Technically Speaking, August 2008

Letter from the Editor

This month’s issue demonstrates the rich variety that exists within the field of technical analysis.

  • Todd Campbell presents quantitative analysis showing stock market behavior after VIX extremes. This is an example of using sentiment as an input into the trading process and demonstrates the value of sentiment when applied as part of a long-term analysis.
  • Kenneth G. Winans, CFA, CMT recently published his second book, Investment Atlas – Financial Maps to Investment Success which documents investment returns using richly detailed charts to place market returns into historical context. If you ever wondered how real estate has performed in France or Japan over the past fifty years, this book is for you. Along with other esoteric asset classes, Investment Atlas also shows stock and bond returns in detail for various historical events which can help those who realize history tends to repeat.
  • George Schade, Jr., CMT, is certainly one of those who understands the importance of studying history. He returns to the pages of Technically Speaking with a biography of R. N. Elliott. George is a renowned expert in the history of technical analysis and his writings always offer valuable insights into the minds of the pioneers of technical analysis. Sir Isaac Newton once said, “If I have seen farther than others, it is because I was standing on the shoulders of giants.” George furthers our field by uncovering the all-too-often lost histories of the giants of technical analysis, making it possible for others to climb onto their shoulders.
  • Mukul Pal uses cycle analysis to develop an analysis of what the markets may look like four years from now. It is interesting to consider how cycles are usually viewed as long-term tools but can be applied in the short-term as well. This would be application of Elliott’s work on the fractal nature of markets.
  • Ashish Kyal applies traditional trendline and indicator analysis to the Indian stock market.

As always, we hope you find this issue of your newsletter to be useful. We also welcome your contributions and suggestions.

Sincerely,
Mike Carr, CMT
Editor

What's Inside...

VIX Profi ts

We crunched the VIX numbers since 1990. Position traders beware; the success rate for a positive SPX 1-month after the...

Read More

Investment Atlas – Financial Maps to Investment Success by Kenneth G. Winans, CFA, CMT

Technical analysts often look at charts of past price action to assess the...

Read More

On the Campus: Technical Analysis on Campus at Rutgers University

The graduate level course in technical analysis at Rutgers University was originated in the early 1990’s by David Krell. He...

Read More

MTA Annual Award Recipient Profi le: Ralph Nelson Elliott

The Market Technicians Association’s (MTA’s) Award Committee seeks to honor the various Annual...

Read More

Intermarket Cycles and the World in 2012

Editor’s note: This has been adapted from previously published client research. Some figures have numbers embedded in the graphics which...

Read More

Technical Analysis Warned of Indian Equity Market Crash

S&P CNX Nifty is a well diversified 50 stock index accounting for 21...

Read More

VIX Profi ts

We crunched the VIX numbers since 1990. Position traders beware; the success rate for a positive SPX 1-month after the first +30 reading is most risky. 13 of the 40 distinct periods of +30 VIX since 1990 found the SPX down 30 days after the first 30 VIX day. Notably, of those 13, 11 began in Q3. Long term buyers take heart: the SPX was lower 12-months following the first +30 VIX reading only 6 times.

Since 1990, the volatility index has had 40 distinct periods with above 30 readings. Each time, investors who bought the S&P 500 on the first day the VIX crossed over 30 made money either 1-month, 3-months, 6-months or 12-months following. (See Table 1)

Table 1: The average return of the SPX from the 1st +30 VIX Reading

Long-term investors who bought on the first +30 reading were rewarded with an average 12-month return of 15.78%. 34 of the 40 periods were higher 12 months later. The table to the right shows the 6 periods where long-term investors were not rewarded 12-months later. As you can see, investors were rewarded at some point along the way in each of these years. (See Table 2)

Table 2

The 2nd longest period of plus 30 VIX readings occurred in 2002 following the VIX rising above 30 on August 27th. The run lasted 47 days and the S&P lost 11.49% in the 30 days following the first +30 reading. Note the begin dates for the two longest persisting periods of +30 VIX readings in 2002 were in Q3. The table to the right shows the periods of +30 VIX readings in ‘02: (See Table 3)

