding discipline as it forces you to adhere to the
price action relative to the technicals you are monitoring. This
“THERE ARE A VARIETY OF
TECHNICALS THAT CAN BE
USED IN ASSESSING THE
AGRICULTURE MARKETS AND
MOST IMPORTANTLY, THEY
OFFER A NON-BIASED OVERLAY TO DISCRETIONARY
DECISION MAKING.”
18
Apr-Jun 2009
CA: Over the years increased investor demand in long only
passive agriculture investments has increased correlation during periods across the calendar year, having short term
impacts on prices. These especially occur during the beginning of the year/beginning of quarter and end of month
time frames. The recent correlation beginning in Q3 2008
was first driven by investor liquidation and then followed by
fresh waves of selling due to the grim economic picture. At
times corn has traded at a 0.90 correlation to crude and sugar,
while cotton and live cattle have closely followed the Dow.
Trading opportunities do exist as these occurrences are created by less fundamental and more money-flow driven reasons. Given the lack of fundamental reasoning behind such
occurrences, the ability to accurately time these phenomena
can provide for exorbitantly high returns when the individual
markets “snap back” and de-correlate. It should be noted
that these chances naturally come with extreme risk due to
nature in which they were developed.
TA: Are there any arbitrage opportunities in your markets?
CA: Yes. Most recently we have seen intra-volatility arbitrage
offer strong risk/reward opportunities. For example, selling
relatively high corn volatility and buying lower volatility in the
soy complex. However, it’s important to have underlying fundamentals confirm these statistical opportunities.
TA: Why are there so few hedge funds trading agricultural, in the UK at least?
CA: The main reasons for this are that trading agriculturals
requires very specific skills and fundamental knowledge. Also,
these markets are probably not deep or liquid enough to raise
sufficient capital in order to run a profitable hedge fund
business.
Awards
Celebrating Excellence in Technical Analysis and Automated Trading
The Technical Analyst magazine was proud to present its first annual awards this year culminating in the ceremony and dinner which took place on March 12th at the Sheraton Park Lane hotel in London. The evening proved to be a huge success
and we would like to congratulate the winners and thank all those who took part as nominees and judges.
Winners:
BEST EQUITY RESEARCH AND STRATEGY
Lowry Research Corporation
BEST SPECIALIST PRODUCT
Trading Technologies International
BEST FX RESEARCH AND STRATEGY
PIA-First
BEST AUTOMATED TRADING PRODUCT
Progress Apama
BEST FIXED INCOME RESEARCH AND STRATEGY
UBS
BEST TECHNICAL ANALYSIS PLATFORM
Updata
BEST SPECIALIST RESEARCH
JP Morgan Equity Quant Research
TECHNICAL ANALYST OF THE YEAR
Paul Desmond, Lowry Research Corporation
Apr-Jun 2009
→
19
Awards
Best Equity Research and Strategy
Paul Desmond of Lowry Research receives the award from Gerald
Ashley and category judge Robin Griffiths of Cazenove Capital
Best FX Research and Strategy
Max Knudsen of PIA-First receives the award with PIA-First
colleagues
Best Fixed Income Research and Strategy
Richard Adcock of UBS receives the award from Robin Griffiths
Best Specialist Research
Marco Dion of JPMorgan receives the award from category judge
Trevor Neil of Betagroup
20
Apr-Jun 2009
Awards
Best Automated Trading Product
Vito Imburgia and Kevin Palfreyman of Progress Apama
Best Technical Analysis Platform
David Linton of Updata with Trevor Neil
Technical Analyst of the Year
Paul Desmond of Lowry Research receives the award from guest speaker, Tom Dorsey
Apr-Jun 2009
21
Techniques
22
Apr-Jun 2009
Techniques
Apr-Jun 2009
23
Techniques
At the start of every year, the January Barometer and January effect
become talking points amongst analysts, fund managers and journalists.
This year has seen the US Presidential election cycle also discussed
which has often given clues as to the direction of stocks in each of its
four years. We look at how each of the common calendar effects performed last year and what they tell us so far about the outlook for the
year ahead.
Definitions
The January Barometer developed by Yale Hirsch in 1972 states that the performance of stocks in January indicates the
direction of the market for the year as a whole or over the next 11 months. If January ends higher the year will end higher, and if January is a down month then the year will end lower. Variations on this state that if the first five or last five
days of January end higher or lower then this alone is a guide to the direction of the market for the year as a whole.
The January effect simply says that stocks in January have a tendency to close higher on the month or on the opening week
because at year-end, the worst returning stocks are sold for tax loss purposes. These stocks are then re-purchased in
January which boosts the overall index. This effect is said to be particularly relevant to small cap stocks.
Post Presidential election years (such as 2001, 2005 and 2009) are traditionally supposed to be a negative year for stocks
as newly elected administrations introduce unpopular economic policies (tax hikes etc) at the beginning of their term
which are non-supportive for business and the stock market.
An examination of years ending in ‘0’ to ‘9’ (e.g. 2000 to 2009) going back to the 1920s has exhibited performance ten-
dencies both on the upside and downside to varying degrees. Although many of these may be due to chance, some years
within each decade have a strong tendency to out- and under perform.
The ‘Sell in May’ strategy simply states that stocks have a strong tendency to underperform during the months of May to
October (or outperform from November to April). The explanation for this may be due to the prolonged summer holidays when strategic stock market investing may decline.
The January Barometer
January: the month as a whole
Since 1950 the January Barometer has had an excellent record in anticipating the year ahead in US stocks. However, January’s
performance has a much better record (92%) at forecasting ‘up’ years (92%) than ‘down’ years (59%), (See Table 1.).
S&P500 (1950-2008)
Table 1
Up Januarys
37/59
63%
Down Januarys
22/59
37%
‘Up’ years forecasted
34/37
92%
‘Down’ years forecasted
13/22
59%
According to Yale Hirsch of The Stock Trader’s Almanac, “Every down January since 1950 in the S&P500 has been followed
by a new or continuing bear market, or a flat year. With the exception of 1956, down January’s were followed by substantial
declines averaging -13.3%”. Jay Keppel, author of ‘Seasonal Stock Market Trends’ (Wiley 2009), only includes the months of
February to December in January Barometer calculations. This produced a return over twice as high in dollar terms than a
buy and hold strategy on average over the last 70 years.
Table 2 shows that the January Barometer performed very well in 2008 for both the S&P500 and the FTSE100. This year
has again seen both indices end lower in January; therefore the Barometer suggests that both US and UK indices will end the
year lower (Table 3).
24
Apr-Jun 2009
Techniques
2008 summary
Open
Close – first five days
Close - January
Close on year
Table 2
January 2009 summary
Open
Close – first five days
Close - January
Close on year
Table 3
S&P500
1467.97
1390.19
1378.55
903.25
FTSE100
6456.90
6356.50
5,879.80
4,434.20
S&P500
931.80
909.73
825.88
?
FTSE100
4434.20
4505.40
4149.60
?
The validity of the January Barometer has been disputed by Ben Marshall of Massey University in New Zealand. In a study
earlier this year he found that the January Barometer was no more profitable as a trading strategy in predicting market direction in the remaining 11 months than any other month. He concludes that the January Barometer underperforms a buy-andhold strategy because it suffers from being out of the market in January and may be subject to data mining bias.
January: the first five days
An alternative version of the January Barometer says that rather than look at the whole of January, the first five days trading has historically been a good guide to how the rest of the year will pan out. According to the Stock Traders Almanac, the last 36 up first-fivedays (not including 2009) were followed by full year gains on 31 occasions or 86% of those years. But the rule works much better for
“up” years than “down” years; on the 22 occasions that the first-five-years down, 11 were followed by up years and 11 by down years.
The January effect
The January effect is the general tendency for stocks to outperform in January and specifically, for small-cap stocks to do much
better than large-cap stocks during the month. Between 1953 and 1995, small-cap stocks out-performed in January in 40 of
the 43 years. The Nasdaq has also performed much better than the other US stock indices. Over the history of the index the
Nasdaq has seen a rise over the month of January 79.2% of the time compared with an average of 63% for the S&P500.
However, in recent years there has been something of a shift in effect with the small-cap advantage beginning earlier in
mid-December. According to Ernest Chan, a US-based trading consultant and author of, ‘Quantitative Trading’ (Wiley 2009),
most of the returns to be had from calendar effect strategies have begun to disappear, although the January effect did perform extremely well in 2008.
Kathryn Easterday, Pradyot Sen and Jens Stephan* looked at whether the January effect has diminished over three sub periods (1946-1962, 1963-1979, 1980-2007). They found that:
1. The January effect for small-caps exists for all three sub periods and that the effect declines as firm size increases.
2. Abnormal January returns are smaller after 1963-1979 but have merely reverted to pre-existing levels rather than disappeared. There is no evidence that the January effect has declined systematically over time, and it continued to be significant through to 2007.
