TECHNICAL A N A LY S I S JOURNAL AU T U M N WINTER 2017
Volume Five Issue 2 2017
The Swiss Association of Market Technicians ZÜRICH • GENEVA • LUGANO • CHUR
2 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
From the President’s Desk
Dear SAMT Members & Industry Colleagues, It was a shock for the industry to hear the sad news, that a veteran of technical analysis has passed away. With Hank Pruden, we lost a friend and a wise technician at the same time. We have a tribute to SAMT Honorary Member Hank Pruden on page 6. This news overshadowed the annual IFTA conference in Milano, Italy which took place in late October. Read about this great industry conference on page 9. At the same time, I would like to remind you, if you missed this chance and would like to participate in the next conference, I am happy to share the news that the 2018 conference will take place in Kuala Lumpur, Malaysia! Great analysts who were speakers at the IFTA conference in Milano appear in this edition: Perry Kaufman (page 12), Robert Prechter (page 19) and Alberto Vivanti (page 27). On page 30, SAMT’s Vice President Mario Guffanti reviews the Annual Lantern Fund Forum in Lugano. Bitcoin and cryptocurrencies were one hot topic there. Make sure you don’t miss this great review! SAMT Vice President, Ron William, interviewed Dr. Van Tharp at the VTI workshop in London in October on page 35. Finally, Phil Roth contributed a great piece of work about Investors vs. Traders showing how the attitudes and activities of stock market participants aid forecasting, see page 41. If you would like to participate in local Swiss events, get to know great speakers, exchange information and tactics with colleagues from the industry, check out the information on our dedicated webpage section. Our best wishes for the holiday season and the new year. Sincerely Yours,
Patrick Patrick Pfister, CFTe President of the Swiss Association of Market Technicians (SAMT)
The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 3
THE SWISS TECHNICAL
A TRIBUTE TO SAMT HONORARY MEMBER Hank Pruden, PhD
Volume Five • Issue 2
AUTUMN- WINTER 2017
THE IFTA 30TH ANNUAL CONFERENCE IN MILAN: Sailing to the Future Mario V. Guffanti 9
Mario V. Guffanti, CFTe Managing Editor +39-333-1420142 email@example.com
PORTFOLIO RISK IN UNCERTAIN TIMES Defense Stocks Required Perry Kaufman 12
Ron William, CMT, MSTA +44 7857 245 424 firstname.lastname@example.org Design & Production Barbara Gomperts +1 978 745 5944 (USA) email@example.com
A Baker’s-Dozen Questions on Socionomics for Robert Prechter Ron William
A KEY THEME AT THE 30TH IFTA CONFERENCE Trend Following in Portfolio Management Alberto Vivanti 27
Follow SAMT on
REVIEW: THE 7TH ANNUAL LANTERN FUND FORUM, LUGANO Mario Valentino Guffanti, CFTe
4 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
AN INTERVIEW WITH DR. VAN THARP, Founder of the Van Tharp Institute Ron William, CMT, MSTA 35
INVESTORS MAKE BOTTOMS; TRADERS MAKE TOPS: An Analysis of Technical Indicators that Show How the Attitudes and Activities of Stock Market Participants Aid Forecasting Philip J. Roth, CMT
THE SWISS ASSOCIATION OF MARKET TECHNICIANS SAMT Speaker Series Q3 2017
Board of Directors
Swiss Journal of Technical Analysis
IFTA’s CFTe Certification Program
The Swiss Association of Market Technicians ZÜRICH • GENEVA • LUGANO • CHUR
To read articles on issuu.com from the past five years of The Swiss Journal of Technical Analysis, click here.
The Swiss Association of Market Technicians (SAMT) is a not-for-profit organization that does not hold a Swiss Financial Services License. It is the aim of the SAMT to promote the theory and practice of technical analysis, and to assist members in becoming more knowledgeable and competent technical analysts, through meetings and encouraging the interchange of materials, ideas and information. In furthering its aims the SAMT offers general material and information through its publications and other media. The information provided on this Journal has been compiled for your convenience and made available for general personal use only. SAMT makes no warranties implied or expressly, as to the accuracy or completeness of any information contained on the Journal. The SAMT directors, affiliates, officers, employees, agents, contractors, successors and assigns, will not accept any liability for any loss, damage or other injury resulting from its use. SAMT does not accept any liability for any investment decisions made on the basis of this information, nor any errors or omissions on the Journal. This Journal does not constitute financial advice and should not be taken as such. SAMT urges you to obtain professional advice before proceeding with any investment. The material may include views and statements of third parties, which do not necessarily reflect the views of the SAMT. Information on this Journal is maintained by the people and organization to which it relates. The SAMT believes that the material contained on this Journal is based on the information from sources that are considered reliable. Although all care has been taken to ensure the material contained on this Journal is based on sources considered reliable we take no responsibility for the relevance and accuracy of this information. Before relying or acting on the material, users should independently verify its accuracy, currency, completeness and relevance for their purposes.