Table 3

This final table outlines all 40 distinct periods since 1990 where the S&P 500 moved above 30, the duration above 30 for each period, the highest the VIX reached per period, the SPX level when the VIX first hit 30 and the 1-month, 3-month, 6-month and 12-month S&P 500 returns for each period. (See Table 4)

Table 4

Contributor(s)

Todd Campbell

Todd Campbell is President and Founder of E.B. Capital Markets, LLC. Todd has been providing independent equity research to professional money managers for more than a decade. In 2003, Todd founded E.B. Capital Markets, LLC, an independent research firm serving professional money...

Investment Atlas – Financial Maps to Investment Success by Kenneth G. Winans, CFA, CMT

Technical analysts often look at charts of past price action to assess the current state of the financial markets. In this beautifully illustrated book, Winans has assembled a great deal of data to illustrate the history of stocks, bonds, commodities, and real estate markets around the world.

Winans asks, “Why did the majority of modern investors, the most knowledgeable and technologically advanced in history, mishandle the ‘Dot.com’ stock bull market and the recent ‘nothing down’ real estate frenzy?” His answer is that there is a general lack of knowledge about US financial history which results in investors making the same mistakes over and over.

To address this lack of knowledge, he has assembled hundreds of charts, many spanning more than 100 years and some more than 200 years, to put financial market history into perspective. Each chart is accompanied with narrative that places it into perspective, and helps even short term traders identify applicable concepts.

As an example of the innovative ideas in the book, I was startled to see the historic performance of mid cap stocks. It is very well documented that small cap stocks outperform large cap stocks in the long-term. I always assumed mid caps delivered performance between these two groups. Investment Atlas showed that mid caps are actually the best long-term performers.

Winans documents mid cap performance going back to 1927. Through 2007, these stocks would have delivered a total return of 1,071,395%, more than doubling the returns available through small cap or large cap stocks (Figure 1). This dramatic outperformance was also found through testing over the time period from 1958 through 2007, during which mid cap stocks also beat the returns available from global stocks.

Ken is doing an author talk and gallery tour at the Museum of American Finance on Thursday, September 18 @ 5:30pm. The talk will be followed by a book signing in the museum gift shop.

MUSEUM OF AMERICAN FINANCE
48 Wall Street
New York, NY 10005
Tel: (212)908-4110

Contributor(s)

Ken Winans, CMT

Ken Winans, CMT President & Founder, Winans Investments For 28 years, Ken Winans, CMT, has conducted groundbreaking financial research within the discipline of technical analysis while serving as a portfolio manager, investment analyst, broker and investor. Ken is the President and Founder...

Michael Carr, CMT

Mike Carr, who holds a Chartered Market Technician (CMT) designation, is a full-time trader and contributing editor for Banyan Hill Publishing, a leading investment newsletter service. He is an instructor at the New York Institute of Finance and a contributor to various additional...

On the Campus: Technical Analysis on Campus at Rutgers University

The graduate level course in technical analysis at Rutgers University was originated in the early 1990’s by David Krell. He was followed by Bruce Kamich, who taught the course until 2003. Over the years it was a pleasure to be a guest lecturer for the module on moving averages and to occasionally jump in as a substitute when Bruce encountered scheduling problems at Baruch College.

When career opportunity called, and Bruce was no longer able to continue the course at Rutgers, I was approached about taking it over. As excited as I was about the challenge of conducting my own course, I had to weigh a number of considerations before making my decision. My commodities brokerage career was typified by long hours and an inflexible schedule. The logistics of working a full day in downtown Manhattan and teaching at night in Newark, New Jersey were  difficult. In addition, offering a course of acceptable quality would require a commitment of both time and effort, which would be an additional toll on home life. After receiving the support and encouragement of my better half, Eloise, I was able to compose a list of factors to be considered in which the pros far outweighed the cons. Remembering how little there was available to me by way of formalized course or study materials early in my career, this would be a means for me to contribute to the MTA in it’s efforts to expand the knowledge of technical analysis – a way for me to leave something behind. On a personal level, having three nieces who have used a degree from Rutgers as a launch pad to extremely successful careers, it provides a way for our family to make a contribution, other than money, to Rutgers growth and continuity.