3. The January effect is present for new (Nasdaq) as well as mature (NYSE, AMEX) markets.
4. Lack of unusual December-January trading volumes for small-cap stocks indicates that traders are not actively arbitraging the anomaly.
Years ending in ‘9’
Years ending in ‘9’ currently rank as the third best performing years within a decade with an average gain of 10.2% for the
S&P500 over the past 10 decades. Although end of decades tend to see a rally in stocks, years ending in ‘5’ have outperformed
by far ending higher 31% of the time. ‘9’ years have ended higher on 9 of the past 12 decades with an average return of 10.2%.
Jay Kaeppel says that the decade effect is more pronounced over a period running from late February of the previous ‘8’ year
until the end of September of the ‘9’ year. Of the past ten decades, only one period has seen negative returns over these
months (1968/69), with the average return being very high at 32%.
Apr-Jun 2009
25
Techniques
The decade effect
Year ending in
Average % return
Rank
9
10.2%
3rd
0
1
2
3
4
5
6
7
8
Table 4. Source: Jay Kaeppel
-6.7%
-1.9%
3.0%
9.0%
7.8%
31.4%
7.0%
-4.1%
19.7%
10th
8th
7th
4th
5th
1st
6th
9th
2nd
Presidential election cycle
2009 is a post election year in the Presidential election cycle and is often considered to be a poor one for stock market performance. In actual fact, the US stock market has seen a mixed performance in these years: of the 18 post election years since 1937,
eight have been ‘up’ years and 10 have been ‘down’ years. The last two post election years, 2001 and 2005, saw negative returns
although the Clinton post election years saw very high returns (See Table 5). There appears to be no correlation between stock
market performance in post election years and the election (or re-election) of a Democrat or Republican president.
Post election years
Table 5.
Year
1965 (LBJ)
1969 (Nixon)
1973 (Nixon)
1977 (Carter)
1981 (Reagan)
1985 (Reagan)
1989 (Bush)
1993 (Clinton)
1997 (Clinton)
% return
10.9
-15.2
-16.6
-17.3
-9.2
27.7
27.0
13.7
22.6
2001 (Bush)
2005 (Bush)
-7.1
-0.6
Sell in May
Finally, as we are about to publish in April, now is a good time to look at the ‘Sell in May’ strategy. In 2008 ‘Sell in May’ performed particularly well. Between May and October last year the S&P500 fell 28%. However, November 2008 opened at 9336
and with the S&P500 currently at around 8000, it is highly unlikely (unless we see a strong rally in April) that buying the index
at the beginning of November 2008 and selling in May 2009 will prove to be profitable.
A study by Plexus Asset Management in South Africa of the S&P 500 Index shows that annual returns of the November
to April six-month periods between 1950 and 2007 were 8.5% whereas those of the May to October periods were 3.2%.
Barclays Capital recently found that going back to 1964, the average return from buying the London index at the beginning
of October and selling at the end of May is just over 7%, but that the average return from buying at the end of May and selling at the beginning of October is minus 2%. A recent paper by Ben Jacobsen of Massey University and Sven Bouman of
Aegon Asset Management found that stock market returns from May to October were lower in 36 of 37 developed and
emerging markets they looked at. The effect was found to be especially strong in European markets while in the UK, the effect
goes back as far as 1694.
Alternative versions of the ‘Sell in May’ theory state that returns are not negative between May and October; returns are
just significantly less than the November to April period. This equates to an asset allocation decision as it means it is optimal
to stay out of stocks from May to October and invest elsewhere. Table 6 shows the absolute performance (in points) of the
26
Apr-Jun 2009
Techniques
‘Sell in May’ – FTSE100 since 1985
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
Table 6.
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
May to October change
76
-28
-319
45
39
-67
58
-1
359
-3
309
173
403
-572
-278
65
-889
-1086
406
77
456
47
303
-1710
Year change
189
259
31
46
640
-291
365
354
557
-343
624
431
1078
689
1051
-440
-957
-1278
467
304
834
539
146
-2097
FSTE100 since 1985. Over the 24 years, May to October was down on 10 occasions. This compares with five down years as
a whole. Moreover, for the remaining years that ended higher the May to October period only made a significant contribution to the absolute points gain on six occasions. However, over the period, the ‘Sell in May’ effect can be said to have unambiguously failed on eight occasions which suggests that the effect has begun to diminish in recent years. Furthermore, when
‘Sell in May’ has worked, the strategy is often subject to considerable downside volatility.
Stock market reaction for 2009
Calendar Effect
S&P500
January Barometer – month close
January Barometer –
first-five-days
Presidential Election Cycle
FTSE100
N/A
Year ending in ‘9’
*“The Persistence of the Small Firm/January Effect: Is it Consistent with Investors' Learning and Arbitrage Efforts?” By Kathryn E. Easterday
(Miami University of Ohio, Department of Accountancy), Pradyot K. Sen (University of Cincinnati - Department of Accounting), and Jens
Stephan (University of Cincinnati - Department of Accounting).
Apr-Jun 2009
27
Techniques
28
Apr-Jun 2009
Techniques
Indicator Focus:
The
200-day
Moving
average
We assess the effectiveness of the 200-day moving average and possible alternatives to its periodicity.
The longer-term moving average, usually the 200-day, is perhaps the technica’ indicator most often quoted in the financial
press. It is usually referred to as being a key support or resistance level, or as an indicator of market bearishness or bullishness. For example, a break below the 200-day indicates the former and a break above, the latter. Needless to say the 200-day
MA is not suitable for a short term trading strategy although a
break of price through the moving average line presents a trading opportunity over the space of just a few days.
The 200-day moving average was originally quoted in
“Profits in the Stock Market” by H.M Gartley, published in
1935. Gartley stressed the effectiveness of using this moving
average length to generate shorting signals in the 1919-21
bear market and buy signals in the subsequent market rally
and sell signals before the 1929 crash.
Based on closing prices, the simple or exponential 200-day
MA is a confirming not a leading indicator. As such, it is only
effective in trending not range bound markets and so may
lead to the missing out on much of the uptrend. However, it
has also been claimed that a reliance on a moving average
during a trending market may mean that you can get whipsawed in and out of the market.
Apr-Jun 2009
Common strategies using moving averages
Buy when the price breaks above the MA line; sell when
the price breaks below the MA line. In both cases it is
preferable if the MA line is also moving in the same
direction as price. Jeremy Siegel, author of “Stocks for the
Long Run”, suggests taking a position when prices move
1% above or below the 200-day MA.
The moving average crossover: buy when the shorter (e.g.
50-day) MA breaks above the longer-term MA; sell when
the shorter term MA breaks below the longer-term MA.
This is the double crossover method. The triple crossover
involves using three moving averages; signals are generated when the shortest MA crosses over the middle MA.
The signal is confirmed when the middle MA crosses the
longer MA.
As part of the MACD indicator. The MACD indicator
consists of two lines: The MACD line (the difference
between two exponential moving averages, usually the 12
and 26 periods) and the second (signal) line which is simply a 9 period exponential moving average of the →
29
Techniques
MACD line. The MACD typically works best at confirming a trend is underway by the crossing of the two lines.
The longer term moving average can act as support and
resistance levels for the market; this effect is probably
more pronounced in some markets (for example, oil) than
others. This also provides a strategy for placing stop losses at optimal levels.
Spread over price: this has been discussed much more in
recent years (see below). A large deviation of price from
the 200-day moving average indicates the degree to which
the market may be overbought or oversold especially if it
is at historically higher or low levels.
Research by Seung-Chan Park of Millersville University in
the US has found that a high price deviation of the 50-day
MA from the 200-day MA has a good track record in
identifying outperforming stocks or stocks with strong
momentum. He found that this moving average ratio,
combined with the nearness of the price to the 52-week
high, can explain most of the intermediate-term momentum profits.
Track record of the 200-day MA
A price crossover trading strategy based on using the 200-day
MA did not perform well during the 1990s. This is perhaps
because although the US stock market was strongly trending
during the decade, some moving average strategies would
have dictated staying out of the market for some of the time,
for example if prices dipped below the 200-day MA line. As
such, any time spent out of a strongly trending market would
have diminished returns relative to a buy-and-hold strategy.
Another view is that following the 200-day MA has become
self fulfilling with any profits from following the strategy
being traded away.
However, during the current decade, moving average strategies have performed well again. A study conducted by Ned
Davis Research in the US found that from late 1979 and until
2007, buying and holding the S&P 500 index would have produced a 10.2% annualized return. In contrast, a strategy that
switched between the S&P 500 and commercial paper according to whether the S&P 500 was above or below its 200-day
moving average produced an 11% annualized return. Not
only did it out perform a buy and hold strategy, it did it with
significantly less risk. Ned Davis Research found that the 200day moving average system has performed better this decade
than it did on average over the previous two decades.
During 2007, there were several false breaks below the 200day MA which suggests that using a Jeremy Siegel style breakout criteria to filter out whipsaws would be an effective strategy. In May 2008 prices broke below the 200-day MA (exponential) and have stayed below this line ever since; selling
short at this time would have gained around 750 points to the
downside.
30
Apr-Jun 2009
Deviations of the index from the 200-day
moving average
Multiple market pundits have mentioned that in March the
S&P 500 traded the furthest below its 200-day moving average since the Great Depression. Below we have plotted the
200-day spread indicator going back to 1927. The index is
currently trading 32% below its 200-day moving average,
which is indeed the most negative spread since 1937. Mean
reversion implies that when prices have deviated a long way
from the moving average, they have a tendency to
rebound.
While the spread can remain negative for quite some time,
the reaction to the upside has been extreme once the market
turns. In the 1930s, and even following the big declines in the
70s, 80s, and early 2000s, the spread turned violently positive in
the months following the ultimate low in the 200-day spread.
Unfortunately, nobody knows when that low will be and current market conditions suggest that the spread could widen
much further before it returns back into positive territory.
What the experts sayD
Despite the popularity of the 200-day MA, there remains a
large degree of subjectivity involved in choosing a periodicity. Consequently, there are several ‘alternative’ versions of the
200-day MA that are widely used. Moreover, the longer term
moving average can also be used in different ways according
to the asset class being traded and prevailing markets conditions. In this section, prominent technical analysts and investment managers give their views on how they use the long
term moving average.
S&P 500 200-Day moving Average Spread (%): 1927-Present
John Murphy is the author of, “Technical Analysis of the
Financial Markets” (New York Institute of Finance)
Brian Marber is the author of, “Marber on Markets” (Harriman
House)
Robert Colby is the author of, “The Encyclopaedia of Technical
Market Indicators” (McGraw Hill)
Techniques
WHAT THE EXPERTS SAY
Perry Kaufman
John Murphy
ARIAD Asset Management
“I don’t use a 200-day moving average specifically; I never select a single calculation period but
use multiple periods spanning a range that is relevant to the strategy. That way you are not subject to the whim of one indicator. Both the 200and the 250-day moving average are interesting
choices for a trader who only wants one indicator. The 200 is popular and tracked by many analysts, but the 250, or better still, the 252, represents one year. I would opt for the 252-day
because it would remove any seasonality and is
more generic. The 200 depends on who is
watching it - I think the 252 targets the economy
better.”
StockCharts
“The 40-week (200-day) can be used to track the
primary trend on weekly charts for futures and
stocks. The use of one moving average alone has
several disadvantages so it is usually more
advantageous to employ two moving averages.
The more statistically correct way to plot a moving average is to centre it by placing it in the middle of the time period. For example a 10-day MA
would be placed 5 days back.”
Edward Loef
Senior Technical Analyst,
Theodoor Gilissen Bankiers
“I don’t use the 200-day moving average, but I
use a simple 20-day and 120-day (week/month)
simple moving average (SMA). Every month,
week and day I check the closing price to get a
clue where we are in the long, medium and short
term cycles. I use the 120-day simple moving
average to decide what to buy and sell and the
20-day SMA to decide when to buy and sell.“
Robert Colby
Author of The Encyclopedia of
Technical Market Indicators
“It is probably no coincidence that the traditional
moving average lengths are based on the number
ten: 10 months (200 trading days), 10 weeks
(about 50 trading days) and 10 days. These specific lengths are well established in technical
analysis literature so criticisms of hindsight curve
fitting do not apply. The use of three moving
averages works well with the approach of multiple time frame analysis for determining the trends
of three degrees (Dow Theory). The Primary trend
is captured by the 200-day, the Intermediate by
the 50-day and the Short term by the 10-day.”
Peter Mauthe
Brian Marber
Former head of technical analysis
at NM Rothschild
“252 days represents a dealing year in the City of
London – 200 days is too volatile and lags logic.
Some use the 237-day because it’s a Fibonacci
number. In Tokyo, it would be 240 days. For the
FX markets that never close, I use the 261 day.
With this long-term MA I use the 21- and 63-day
moving averages for crossovers. All the moving
averages I use are arithmetic not exponential.
A rising moving average is potential support. A
falling moving average is potential resistance.
Potential support becomes actual support when
a close above a rising average follows one or
more closes beneath it. Likewise, it doesn’t matter how many closes there are above a falling
average as long as the average is still falling on
the day price closes beneath it again.”
Apr-Jun 2009
President, Rhoads Lucca Capital Management
“Moving averages, like most technical indicators,
find their validity in the numbers of investors who
follow them. The 200-day MA has found a large
following over the years so it is something of a
self fulfilling prophecy that it tends to work. I do
not use the 200-day moving average in our asset
management practice because it is too long term
for our portfolio strategies. We tend to use shorter term moving averages such as the 20- and 50day MAs for fine tuning our portfolios and our
hedges. I also have found useful a much longer
term moving average; 20-months or about 417trading days in identifying bull and bear markets.
As for using moving averages, I give most validity
to a close above or below the moving average.”
31
Momentum and
Mean Reversion in
Volatile Markets
Techniques
Volatility and Mean Reversion of Daily Returns
Market mean reversion can be quantified using autocorrelations (correlation of returns from today and tomorrow, today
and the day after tomorrow, etc). The correlation of consecutive market returns is called the one-day lagged correlation,
and similarly one can look at the correlation of the two-day
lagged returns, etc. A negative one-day lag correlation would
mean that the market tends to reverse direction every day, and
a negative two-day lag correlation would suggest that large
market moves tend to revert after two days, and so on (similarly, a one-day lag positive correlation would mean that large
market returns tend to extend into the following day, etc).
Any momentum or reversion trends are expected to wash out
over longer periods of time as the market does not retain a
long memory of past return patterns. For this reason, one
would expect that if there are any price momentum or reversal trends, they would appear in the correlation of consecutive returns (one-day lag returns).
This is indeed the case as can be observed for the S&P 500
in Figure 1. The figure shows the average (absolute) level of
correlations of daily market returns lagged by 1, 2…10 days
over the past 80 years. It appears that price trends of
momentum or reversion are relevant only over one-day time
periods (consecutive returns). For this reason, we focus only
on one-day lag correlations and look at strategies that identify and profit from one-day patterns.
Apr-Jun 2009
Autocorrelation (Absolute)
In this article we show that in the current high volatility environment behavioural and structural effects can lead to persistent patterns of momentum and reversion, and that these patterns can be captured in a systematic trading strategy.
12%
10%
8%
6%
4%
2%
0%
0
1
2
3
4 5 6 7
Time Lag (Days)
8
9
10
Source: J.P.Morgan Derivatives and Delta One Strategy, Bloomberg
Figure 1. Lagged S&P 500 Autocorrelation (Absolute Value) –
Average from 1928 to 2009
Figure 2 shows the one-day lag correlations over the past
four years. One can see that over the past two years, these 1day lag correlations were persistently negative. This means
that day to day, the market was more likely to revert than
trend. While in 2006 autocorrelation was equal to zero (consistent with efficient markets), 2007 and 2008 had significant
negative autocorrelations in a range of -10% to -20%.
Carefully looking at Figure 2 raises the following ques- →
33
Techniques
34
Apr-Jun 2009
Techniques
10%
0%
0%
-10%
-20%
-20%
-40%
-30%
Lag One
-60%
Oct 05
Jul 06
-40%
Apr 07 Jan 08 Oct 08
Source: J.P.Morgan Derivatives and Delta One Strategy
Figure 2. One-Day Lag correlations and Volatility for the S&P
500 over the past four years
tion: Is the simultaneous drop in autocorrelation and increase
in volatility a coincidence or is there a relationship between
the two quantities? Figure 3 illustrates the volatility and oneday lag correlation over the past 30 years. It is evident that the
volatility and lag-1 correlation are inversely related. Also one
can notice a gradual drop in absolute levels of autocorrelation over the past 10 years.
There are both structural and behavioural reasons that
could explain the negative relationship between market
volatility and 1-day lag correlations. In one of our earlier
papers1 we demonstrated how the hedging of short gamma
risk has resulted in intraday momentum and price trending in
the last c.30 minutes of a trading day. The net short gamma
imbalance comes from index option positions, variance
swaps, dynamic delta hedging programs (constant proportion
portfolio protection), and most recently from hedging of
leveraged ETFs. As this hedging demand is liquidity driven,
its market impact is of temporary nature and reverts back
over the course of the following day. This has significantly
contributed to the reversion of daily levels. An additional
contribution to the reversion may come from asset allocation
rebalances. If the market drops, an investor will need to
increase the allocation to equities and buy the market. Some
of these allocation trades can be only done on the following
day causing reversion of returns (e.g. allocations to mutual
funds that can not be priced/traded intraday).