During the first lecture, time is spent explaining to students how a course covering the practical application of technical analysis, being presented from the perspective of an experienced practitioner, provides a valuable complement to their other courses which tend to be anchored in academic theory. During the semester, fundamental and quantitative analysis are explored, in an effort to compare and contrast them with, and eventually incorporate them with, technical analysis. Technical analysis websites that can be used as resources on an ongoing basis are reviewed and discussed. At least one class is scheduled in Rutgers state of the art trading room so that students can get hands on experience applying what has been learned.

It has been my experience, that for many of my students, English is not their first language. Many are getting their first exposure to a completely new discipline in a second language. Every effort is made to design lecture notes, and especially tests, in such a way as to minimize the occasion of someone being tripped up by nuance of language, and instead concentrating on the ability to convey comprehension of principles and applications. In recent semesters, a good deal of classroom discussion has been stimulated when students are reminded that they are living through a period of financial history that will be looked back on and talked about for years to come. Market moving events such as the subprime crisis and record high oil prices are familiar to students from the evening news and, as such, make for an easier fl ow of classroom discussion of topics such as trend analysis, intermarket relationships, etc.

Difficult logistics tend to make the days I teach a bit like running a marathon. I arise very early to drive down to Newark to leave my car on campus. I then walk across town to Penn Station, where I pick up public transportation to my office at Prudential in lower Manhattan. In the afternoon I reverse the process to get back to campus for class. As I am only on campus the one day a week that I teach, I maintain open office hours after each class. As a result, my class time which is scheduled to run from 6:00 PM to 9:00 PM, more typically runs closer to 10:00 PM.

It is my goal to give my students the most thorough and well rounded foundation in technical analysis possible, enabling them to move forward in both their education and career. I am pleased that over the years, the student evaluations of the course have come in consistently above the mean for both the department and the school.

Contributor(s)

Lawrence Laterza

MTA Annual Award Recipient Profi le: Ralph Nelson Elliott

The Market Technicians Association’s (MTA’s) Award Committee seeks to honor the various Annual Award recipients with this profile series describing the individual’s life and work in technical analysis.

Ralph Nelson Elliott — 1996 Annual Lifetime Achievement Award

During the last 13 years of his life, Ralph Nelson Elliott advanced a unique theory of stock market action. The Elliott Wave Principle was first revealed privately, in December, 1934, to Charles J. Collins, an investment counselor.* Elliott describes his work as “a much needed complement to Dow Theory”, according to the letter which appears in Elliott Wave Principle: Key to Market Behavior by Frost and Prechter.1

Technical Analysis Background

Market participants were introduced to Elliott Wave Theory in August 1938, in the published monograph The Wave Principle, written by R.N. Elliott with Collins’ assistance. A year later, Elliott wrote the now-famous twelve articles for the magazine  Financial World, where Elliott’s theories were presented to a larger audience. Between November 1938 and August 1945, Elliott issued interpretive letters and educational bulletins. In June 1946, his last work – Nature’s Law: The Secret of the Universe – was published.*

As an extension of Dow Theory principles, Elliott Wave Theory (EWT) provides a more specific road map for potential market movement using a quantitative approach that applies fractal concepts and the Fibonacci Series. Although EWT is more quantitative in nature, human behavior serves as its basis. The result is a form of analysis that describes market action and reaction.

Contributions

Developed Elliott Wave Theory which was introduced to the public in the late 1930’s

Elliott Wave Structure

Elliott extended different principles in Dow Theory, most notably the ebb and flow nature of the markets. Elliott Wave Theory describes a structure comprised primarily of five wave and three wave movement with wave extensions within these series. In addition to waves, Elliott identified specific patterns to further support an understanding of market behavior.