In addition to the negative relationship between volatility
and 1-day lag correlation, Figure 3 shows a pronounced drop
in 1-day lag correlation over the last 30 years. We believe that
this drop is caused by an increased use of leverage dynamic
hedging programs, and derivatives that create short gamma
exposure. While gamma hedging programs have contributed
to a current low (negative) level of autocorrelation, they are
not responsible for the relationship between autocorrelation
and volatility. By looking at the 30-year time period from
1928 to 1958 (Figure 4), we also found that as volatility rose
autocorrelation fell, and vice versa. In particular, following
1 “Market Impact of Derivatives Hedging”, JPMorgan paper, 22 October 2009.
Apr-Jun 2009
40%
Volatility, Autocorrelation of 1d Returns
Volatility
20%
20%
Jan 05
30%
Autocorrelation
Realized Volatility
40%
the crash of 1929, autocorrelation dropped to low levels similar to the current readings. Thus, we find that the negative
relationship between volatility and 1-day lag correlation was
present even before derivatives contracts were introduced to
the market.
30%
18M Realized Volatility
18M Autocorrelation (1-Day)
20%
10%
0%
-10%
-20%
-30%
1980
1984
1988
1992
1996
2000
2004
2008
Figure 3. Market Volatility and Lag-1 Autocorrelations over the
past 30 years
60%
Volatility, Autocorrelation of 1d Returns
40%
60%
18M Realized Volatility
50%
18M Autocorrelation of Returns 1
day lag
40%
30%
20%
10%
0%
1929
-10%
1934
1939
1944
1949
1954
-20%
Figure 4. Market Volatility and Lag-1 Autocorrelations for
1928-1958
We suggest that the relationship between volatility and
autocorrelation is also related to behavioural finance and
market impact theory. A high volatility environment is usually an environment of high economic uncertainty. In turn,
uncertainty can cause market participants to overreact on
insignificant and often contradictory news. Low liquidity
increases temporary market impact, and dissipation of this
temporary impact further contributes to price reversion.
The behavioural and structural reasons may be related to
each other. As investors are driven by “fear and greed”, they
usually want to participate in the market upside, but scramble
for protection in a market downturn. This has caused
investors to frequently overreact on insignificant news by
selling on the downside and buying on the upside – →
35
Techniques
effectively increasing the overall “short gamma” of the market. The overreaction is then followed by an adjusting phase
leading to reversion. Similarly, the factor of “greed and fear”
has led to demand for downside protection, leverage, and
dynamic hedging programs that increase the overall market
gamma. These programs are usually rebalanced and hedged
once a day, usually near the market close. The hedging impact
causes intraday momentum and is particularly pronounced in
volatile markets. The temporary nature of this market impact
(hedging of any short gamma trade) contributes to the subsequent price reversion.
Investment Strategies
In this section we analyze the performance of a trading strategy that aims to profit from the daily reversion of market
returns and show how reversion strategies are closely related
to intraday momentum strategies. The simplest way to implement the reversion strategy would be to buy the market at the
close if it is down for the day, or sell it on the close if the
market is up for the day. This simple strategy is repeated
every day. If the market is mean reverting, and the average
amount of mean reversion exceeds the transaction costs, this
strategy will be profitable. Volatility will also help the strategy
as high volatility usually results in lower autocorrelations, and
provides higher absolute daily returns. As the strategy has a
large turnover, it can be implemented by investors with very
low transaction costs. Figure 5 shows the performance of the
simple reversion strategy for the S&P 500 Index over the past
four years. We show the performance with transaction costs
350
in a 4-10bps range. In 2008, the strategy would have had a
114% return and 40% volatility for an information ratio of
around 2.8. We estimate the performance in 2007 would have
been strong as well and the strategy also performed well for
the EuroStoxx 50. Similar to the intraday momentum strategy that we discussed in our earlier paper2, the reversion strategy thrives in volatile market conditions.
Finally, we investigate the link between daily reversal patterns, and intraday momentum patterns both in the US and
in Europe. Intraday momentum patterns result from the
hedging of market short gamma exposure and cause the market to extend gains or losses in the last c. 30 minutes of a
trading day. We also compared the returns of these two
strategies (the close-to-close reversion strategy vs. the intraday momentum trend as measured by the correlation of
9.30AM-3.30PM returns to 3.30PM to 4.00PM returns) and
made several observations that directly linked daily return
reversal patterns with intraday momentum patterns.
In 2006 there was no reversal effect in the US, and during
the same year there was no momentum effect either. In 2005,
the reversal effect was absent in Europe, and that was the
year when the momentum effect was the weakest as well.
2007 and 2008 in the US were the years with the strongest
momentum and reversal effect. It would be hard to imagine
that the relationship between momentum and reversal effects
is coincidence. This is confirmed by a statistical test we carried out that point to a strong relationship between the intraday momentum and the daily reversal effect, as shown in
Figure 6.
20%
Momentum (Intraday)
“IS THE SIMULTANEOUS DROP
IN AUTOCORRELATION AND
INCREASE IN VOLATILITY A
COINCIDENCE…?”
15%
Correlation: 80%
t-stat: 3.3
US '08
US '07
10%
5%
EU '06
EU '08
US '05 EU '07 EU '05
Performance
S&P 500 Performance
300
Reversion Strategy 10bps cost
250
0%
-20%
-15%
-10%
-5%
Reversal (Close-to-Close)
0%
Source: J.P.Morgan Derivatives and Delta One Strategy, Bloomberg
200
Figure 6. Reversal and Momentum Effect in US and Europe
150
100
50
Jan 05
Sep 05
May 06
Jan 07
Sep 07
May 08
Jan 09
Figure 5. Simple Reversion Strategy for S&P 500 – Performance
Chart (left) and Performance Summary (7bps transaction cost, right)
36
US '06
Apr-Jun 2009
Marko Kolanovic and Amyn Bharwani, US Equity
Derivatives & Delta One Strategy, J.P.Morgan Securities
Inc.
Full details can be found in “Price Patterns and Chaos Theory in
Volatile Markets”, J.P.Morgan, 05 February 2009.
2 “Market Impact of Derivatives Hedging”, JPMorgan paper, 22 October 2009.
Interview
INTERVIEW
Perry Kaufman has thirty-five
years experience working in
financial engineering and hedge
funds. Beginning as a scientist
in the aerospace industry, where
he worked on the navigation
and control systems for
Gemini, he has been in the
forefront of market analysis
since the 1970s. Perry specializes in the development of fully
systematic trading programs in
derivatives and equities, as well
as asset allocation and leverage
overlays. He is the author of
‘New Trading Systems and Methods’
(Wiley 2005) and ‘A Short Course
in Technical Trading’ (Wiley
2003).
Apr-Jun 2009
37
Interview
TA: How are you currently employed? Do you work
mainly as a consultant to hedge funds?
PK: I’m a partner with a European hedge fund. I’ve always
been active developing trading strategies for institutions and
I continue to do that. I’ve been fortunate to participate in
three successful hedge funds over the years and I’m looking
forward to continuing that run.
TA: Your book, ‘New Trading Systems and Methods’ is now in
its fourth edition. This still appears to be the book on
building trading systems. Given the huge interest in the
subject, why do you think there are so few books and literature available on the subject?
PK: As you can see from the size of the book, it is a big
effort to put all the pieces together. Other authors have chosen to look at a narrower topic. I’m facing the daunting task
of overhauling the book in the next edition as some of the
material has been historically interesting but is not practical
anymore. In addition, there are many topics that have been
omitted due to space. A new edition would be both cleaner
and more comprehensive. But before that I’m publishing
‘Alpha Trading’, a book highlighting strategies that remove
directional risk such as market neutral, pairs trading, and statarb. I believe many traders will be interested in ways to avoid
the risks of last year and gain diversification. It should be out
in the first quarter of 2010.
TA: What effect has the market impact of the credit
crunch had on systematic trading systems?
PK: Anyone who has access to hedge fund reports can see
that systematic futures trading had its best year ever. Some
funds were up 30% to 100%. I understand that the Managed
Futures Association conference in Florida was extremely well
attended last month with investors looking for the one glimmer of light. But these successes are in a broad range of
financial instruments and commodities and are not widespread in stock trading.
The biggest successes were macro-trend systems. These
generally run trends of 60 days and longer. You can imagine
how well they did capturing the move up in the index markets and then staying short all the way down. The same goes
for the crude oil market, gold, and grains. Markets were
pushed by a large number of commodity funds promoting
inflation protection which just feed on one another. Even the
simplest trend-following system made big money.
This brings a new light to understanding performance.
Investors have been very selective about finding systems that
never had a bad year. Macro-trends lost 5% to 10% during
2004-2006, which caused some attrition. Now it looks as
though it was a serious mistake to leave. It’s a dose of reality.
We need to stay with sound concepts and accept drawdowns
that are part of the profile.
38
Apr-Jun 2009
TA: Are all equity trend-following systems now redundant? If we are now in a rangebound market, are there
many systems which are now wholly unprofitable?
PK: First, no one knows what type of market we’re going to
have this year or next. If we did, then everyone would be
making money. Equity trend-following systems have always
been a problem because the stock market has an incredible
amount of noise that makes it difficult to identify the trend
in a timely fashion. The best trend for stocks has always been
the very long-term.
Last year’s big move up, followed by a historic drop makes
it look as though short-term trends work. But over the longterm they don’t. You would need to know when the big
moves will start and end, and I wouldn’t put too much money
on that.
A range-bound market can be traded successfully, and is a
better bet than a trending market in stocks. That’s why I
include mean reversion strategies in my portfolio, as well as
other numbers games. The strategies in my new book, ‘Alpha
Trading’, all target profits in sideways markets, but can still
profit when there is a secular move. We can never forget how
important it is to diversify. But even if long-term trend-following systems aren’t profitable next year, it’s no reason to
get rid of them. They profit from the fat tail, and you never
know when that will happen.
“[IN 2008]…SYSTEMATIC
FUTURES TRADING HAD ITS BEST
EVER YEAR. SOME FUNDS ARE UP
30% TO 100%”.
TA: What are the main challenges that hedge funds face
when they look to automate their trading strategies?
PK: Some of the hedge fund strategies can’t be automated
easily. Take merger-arbitrage for instance: I worked on a fully
automated version that was doing well until the credit
squeeze. Then more deals fell through and changed the
risk/reward profile. Merger-arb has a short options profile –
lots of small profits and an occasional big loss. Once the “lots
of small profits” becomes “some small profits” you have a
problem. Other companies have been successful trading
merger arb because they can qualitatively determine if a deal
is likely to close and then raise their percentages. You can’t do
that automatically.
On the other hand, hedge funds that have discretionary
overlays, that is, they can decide which trades to take and
which to pass, are likely to move more to a fully systematic
method. It’s easier to justify and far easier to control the risk.
→
I would say that full automation is politically correct.
Interview
TA: What is the essence of a good trading system?
PK: A sound premise and a simple concept; one that works
across a broad range of markets, or at least those markets that
it’s supposed to work on. For example, a trending system that
works on fixed income should work, without changes, on FX.
A market neutral program that works on energy stocks
should work, without changes, on pharmaceuticals. The performance doesn’t need to be perfect, but it needs to be realistic. If you combine a lot of markets with modest performance you get a portfolio with good performance.
TA: What model strategies have been most successful
since the onset of the credit crunch?
PK: The credit problem has caused the biggest, fastest trends
that I’ve ever seen, and I’ve seen a lot. It is certainly comparable to the gold crisis in 1980 and much faster than the
demise of the tech bubble in 2000. A wide range of trendfollowing systems would have been profitable along with all
of the programs that traded a diversified financial and commodity portfolio.
TA: What is the success rate usually achieved with automated systems? By this I mean, how many go on to lose
money or fall massively behind their benchmark?
PK: That really depends on the system. Some developers
fine-tune the parameters in their strategies or have too many
rules. I’m sure they would work in the recent subprime market, where everything worked, but they would never have
long-term success. Normally they fail quickly so the anxiety
doesn’t last long.
It’s interesting to look at the historic performance of hedge
funds to see how difficult it is to sustain good success. There
are extreme examples, such as LTCM which overleveraged
itself into extinction, but there are many other hedge funds
that kept seeking better and better returns. Recently, Citadel
posted greater than a 50% loss in its bell-weather fund (I’m
not sure how much of it was fully automated). It’s somewhat
like the Peter Principle; you get better and better until you
fail.
But
look
for
yourself.
The
website:
www.managedfutures.com shows all of the major futures
funds (remember that’s my main area of expertise), their performance and their maximum drawdown. The oldest one,
Milburn Ridgefield, started in 1977 and has a return of
14.3% per year and a 33% drawdown. Campbell, which
began in 1983, has an annualized return of 12.5% and a
drawdown of 42%. Most of those years have been brilliant,
but then there is the occasional devastating surprise. In retrospect, we would have all stayed with a program that returned
14% from 1977 to 2009. But that’s now. Would we have really held on with a 33% drawdown, or 42%? Not likely.
Apr-Jun 2009
TA: What systems and software do you use and why?
PK: I still do some early stage development in TradeStation,
where I can see the signals and decide if they make sense.
Some of my strategies are too complicated, especially the
market neutral and large-scale arbitrage. For those I use
Fortran. Yes, I’m old(ish) and started in aerospace. But
Fortran is still actively used in Europe and it still beats all
other languages for speed.
TA: What ongoing research do you carry out for your
existing systems and/or new systems?
PK: I monitor existing systems to be sure they stay in profile,
but it’s very difficult to catch a problem before it happens.
For example, during the late 1990s I was trading a short-term
breakout strategy. Most of the money was made in the bund
and other fixed income futures, but then the bund started a
run of lower profits. When I studied the problem I found
that volatility had been dropping and had reached a point
where it was barely covering slippage. I can’t change the market but I could eke out a bit more profit by extending the
holding period. The longer you hold a trade, the greater the
potential profit (and the greater the risk); that helped for a
while. But systems that target a specific pattern, or take
advantage of certain industry fundaments, can just fail after a
while and need to be abandoned.
That’s why I am constantly developing new strategies.
These days I find myself concentrating on simple, long-term
trend systems, or shorter-term market neutral strategies. But
it’s not easy to come up with new ideas. You need to watch
the market and develop a philosophy.
“MOST DEVELOPERS USE
ALL THE DATA FOR TESTING;
THEY HAVE NO WAY OF
TELLING IF THEY’VE OVERFITTED THE DATA OR CREATED SOMETHING BRILLIANT”.
TA: What risk management techniques do you employ
in your systems?
PK: Risk control is really important and I have my own way
of doing it. I’ve written a full blown portfolio optimization
program, which I published as the last chapter in ‘New
Trading Systems’. But I’ve decided that’s overkill. You →
39
Interview
need to follow a few simple steps: (1) diversify as much as
possible (2) size your positions so that each trade in every
market has the same risk of loss (3) maintain constant target
volatility.
The first one should be clear, but you need to have equal
risk across different sectors. Number two draws on the fact
that you maximize diversification if you have an equal chance
of a profit or loss in everything. Otherwise, you are concentrating your chances in just a few markets. The third point
emphasizes that you must scale down your entire portfolio
when the risk increases, then scale it back up as risk drops. All
three points try to keep risk under control.
TA: How do you deal with correlated markets? What
problems do they pose in system building?
PK: Another good question and the root of many losses. It’s
not the daily correlations that are important, it’s the extremes.
When markets go to peak volatility, correlations may go to 1.
It’s not the markets that are the same; it’s the flow of money.
A lot of money follows profits and when that disappears, the
money flows out. During a crisis like the recent one, losses in
stock people don’t know what semi-variance is.
Either way, the number is not cast in stone. As time goes
on, both the profits and losses should get bigger. But it’s also
unreasonable to say that your history includes all possible situations. And, if you’re looking at back-tested results and not
real trading, then I guarantee your results are highly optimistic, no matter how conscientious you’ve been.
TA: What basic rules do you follow when backtesting a
strategy/system? What are the most common mistakes
made when backtesting?
PK: That’s easy. Most developers use all the data for testing.
When they’re done they have no way of telling if they’ve
overfitted the data or created something brilliant. You need to
set aside about half the data, but not the first or last half, but
alternating periods. Markets have changed and if you are testing 20 years of data and used the 1990s for development and
then tested it out-of-sample from 2000 on, you’re in for a
shock. The data during these periods have little in common.
It’s best to use 5 years on and 5 years off to give yourself a
chance to see the developing changes.
“SYSTEMS THAT TARGET A SPECIFIC PATTERN CAN JUST
FAIL AFTER A WHILE AND SO NEED TO BE ABANDONED”.
some markets caused money to flow out of all markets. The
only way to deal with it is: (1) keep reducing your position as
volatility increases, and (2) try finding a strategy that is not in
the market all the time. If you’re only in 50% of the time then
you avoid 50% of the price shocks – maybe.
With regard to system building, you need to be diversified,
so trading in markets that are highly correlated won’t do that.
However, in the spirit of diversification, if you have two markets that are very closely correlated, you are better off trading half the position in each. There is no percentage is choosing one.
TA: What is the best way to measure the risk of any trading strategy?