Elliott describes market structure using larger scale and smaller scale wave formations. His theory makes use of both the numbers that make up the Fibonacci Series, along with ratios formed as you move forward in the series. The latter is a more significant component of the work.

EWT practitioners understand the probability-possibility nature of Elliott’s work, which underscores the flexibility a trader must have when approaching the markets.

R.N. Elliott’s Market Analysis Publications

The Wave Principle, published monograph Financial World (magazine), 12 article series Nature’s Law: The Secret of the Universe, published monograph

Influences*

According to market historian George A. Schade, Jr., CMT, R.N. Elliott studied the work of Charles Dow from Robert Rhea’s Dow Theory text. He continued gaining perspective through Rhea’s stock market service.

Since Elliott began his career in market analysis later in life, his previous business experiences undoubtedly influenced him. He spent more than a third of his life in Mexico working for different companies and also worked extensively with Latin American companies. Once back in the United States Elliott traveled to Canada and Europe as a business consultant in corporate restructuring.

Abridged Biography

Ralph N. Elliott’s Wall Street career is known. But what was Elliott’s background, and what do we know of his first 64 years?

Ralph Nelson Elliott was a child of the American Western frontier and an international man. He was born on July 28, 1871, in the small community of Marysville, Kansas located in the northeastern portion of the state. However, most of Elliott’s early childhood was spent in Fairbury, Illinois, a small and prosperous farming community about 100 miles southwest of Chicago. Elliott’s father was a merchant, while his mother’s family farmed.

In early 1880, the Elliotts moved to San Antonio, Texas, where Elliott formed a lifetime love for Mexico and Latin America. He spoke Spanish, and wrote it well as some of his letters written in Guatemala in 1926 attest. His family typified the American family that migrated West living at the frontier’s dynamic economic edge. In 1891, at age 20, he moved to Mexico to work on the railroads at the height of North America’s railroad boom. He lived in Mexico for the next 25 years. In youth, he worked as a lineman, train dispatcher, stenographer, telegraph operator and station agent. Later, he was employed in a variety of railroad executive positions, primarily in accounting and business reorganization.

During this time Elliott wedded Mary Elizabeth Fitzpatrick to whom he was married for 38 years. By early 1920, the couple moved to New York City, where they remained until late 1924. While here, Elliott worked in corporate restructurings and established a consulting business. He traveled to Canada, England, France and Germany.

Elliott continued his business consulting practice while living in Los Angeles and then once again in New York. He now also included “private investments” as part of his services. His health, however, was deteriorating. By 1929, he was physically debilitated and was forced into unwanted retirement.

His sharp and energetic mind would not rest. His stock market studies began. He read Robert Rhea’s book, Dow Theory, and was one of the first subscribers to Rhea’s stock market service. Elliott’s observations led not only to the discovery of the Elliott Wave Principle but, at age 64, to a career in Wall Street, to what he later referred to as “Wave number five of my own life.”

The Elliott Wave Principle is described in Robert R. Prechter’s R. N. Elliott’s Masterworks: The Definitive Collection, New Classics Library (1994).

Elliott died on January 15, 1948, in King’s Park, New York, after 77 years of a very productive life. The New York Times published his obituary. The entire biography was taken from the more complete R.N. Elliott biography written by market historian George A. Schade, Jr., CMT. Other references to this document also appear in earlier portions of the article.

George A. Schade, Jr., CMT, has been researching Ralph N. Elliott’s biography since 1991 and contributed new research to Elliott’s biography published in Robert R. Prechter’s R.N. Elliott’s Masterworks: The Defi nitive Collection. Mr. Schade acknowledges Mr. Robert Prechter’s invaluable assistance throughout the project.