PK: I think the traditional way is as good as any. Finding the
standard deviation of daily returns and multiplying by the
square root of 252 (days) gives you the annualized risk. A
method that I prefer is to look at the semi-variance, which is
the risk of a drawdown, when you ignore all of the days that
post new high profits. Or you can collect each drawdown and
find the standard deviation of the maximum loss of each
drawdown. Then you have removed any distortion cause by
upward burst of success and have focused only on the losses. But then you need to calculate it both ways because most
40
Apr-Jun 2009
When you’ve successfully run your strategy on the out-ofsample data, you’ve proved that it all works. Now you can go
back and retest, using the same process (not adding anything
new), on all the data. The bad news is that if your out-ofsample test fails, you must discard your system. Any change
to it now is overfitting as you would be fine-tuning the problem areas. So you’d better take your time and get it right the
first time. Coming up with another idea is going to be
harder.
TA: Of the different types of strategies that you are
involved with (stat-arb, trend following etc) which is
likely to be most effective over the next couple of years?
PK: If I knew that I would know what the markets were
going to do and I’d only use the system that makes the most
money. So the answer has to be to diversify systems: stay with
the simple, most robust ones, tested over the longest period
of time. Strategies that worked over many years must be simple. Anything too complex would fail. This past year should
have taught us that what we know is that we don’t know what
the markets will do. You’ll need to accept risk to stay in the
game. But in the end, taking systematic and controlled risks
will pay off.
→
Interview
TA: How much of a problem is model performance
decay? Why does this happen and what action is usually taken if a system’s performance starts to decline?
PK: I have described the case where volatility dropped, which
is a problem for any short-term system. Trend systems
looked bad in 2004-2006 when the fixed income markets had
no direction. Trend systems always look bad in the stock market because there is too much noise. You can fool yourself if
you made money on the historic drop in all stocks in the second half of 2008. But I wouldn’t hold my breath waiting for
that to happen again. A system that’s worked over 20 years of
data shouldn’t decay easily. If it does, look at simplifying it.
TA: What performance measures do you use?
PK: I use the Sharpe ratio most of the time. Well, not exactly the Sharpe ratio but the Information Ratio, which is the
same thing without using the interest rates. Because we’re
always looking for a comparative measure, removing the
interest rate doesn’t make a difference.
As long as you’ve mentioned the ratio, let me say one thing
about expectations. When you develop a system using insample data, it’s likely that you will show a ratio of 3 or more.
If it’s much higher then you should be suspicious that the
system is overfitted. Another way to recognize if it’s overfitted is the kurtosis; a really good system will have a kurtosis of
maybe 6. An overfitted system will have a kurtosis of 15 or
more. If you find that then the system will never work.
Getting back to expectations, for a best case scenario, when
you go out-of-sample, the ratio should drop from 3.0 to
maybe 2.2. Then, in real trading it would drop again to 1.4.
It’s more realistic, and still good, to see the ratio start at 2.0,
drop to 1.2 and then fall to 0.8 in real trading. But don’t get
discouraged; that’s a successful system. I haven’t looked at the
stock market ratio since this crisis started, but if it’s as high
as 0.4, I’d be surprised. I know that investors all want systems
that have ratios of 3.0 or better, and institutions want at least
1.0, but they need to reevaluate their objectives. By seeking
only high-ratio systems they program themselves for failure
sometime down the road.
TA: What technical strategies (e.g. moving averages,
momentum etc) are most commonly employed in systems?
PK: The most popular techniques are moving averages and
momentum, such as RSI, stochastics, or MACD. Systems usually have stops and profit-taking, often based on market
volatility. A lot of traders watch the momentum on their
screen and try to go long when the momentum is oversold.
TA: What technical analysis methods have you found to
be most effective?
PK: It’s not the technical method; it’s the underlying premise
Apr-Jun 2009
or concept that’s important. If you’ve decided that pairs trading strategy is the way to go, then there are many different ways
to measure when to enter a trade. Any momentum indicator
will work, as will any volatility measure. And that’s the proof of
a good idea. You don’t need to struggle with the implementation to make money. If all of the indicators produce profits
then it’s a good bet you’ve got a profitable strategy. For example, with pairs trading, the only important issue may be that the
two stocks have enough volatility to be profitable.
TA: What are the specific problems associated with
building a trading system based on pattern recognition?
PK: There’s a good book out, ‘The Encyclopedia of Chart
Patterns’, by Tom Bulkowski. In it he’s automated and tested
most chart patterns and concluded which are most likely to
work. I have one program that’s based on chart patterns and
it’s been very successful. However, you need to pick the environment. For example, if you’re looking for a bullish breakout of a triangle, then it helps if the overall market is trending up. For the most part, I wouldn’t use any chart pattern in
isolation.
One problem with chart patterns for individual stocks is
that the arbitrage with the S&P can crush those patterns. You
expect prices for one stock to stop moving higher at a resistance level, but if the S&P index stops sooner or later, you can
bet that your stock will stop at the same time. You now have
to watch both the index and the stock, whichever comes first.
TA: What patterns are the ones most often identified
and successfully traded?
PK: I like rounded bottoms and head-and-shoulders, but
they’re very difficult to implement automatically. If I can’t
automate it, then I can’t understand the risks and returns, and
I don’t know if I can really identify it in a timely manner. So
I stay with simpler patterns.
TA: What value does regression analysis of market
prices have in forecasting?
PK: Regression analysis can be useful in different ways. It can
be an alternative trend strategy, which has a little forecasting
built into it. Normal trend-following is entirely reactive, so
using a forecasted linear regression slope gives you a new
view.
Better still, you can use linear regression to compare the
strength or weakness of different stocks. In my new book,
‘Alpha Trading’, I use linear regression to build a very nice
market neutral program, where you can buy the strongest and
sell the weakest, or the opposite, sell the strongest and buy
the weakest. Either way, the slope is a great way to measure
relative value in the stock market.
Perry Kaufman can be contacted at: [email protected]
41
Research Update
VOLATILITY
CLUSTERS IN
US STOCKS
One striking feature of the United States stock market is the tendency of days with very large movements of stock prices to be
clustered together. Three US-based researchers have defined an
extreme movement in stock prices as one that can be characterized as a three sigma event; that is, a daily movement in the broad
stock-market index that is three or more standard deviations
away from the average movement. They find that such extreme
movements are typically preceded by unusually large stock-price
movements in previous trading days. A particularly robust finding is that extreme movements in stock prices are usually preceded by large average daily movements during the preceding threeday period. The authors also make some interesting comparisons
with the weather, in that they find a similar clustering of extreme
observations of temperature in New York City.
All papers are available from the Social
Science Research Network, SSRN,
www.ssrn.com
DOES OIL LEAD
THE MARKETS?
It has been argued that oil price changes lead the aggregate market in most industrialized countries, and that an underreaction
to this information is something investors can profit from. Lars
Qvigstad Sørensen of the Norwegian School of Economics and
Business Administration has identified oil price changes that are
caused by exogenous events, and shows that it is only these oil
price changes that predict stock returns. The exogenous events
usually correspond to periods of extreme turmoil - either military conflicts in the Middle East or OPEC collapses. Given the
source of the predictability, the author questions its usefulness
as a trading strategy.
Sørensen, Lars Qvigstad,Oil Price Shocks and Stock Return
Predictability(February 11, 2009).
42
Apr-Jun 2009
In another paper, researchers at the University of Georgia
have found something similar. In their investigation into the
causes of the stock market crash in 1987, they find that while
the market crash on October 19 was the largest one-day S&P
500 drop in percentage terms in history (20.47%), there was
also a large market drop (10.12%) in the three trading days
before the crash. The three-day decline was the largest in more
than 40 years, large enough that the drop was news itself.
Saha, Atanu, Malkiel, Burton G. G. and Grecu, Alex,The Clustering of
Extreme Movements: Stock Prices and the Weather(February, 09 2009).
McKeon, Ryan and Netter, Jeffry M.,What Caused the 1987 Stock Market
Crash and Lessons for the 2008 Crash(January 19, 2009).
REVERSE DISPOSITION
Three researchers have investigated the disposition effect – the
tendency to sell winning stocks but hold on to losing stocks –
and reverse disposition, an argument that says the disposition
effect is only correct for describing an investor’s behaviour once
he/she has invested in an asset, but ignores the investment decision in the first place. In cases of reverse disposition, when the
expected return of a stock is sufficiently large, the investor will
invest most of their wealth in the stock and, after a gain, he/she
will take an even larger position. This latest paper develops a
new measure for disposition effects based on the average length
of a trading strategy and whether it is loss-making or profitable.
Using this new measure, the authors find the existence of disposition and reverse disposition effects. The reverse disposition
effects are particularly prominent during falling markets. They
also note that the bias reduces for more experienced traders,
traders holding investments for a longer period, and traders
using buy strategies.
Krause, Andreas, Wei, K. C. John and Yang, Zhishu,Determinants of
Disposition and Reverse Disposition Effects(March 27, 2009).
Research Update
Trading the Index Effect
The index effect, or the excess returns of
a stock added to a leading index, is one of
the most researched pricing anomalies in
finance, but is the index effect shrinking?
To answer this question, the Index and
Portfolio Services department of
Standard & Poor’s has studied the index
effect for five major equity markets: U.S.
(S&P 500), Canada (S&P/TSX 60), Japan
(Nikkei 225), U.K. (FTSE 100) and the
Germany (DAX 30). They find that
excess returns for index additions have
diminished over the past five years. The
median excess return of S&P 500 additions was 3.8% for the past five years,
compared to 6.0% for the five years prior.
The declining pattern is also observed in
Nikkei 225, S&P/TSX 60 and DAX 30,
but not the FTSE 100. The diminishing
index effects may be attributed to several
factors: First, the index effect has fallen
victim to its own popularity. As more
arbitrageurs have come in to the market,
arbitrage profits have reduced. Second,
changes in market structure and trading
patterns of index funds have dented the
index effect. The index effect may never
vanish completely. But its days as a profitable trading strategy may be numbered.
Alternative index-related profit opportunities involve trading index changes in the
options market or trading index shares.
As such, the Standard & Poor’s team
has analyzed the impact on publicly traded options of additions to the S&P 500.
Their analysis shows that changes in atthe-money call and put prices are 20 to
30 times higher than changes in the corresponding stock price. Comparison
between the inter-index transfers and
outside additions shows far greater index
effect on options prices if the underlying
stocks are introduced out of the S&P
1500 index family. Between announcement and effective dates, the median atthe-money call option rises 120% for
Why did leveraged
ETFs underperform
in 2008?
Mean reverting markets lead to significant underperformance for leveraged
ETFs, according to Marko Kolanovic
and Amyn Bharwani of JP Morgan.
Leveraged ETFs aim to provide leveraged long and short exposure to the daily
return of certain equity indices. If, for
example, the underlying index is down it
would be reasonable to expect a 2x leveraged short ETF to be up approximately
twice the amount. However, leveraged
ETFs have surprised investors with the
rapid deterioration of their performance
over longer time periods. For instance,
over the course of last year, a double
short Dow Jones US Real Estate ETF
was down 50% at a point in time when
the index was down 42%. In order to
understand the drivers of such large
underperformance, Kolanovic and
Bharwani show how the effect of compounding causes this significant discrepancy. The overall discrepancy is dependent upon the extent to which the market
mean reverts (leveraged ETF underperforms) or the market trends (leveraged
ETF outperforms). The authors observe
that the significant underperformance of
leveraged ETFs in 2008 suggests that
market returns were more mean-reverting than trending, consistent with a period of high market volatility.
From “Price Patterns and Chaos Theory in
Volatile Markets”, Kolanovic, M and
Bharwani, A., JP Morgan, 05 February 2009.
Apr-Jun 2009
additions from outside the S&P 1500,
and 32% for promotions within the S&P
index family. While they point out it is
not possible to capture most of these
price changes because they happen very
shortly after the announcement, their
study highlights several trading strategies
with statistically significant returns.
Buying at-the-money calls for stocks
added to S&P 500 from outside the S&P
1500 on the day after the announcement,
and selling the position on the effective
date of addition yields returns of 31%
on average.
Standard & Poor's, Index and Portfolio
Services, The Shrinking Index Effect - A
Global Perspective (December 30, 2008).
Standard & Poor's, pp. 1-14, November 2008.
Dash, Srikant and Liu, Berlinda,Capturing the
Index Effect Via Options(June 2008).
SHORT SELLERS
EXPLOIT
OVERREACTION
Short sellers increase their trading following positive returns and they correctly predict future negative abnormal
returns, according to researchers from
Ohio State University and Rutgers
Business School. The researchers, who
examined short selling in US stocks for
2005 (where short sales represent a staggeringly high 24% of NYSE and 31% of
Nasdaq share volume), point out that
their results are consistent with short
sellers trading on short-term overreaction of stock prices. A trading strategy
based on daily short-selling activity generates significant positive returns during
the sample period.
Diether, Karl B., Lee, Kuan-Hui and Werner,
Ingrid M.,Short-Sale Strategies and Return
Predictability(February 2009). The Review of
Financial Studies, Vol. 22, Issue 2, pp. 575607, 2009.
43
Research Update
SHORT-SELLING VS. PUT-OPTIONS
Research from Benjamin Blau of Brigham
Young University and Chip Wade of the
University of Mississippi compares shortselling and put-option activity, both of which
are found to predict negative returns. Their
results provide evidence consistent with the
idea that pessimistic trading activity spills
over from the stock market to the options
market when equity borrowing costs rise. In
addition, they show that short sellers and
put-option traders react differently to prior
stock performance. While short selling
increases after periods of positive returns,
they find that put-option activity follows
periods of negative returns. The net effect is
that short-selling activity is better at predicting negative returns at the daily level than
put-option activity.
A team of researchers from the University
of British Columbia has shown that the
time of year of a person's birth is an
important factor in the likelihood they
become a CEO, and conditional on
becoming a CEO, on the performance of
the firms they manage. Based on a sample
of 321 CEOs of S&P 500 companies
from 1992 to 2006 they find that (1) the
number of CEOs born in the summer is
disproportionately small, and (2) firms
with CEOs born in the summer have
higher market valuation than firms headed by non-summer-born CEOs. An
investment strategy that bought firms
with CEOs born in the summer and sold
firms with CEOs born in other seasons
would have earned an abnormal return of
8.32 percent per year during the sample
period. The evidence is consistent with
the so-called "relative-age effect" due to
school admissions grouping together children with age differences up to one year,
with summer-born children being
younger than their non-summer-born
classmates. The relative-age effect has
been demonstrated in numerous sporting
and other contexts to last to adulthood
and to favor older children within a
school year. Those younger children who
nevertheless succeed by overcoming their
disadvantage have to be particularly capable within their cohort. According to the
authors, the advantage enjoyed by older
children and the particularly high capability of successful young children explain
the statistically and economically significant findings.
The use of celebrity endorsement as a
part of marketing communication strategy has been gaining popularity over the
past years. Monies paid out by firms on
endorsement contracts are estimated to
be 10% to 25% of total advertising
expenditures. However, empirical evidence on the effect of endorsement
announcements on the stock price performance of firms has been mixed at
best. As such, three researchers from
Massey University have analyzed the
share market perception of celebrity
endorsements using a sample of 102
announcements. Average stock returns
equal -0.95% on the announcement day,
implying that endorsements are generally
not viewed as worthwhile investments.
Average trading volumes first increase
and then drop shortly after the
announcement, which suggests that the
news begins to disseminate quickly. Stock
returns and trading volumes depend on
the
level
of
press
attention.
Blau, Benjamin M. and Wade, Chip,A Comparison of
Short Selling and Put Option Activity(February 23,
2009).
Summer-Born
CEOs Outperform
Price, Volume
and News
There have been several attempts in asset
pricing theory to link information availability to expected returns, with mixed
success. Umut Gokcen, a PhD student at
Boston College, has contributed to this
literature by first proposing a new proxy
for information revelation, and second,
by demonstrating its relation to returns.
His proxy is based on the widely known
price/volume relation; that these two
variables tend to move together around
public news events. In the 1964-2007
period, he finds that the estimated correlation between the absolute value of price
changes and dollar trading volume of
individual NYSE stocks is significantly
negatively related to their future returns.
Information revelation seems to imply
lower expected returns going forward. A
long/short trading strategy based on this
information proxy reveals its economic
significance. A net-zero-investment portfolio generates alphas around 3% to 4%
(annualized), depending on the portfolio
construction scheme and the risk adjustment. Alphas jump up to almost 7% per
year among smaller stocks, indicating that
the information risk premium might be
larger when the asymmetry in the information environment is greater. These
results can be interpreted as time-varying
expected returns in a rational setting or as
over/under reaction of investors in a
behavioral setting.
Du, Qianqian, Gao, Huasheng and Levi,
Maurice D.,Born Leaders: The Relative-Age Gokcen, Umut,Information Revelation and
Effect and Managerial Success(March 18, 2009). Stock Returns(March 18, 2009).
Celebrity endorsements – a sell signal?
44
Apr-Jun 2009
Endorsements that appear in a major
newspaper show higher average returns
and larger trading volume changes at
announcement date than those
announced on the corporate website
only.
Molchanov, Alexander, Ding, Haina and
Stork, Philip A.,The Share Market Perception
of Celebrity Endorsements: An Empirical
Analysis(January 20, 2009).
Research Update
The Merit of Analyst Recommendations
The debate continues on the usefulness
of equity analyst recommendations. A
researcher in Australia has examined the
investment value of analyst recommendations on constituent stocks of the
S&P/ASX 50 index. For the period from
30 June 1997 to 30 October 2007,
William He found that stocks with
favourable consensus recommendations
(strong buy and buy) on average earn a
higher return than the market, whereas
stocks with unfavourable recommendations (strong sell and sell) earn a lower
return. An investment strategy that overweights (underweights) stocks with
favourable (unfavourable) consensus recommendations, in conjunction with daily
rebalancing, outperforms the market in
terms of raw return and risk adjusted
performance measures. The investment
strategy, however, involves high levels of
trading and, as a result, no significant
abnormal returns are achieved after
accounting for transaction costs. Less frequent rebalancing, under most situations,
causes a decrease in both performance
and turnover.