References

1. Frost, A.J. & Prechter, R.R. (2000). Elliott Wave Principle: Key to Market Behavior (p. 14). Gainesville: GA. New Classics Library

*Schade, G.A., CMT (March 2008). R.N. Elliott – The years before the discovery. (pending publication).

Contributor(s)

Intermarket Cycles and the World in 2012

Editor’s note: This has been adapted from previously published client research. Some figures have numbers embedded in the graphics which do not match the caption number. We apologize for the confusion.

Intermarket cycles can change the way we look at asset cycles. This article, starting from 30 year mirroring of commodity and equity cycles, inflation and deflation cycles, this article also redefines Sam Stovall’s sector rotation structure giving new insights about the world order ahead.

It was not just investment gurus, but philosophers and painters who talked about simplicity being solved complexity and a very powerful investment approach. We also saw a Nobel Prize awarded to a simple thought that there is no economics without psychology. Sentiment comes before rationality and human beings are nice and dumb, not profit maximizing smart souls. Crowds are accordingly involved primarily with instincts, biological drives, compulsive behavior and emotions. Hence market success is less about economics than it is about psychological competence.

Many experiments regarding crowd behavior also prove that an individual transforms into another being when he becomes a part of the crowd, so much so that he completely abandons logical and rational thought. He does not think as much as herds. Solomon Asch, a Harvard professor, conducted the matching lines experiment (Fig 1).

Figure 1

Individuals and groups were asked to match the length of a line with three other lines. Individuals in isolation made a mistake less than 1% of the time. However, when placed in a group that had been instructed beforehand to claim the mismatched lines were actually the same, 75% of participants agreed with the majority. This was true even when the actual difference between the lines was very significant. Participants lacked the nerve to disagree with the majority.

Another interesting experiment demonstrating the lack of rational thought was a group exercise conducted by Stanley Milgram of Yale. Individuals were ordered to inflict pain on an innocent victim (who was acting) in the interests of an important cause. More than 60% of the subjects were prepared to obey instructions and administer the highest and most lethal dose of electricity, even after the victim was, to all intents and purposes comatose.

If pain infliction seems too different from the stock markets, we have numerous mathematicians and scientists proving the same simplicity mathematically. It was George  Kingsley Zipf, an early twentieth century scientist who revolutionized our understanding of power law and revealed their astonishing presence throughout society and nature. Zipf’s law states that the most common word used in language is a constant factor (say two times) more common than the second most common, and the second most common word is twice as common as the third etc.

In 1955 Herbert Simon sought to unify the observations of Zipf and others by formulating a single common explanatory model for many of the systems displaying power-law behavior, including language, population and wealth. Stock markets around the world also work on a power law. In 2003 in a paper submitted to Econophysics, Kaushik Matia and I illustrated that Indian market price fluctuations exhibited an intermediate form between Power Law and Gaussian behavior. This aberration also did not last long when Sitabhra Sinha, re examined the prices in May 2006 finding the price fluctuations exhibiting a power law.

Another interesting aspect was that of Fractal geometry, published in 1977 when Benoit Mandelbrot proved that Fractal Geometry was mathematical. His work extended the area of late nineteenth century mathematicians like Giuseppe Peano, who demonstrated the completed inadequacy of the common idea of dimension. The subject of fractal geometry can not only calculate coastline lengths but is used in seismology and a host of other scientific applications. It is scientifically proven that rugged fractals are more efficient in saving coastlines than concrete walls. The fractal nature of the web is also behind Google’s success.

The worldwide web is in the form of a bow tie with four components: a core, inbound links, outgoing links and the disconnected pages. Any way you slice the web – geographically, topic specific clusters, organizationally or into groups of pages owned by the same person – the bow tie shape emerges again and again. This is the same fractal behavior in nature, societies and price behavior that connects all of us. This is also a reason why Elliott’s wave principle and Dow’s theory have survived more than 125 years. 