In addition to firm recommendations,
brokers also issue industry recommendations on a monthly or quarterly basis.
These recommendations are based on an
analysis of publicly available macroeconomic data, and are relatively stale. As
such, it could be expected that industry
recommendations have no value to
investors. Using newly available data, a
US- and Singapore-based research team
rejects this hypothesis and finds that
portfolios long in industries about which
analysts are optimistic and short in indus-
tries about which analysts are pessimistic
generate an out-of-sample alpha of
11.7% per year. According to the
researchers, such a strategy is easily and
cheaply implementable using ETFs. They
also find that industry recommendations
provide information independent of
firm recommendations. A trading strategy that exploits both industry and firm
recommendations yields an annual outof-sample alpha of 19.2%.
He, Peng William,The Investment Value of
Australian Security Analyst Recommendations:
An Application of the Black-Litterman Asset
Allocation Model(February, 06 2009).
Kadan, Ohad, Madureira, Leonardo, Wang, Rong
and Zach, Tzachi, Do Industry Recommendations
Have Investment Value?(March 16, 2009).
Bad News Travels Fast DISTINCTIVENESS
Bad news is more efficiently priced into the price of a stock
than good news, especially for “low attention” stocks, according
to Charles Gaa of the University of Oregon. In his research,
Gaa devised a portfolio strategy based on long positions in neglected stocks (typically small firms with low analyst coverage,
low institutional ownership, and little trading activity) and found
that it yielded excess returns of approximately 70 bps per
month between 1984 and 2005. The author also finds evidence
of a significant "negativity bias" in attention: holding other factors constant, bad news is more likely to attract coverage than is
good news regarding an otherwise-identical firm. This suggests
that findings of significant premia for "neglected" stocks may
be explained, at least in part, by underreaction to positive news
events from these firms.
Gaa, Charles,Asymmetric Attention to Good and Bad News and the
Neglected Firm Effect in Stock Returns(February 13, 2009).
HAS ITS REWARDS
How do you measure the distinctiveness of hedge fund strategies
and how important is it that hedge funds pursue their own investment ideas? A team from the University of California has constructed a measure of the distinctiveness of a fund’s investment
strategy based on historical fund return data – the Hedge Fund
Strategy Distinctiveness Index (SDI). Their main conclusion is
that, on average, a higher SDI is associated with better subsequent
performance. Funds in the highest SDI quintile significantly outperform funds in the lowest SDI quintile by about 6 percent over
the subsequent year.
Zheng, Lu and Wang, Ashley,Strategy Distinctiveness and Hedge Fund
Performance(September 2008). 21st Australasian Finance and Banking
Conference 2008 Paper.
MOMENTUM EVERYWHERE
All papers are available from the Social Science Research Network, SSRN, www.ssrn.com
Value and momentum ubiquitously generate abnormal returns for
individual stocks within several countries, across country equity
indices, government bonds, currencies, and commodities – but do
similar conditions occur simultaneously across them all?
According to research from the National Bureau of Economic
Research, they do. In their study, they look at global returns to
value and momentum and find that value (momentum) in one
asset class is positively correlated with value (momentum) in other
Apr-Jun 2009
asset classes, and value and momentum are negatively correlated
within and across asset classes. Moreover, liquidity risk is positively related to value and negatively to momentum, and its importance increases over time, particularly following the liquidity crisis
of 1998.
Asness, Clifford S., Moskowitz, Tobias J. and Pedersen, Lasse Heje,Value and
Momentum Everywhere (March 6, 2009).
45
Books
CANDLESTICK CHARTS
An introduction to using candlestick charts
By Clive Lambert
Published by Harriman
House
175 pages
£24.99
Clive Lambert is well known in the UK technical analysis community as an expert on candlestick charting, a member of the board of the Society of Technical Analysts (STA), and as the
founder of FuturesTechs, a trading consultancy. After a general introduction to candlesticks
including their construction and how they convey the psychology of the market, Lambert goes
on to give detailed and in-depth descriptions of numerous candlestick patterns and formations.
These include single reversal patterns (hammer, shooting star, hanging man etc) and multiple
reversal patterns (engulfing patterns, dark cloud cover, piercing pattern etc) along with various
continuation patterns. With clear and detailed descriptions of how to interpret each pattern
and how they generate trading signals, this book also includes many examples across all markets in the form of colour CQG charts. A well written, and above all, practical guide to candlestick charting.
QUANTITATIVE TRADING
How to Build Your Own Algorithmic Trading Business
By Ernest Chan
Published by Wiley
Trading
181 Pages
£42.50
Given the widespread interest in systematic trading systems, it is perhaps surprising that there
are so few books published on the subject; Perry Kaufman and Robert Pardo are perhaps the
best know authors on systematic trading. Ernest Chan, A New York based consultant with an
impressive CV in quants research and trading systems development has written an excellent
starter guide to anyone looking to automated a trading strategy. Chapters devoted to backtesting (using Excel, Matlab and Tradestation), risk management, execution and statistical arbitrage
answer many of the questions typically raised by those new to the subject. Nevertheless, the
book is written in a style targeted at experienced traders and is not elementary in tone.
A ll of the above books are available from the Global Investor bookshop at a discount.
Please call +44 (0)1730 233870 and quote "The Technical A nalyst magazine".
46
Apr-Jun 2009
Books
COMING SOON…
Global Markets Media is proud to announce the launch of a new range of books dedicated to the discussion of technical analysis and behavioural finance. The first book to be
published, ‘Technical Analysis in Fund Management’, features interviews with leading
investment managers and analysts on their approach to using technical analysis in their
investment decisions.
Due out June 2009
Price: £39.50 available direct from the Technical Analyst
[email protected]
The Technical Analyst is published by Global Markets Media.
Apr-Jun 2009
47
Training Courses
INTRODUCTION TO TECHNICAL ANALYSIS
Training with The Technical Analyst
The Technical Analyst offers a range of
exciting training courses for traders and
investment managers. We also offer
specialist in-house training on request.
The essential technical analysis course providing a thorough
grounding in TA techniques for traders and investment
managers new to the subject.
06 May
19 May
Hong Kong
London
SHORT TERM TRADING WORKSHOP (2 DAYS)
Course Details
Duration:
Our very popular course for all market professionals looks at a
variety of trading techniques for developing an effective short
term trading strategy.
Courses are run from 9am to 5pm and
include lunch and refreshments.
20/21 April
London
Who Should Attend:
Traders, fund managers, hedge funds,
risk managers, analysts and brokers
ADVANCED TECHNICAL ANALYSIS (2 DAYS)
This highly regarded 2-day course provides in-depth training
in the most effective technical trading strategies for more
experienced market professionals.
Principal Trainer
Trevor Neil
Trevor Neil became a
commodities trader at Merrill
Lynch in the mid 1970’s
before going on to work
at LIFFE giving technical
analysis support to floor traders.
In 2000 he became head of technical
analysis at Bloomberg where he was
responsible for training and technical
analysis software development.
07/08 May
20/21 May
Hong Kong
London
TA FOR THE PORTFOLIO MANAGER
Recognising that portfolio managers and analysts do not need
the kind of technical analysis used by day-traders and dealers,
this course concentrates on market timing from a longer-term
point of view. This course is designed to give a timing overlay
for fundamental decisions.
07/08 July
London
DEMARK INDICATORS
An in-depth look at these unique market timing tools.
18 June
London
BACKTESTING AND OPTIMISATION WORKSHOP
This two day workshop is designed for traders and investment
managers of all asset classes and time scales who are looking
to test and optimize their own trading strategy or model.
22/23 July
London
Full course details can be found at: www.technicalanalyst.co.uk/training
For further information email: [email protected]
Register online at: www.technicalanalyst.co.uk
or call: +44 (0)1483 573150
GET QUALIFIED IN TECHNICAL ANALYSIS
The Society of Technical Analysts (STA) represents and accredits
professional and private Technical Analysts operating in the UK
Originally established in the 1960s, the STA provides its members:
• Education
Monthly lectures and regular teaching courses in technical analysis
• Research
The STA Journal publishes research papers on TA techniques and approaches
• Meetings
Provide members the opportunity to discuss technical approaches and markets
• Representation The STA lobbies on behalf of analysts with data vendors, exchanges and regulators.
The STA represents the UK at the International Federation of Technical Analysts (IFTA)
• Accreditation
The STA Diploma Exam is internationally recognised as a professional level qualification
in Technical Analysis
For more information on how to join and what is involved in passing
the STA Diploma exam, visit our website at: www.sta-uk.org or call
us on +44 7000 710207
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