Fractal self affinity, as Elliott said, is fundamental to nature and all human activity leads to a socionomic process. It follows a law, repeats in time, has a definite number and pattern (Fig 2) and covers areas as diverse as Gold prices, population movement, prices of seats in stock exchanges, patent applications, commodity prices, epidemics, real estate, politics and the pursuit of pleasure.

Figure 2

All of this fractaled nature of Economics, nature and universe has a fixed periodicity linked with it, i.e. there are cycles running through them. Edward R Dewey started the Foundation of Cycles early 1940’s when he realized the uncanny similarity in  data cycles found by Hyde Clark in business activity, Benner in industrial prices and Seton in animal population cycles. Idealized cycles are shown in Figure 3

Figure 3

The 11 year sunspot cycles is linked with human excitability and stock markets. There is a nine year cycle linked with credit, and interestingly the rise of deposits every nine years is inversely linked to the rise in people going to church. Other research has identifi ed a 25 year volatility cycles (Fig 4), a 17 year international battle cycles, and dividend cyclicality. Interest rate cyclicality is a reality that flies in the face of believed truth that the central banker is in charge.

Figure 4

The current linear research model (Fig 5) fails to assume the cyclicality of the market. The model works on an assumption of an economic growth that leads the positive news which leads to price growth and prosperity. Robert Prechter’s socionomic model was the first to illustrate the social mood cyclicality.

Figure 5

The alternative research cycle (Fig 6) considers the social mood at the start of all human action and activity. A positive mood reflects in productivity and creation which reflects in markets and finally confirms as the economic cycle turns up. The positive economic cycle creates positive news and hence the self feeding feedback loops. On the other hand with a negative social mood, it’s the negative feedback loop which works.

Figure 6

Sam Stovall, chief global strategist, S&P recently spoke to the MTA (Market Technicians Association). He was wondering why he was invited as S&P already had a few CMTs. The reason Sam was invited was because his sector rotation structure (Fig 7) are the first steps of a fundamental thought towards cyclicality, which is a technician’s domain. As we move ahead the thick line between fundamental and technical starts to erode. John Palicka’s fusion analysis is another attempt to bridge the two subjects.

Figure 7

Stovall’s sector rotation identified five economic cycle stages in the market – early expansion, middle expansion, late expansion, early contraction, and late contraction. The market sectors move within these five stages. Technology and transportation perform well in the early expansion; capital goods in the middle expansion stage; basic materials, energy, and consumer staples in the late expansion stage; utilities in the early contraction stage; and financials and consumer cyclicals in the late contraction stage.

The father of intermarket analysis John Murphy, defines four broad asset classed of bonds, currencies, equities, and commodities and their linkages. He details how bonds leads equity markets and how commodity upcylces are the inverse of equity cycles.

Just like all other fractals, a link runs through market cycles which Tony Plummer writes about in his book on forecasting  financial markets. Plummer classifies the broad economic cycles into TRIADS with base, trend and terminal cycles. Cycles are seen from low to low and not from high to high, as bullish market bias extends markets longer pushing the cycle top ahead of the symmetrical high. Bear markets correct faster. This is why the bear cycle tops are translated (biased) towards the left and are likely to occur before the symmetrical cycle tops. Plummer identifies behavioral traits linked with the respective base, trend and terminal cycles. Base cycles are characteristic of indolence, recuperation and rejection. Trend cycles are  characteristic of confidence, change and deception. Fear, resistance and accusation are characteristic of final terminal cycles. All these cycles have a three wave pattern labeled as 1-2-3 up and a-b-c down (Fig 3).

These triads build the economic cycles and exhibit a power laws behavior. Kitchen cycles (Fig 8) last from 3 to 5 years and on average are 3.33 years long. These are known as inventory cycles and are close to the well-known US presidential cycles. These are the only cycles conventional economists believe to work. Next comes the Juglar cycle, which lasts for 7 to 11 years and average around 10 years, known popularly as the decade cycles. Juglar cycles, which follow the capital investment cycles, are three times the length of Kitchen cycles. Then next power law that is three times Juglar cycles take us to Berry cycles also known as the infrastructure cycles. Berry cycles last for 25 to 30 years, averaging around 30 years.

We have seen 25 year cycles in commodities, gold and silver ratio, and volatility. A step ahead on power law takes us to the Strauss and Howe cycles (crisis cycles) of 90 to 99 years. Plummer makes an interesting observation about Kondratieff cycles while classifying the triads from 3.33 years to 90 years. Kondratieff is not an economic cycle as Kondratieff saw prices rising and falling in long waves. Not all Kondratieff lows are major depressions because not all Strauss and Howe lows will coincide with a Kondratieff low.

Figure 8

Strauss and Howe crisis alternate between deflation and accelerating inflation. This is why Plummer believes we have finished the deflation Strauss and Howe metacycle in 1946 and now we are in the inflationary cycles that should push until 2030 marked by a possible world war. With a rough calculation and one can see that India’s first war of independence and US civil war of 1857 had an uncanny similarity. This, we at Orpheus believe, was the second 90 year metacycle, the first starting somewhere near 1720, coinciding with the start of capitalism. Hence we are indeed in the revolution cycle which ends the 270-300 years of economic activity near 2030. We are not sure how inflationary things may get or whether we are indeed heading for hyperinflation and destruction of real money, the indicators more indicate at the latter than the former. The intermediate pause before the last 30 year cycle starts should be around 2012-2014.

This should be marked by economic growth accompanied by continued rise in commodity prices – Gold at $3,000 and Oil potentially much higher. New sectors to watch should be the alternative energy and biotech until 2020 and beyond.

Intermarket cyclicality is a subject coined by us at Orpheus. This subject not only redefines Stovall’s sector rotation, but it also attempts to extend market cyclicality from an intermarket perspective. Since cycles work on Triads, Sam’s sector rotation sectors can be reclassified in three broad sectors – early economic, mid economic and late economic cycle sector. Early economic is led by Financials, Information Technology and Discretionary; mid economic by industrials; and late economic consisting of Energy, Materials, Staples and Utilities. As we head into the terminal Kitchen cycle and Juglar cycle low in 2010-2012 the late economic cycle sectors should outperform the market.

This means energy, utilities, staples and materials sector stocks should have more upside left. According to intermarket cycles, the 30 year Berry cycles is linked to equity cycles. That means equity markets grow and decay in about 25-30 years cycles. This is what the gold-silver ratio (the metals maze) and volatility cycles highlight (25 yr cyclicality). This 30 year equity cycle is inverse of the 30 year commodity cycles. This means when equity rises, commodities fall and vice versa. 1975-80 was a commodity market top and an end of equity bear market in US.

And 2000 was an equity market top in US and a start of a commodity boom. The current commodity boom should end in the 2024-2030 (Strauss and Hauss) metacycle low with potential highs in the 2012-2015 time windows. It is in this time frame equity markets should make the decade low. Intermarket cycles can extend the cycle explanations to regional allocations between Asia and the west, between inflation and deflation and between interest rate and yield cycles.

Sector rotation remains a key intermarket strategy for portfolio allocation but in terms of early economic, mid economic and late economic instead of what is classified by Stovall’s five stage economic cycle approach. Commodity and equity intermarket analysis also suggests that materials, metals, chemicals, staples and pharmaceuticals could be defensive and relative performers. Intermarket cyclicality can also help time moves in and out of large and small cap sector stocks. The subject can also help create low correlation combination portfolios to better balance the overall portfolio return to risk profile.

The current multi decade cycle is inflationary and should see rising interest rates. Many emerging markets are between the late expansion and early contraction stage. This means that the sectors which will outperform are energy, staples, materials and utilities. This should happen for at least a primary (more than 9 months) time frame. The late economic sector cycle is in sync with the ongoing 30 year commodity cycle (Fig 9), which started in 1998-2000 and should top in 2012-2015.

Figure 9

This is another reason why food, material and commodity prices will continue to rise. However, we should not forget that this commodity cycle is a 1-2-3 structure up. And we have had no retracement of primary degree as of the time of this analysis. This is the very reason a sharp retracement is pending before the CRB (Reuters Commodity Index) regroups again and heads higher. The real depression activities are likely to start then. The world in 2012 will be a stranger place with the lower billion people of the world struggling for food and the top billion still getting richer. The hedge funds (the ones that survive) which will create news will likely be the ones doing Long Water – Short Oil strategies. The world beyond 2012 will be ruled by global macro funds; market psychology will gain more prominence; fractal forecasting might be taught at Yale. Cycles will get their place in statistics as the relentless cyclical change pushes ahead. And all this will happen while we wonder about the randomness of the world we live in, unaware of the simple structures that got us so far. 

Contributor(s)

Mukul Pal

Mukul Pal, a technical analyst who holds the Chartered Market Technician (CMT) designation, is the founder of AlphaBlock, a technology group focused on bringing the predictive mapping characteristics of AI to the market mechanisms that use blockchain to create an adaptive “intelligent...

Technical Analysis Warned of Indian Equity Market Crash

S&P CNX Nifty is a well diversified 50 stock index accounting for 21 sectors of the economy. S&P CNX Nifty is owned and managed by India Index Services and Products Ltd.

This year has seen turbulence in equity markets worldwide. NSE Nifty, representing the Indian equity market was no different. The period shown below represents times when sentiment moved from extreme optimism to extreme pessimism in a very short time. The fall in the Nifty of more than 21% in only 5 trading days was one of the effects of extreme greed as the market was reaching new highs even as global scenarios were weakening. The magnitude of the fall may not have been easily predictable, but indicators did give signals of weakness when a new top was being formed in mid-January.

From November to mid-January, when Mr. Market was busy going to unexplored regions, different indicators gave very weak signals.

  • On Balance Volume formed lower tops and lower bottoms except for the final phase of rally. This was a negative divergence from October 2007 to mid-December 2007, clearly giving a caution sign.
  • MACD gave a very strong negative divergence making lower tops and also failed to sustain strength above the signal line.
  • RSI failed to provide any supporting evidence to the rally.

All this indicated strong weakness in the market structure and price confirmation was obtained when the index closed below its 50-day EMA, which had provided good support, after which the Nifty crashed over the next 2 days. There was a dead cat bounce after the crash and the index slowly slid again to test the bottom it made on January 22, 2008. This bounce provided a good opportunity to sell.

From March to April we can see that the Nifty slowly started bottoming and formed a small inverted Head and Shoulder pattern with the top  of Head at 4500. There were bullish signals in several indicators.

  • On Balance Volume gave a strong positive divergence and formed an uptrend during the formation of the inverted H&S,  indicating that the volume was increasing on rises and decreasing on falls.
  • MACD completed a positive crossover above its signal line during the formation of the right shoulder and crossed above the zero level on the price breakout.
  • RSI also formed higher tops and higher bottoms during the formation of the inverted H&S.

The strong bullishness of the indicators was confirmed when the inverted H&S pattern was completed and prices decisively broke above the neckline at around 4850 in the middle of April. The Nifty achieved the measuring implication of the inverted H&S at 5200. After achieving the minimum measured target, a throwback to the neckline weakened the technical situation and the other indicators added to the bearish bias. A short term sell signal will be given if prices break below the neckline, which will then act as strong resistance.

We can see here that even by using a few indicators judiciously along with price confirmation can be helpful in timing the market, especially at times when Mr. Market does not pay much heed to intuitive or gut feelings.

Contributor(s)

Ashish Kyal, CMT

Ashish Kyal, is the Founder of Waves Strategy Advisors and Ashish Kyal Trading Gurukul. He is the Author of Effective Trading in Financial Markets using Technical Analysis.  His articles have appeared in newsletters of CMT Association, International Federation of Technical Analysts (IFTA...