The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 5
A Tribute to SAMT Honorary Member, Hank Pruden, PhD Barbara Gomperts On August 26, the world lost Hank Pruden. He was a shining light in the field of technical analysis, to his students at Golden Gate University and to his many colleagues throughout the world – and, he was my dear friend.
I can’t remember when I met Hank. We really got to know each other when we worked on 11 issues of the MTA Journal — he as editor and me as the ‘production department.’ In the spring of 2013, I asked if he would like to contribute an article to this new publication, The Swiss Technical Analysis Journal. He did and continued to contribute an article to every issue through Spring 2017.
Our shared passion was not technical analysis, journals or Wyckoff, but France and all things French. Our last conversation was about whether he and Sarah should have lunch or dinner at Le Jules Verne in the Eiffel Tower — he was planning their visit to Paris after the IFTA conference. Hank was a ‘foodie’. Planning the restaurants was more important than booking the flight. When I had the idea to do a tribute in this Journal, I was amazed to see how many lives Hank had touched all over the world. Even the November issue of TA of Stocks & Commodities magazine printed a tribute to Hank and reprinted one of his articles. As I collected these tributes, I cried and laughed as I read them. That smile and laugh of his was infectious and drew everyone in. At the end of this article there are print and video interviews with Hank – listen if you can – as they will remind you just how terrific he was. — Barbara Gomperts, SAMT Social Media Manager
I can still recall first meeting Hank Pruden. In all the years I knew him, his enthusiasm was infectious, his generosity full and his support unwavering. Hank was equally committed to the Technical Securities Analysts Association of San Francisco, the MTA and IFTA. Personally gratifying was Hank’s steadfast inclusion of pattern analysis under the TA banner. We had planned to get together with our wives in Milan at the IFTA conference this year, but alas it is not to be. — Robert Prechter, Elliott Wave International, USA gh Each year as the annual IFTA conference approached I would look forward to having dinner with Hank Pruden and, if we were lucky, with his lovely wife Sarah. This year there was no dinner; Hank had moved on to the great charting room in the sky. Hank pioneered the teaching of technical analysis at the university level and was the expert on the work of Richard D. Wyckoff, but most of all Hank was simply a good human being and a good friend and I will miss him dearly. — John Bollinger, Bollinger Bands, USA
March 2015. Sarah and Hank at the Umbria restaurant near Golden Gate University in San Francisco. It was a favorite spot of Hank’s. The owner called him Il Professore.
I had the honor and the pleasure to meet Hank Pruden in 2006 when I chaired the 19th IFTA Conference in Lugano. Hank was among the speakers, his presentation was intriguing, but what impressed me most was his vivacity as a moderator at the roundtable with a group of well know analysts discussing the markets perspectives. He was an anchorman! A great analyst and teacher, a friend. We are missing him. – Alberto Vivanti, SAMT Board gh Hank Pruden will be dearly missed, both as a true champion of our industry and a heart-felt friend. Thank you Hank for being such an inspiration to so many people around the world. May future generations honour this great heritage that you have contributed so much to, and may we all shine a light so bright for you to smile back on. I will always be grateful for your passion and support. — Ron William, SAMT & IFTA Boards gh Hank was one of the most delightful people that I have encountered both within the realm of Technical Analysis and outside it. At IFTA conferences he always greeted me so warmly and I remember at a dinner one year he observed that I was by myself so he invited me to join his table and enveloped in his warmth and generosity. In my role as IFTA journal editor I published several of his Wyckoff articles and it was always a pleasure to work with him. I will surely miss his smiling face. — Regina Professional Sydney
6 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
Meani, Australian Technical Analysts,
I first met Hank at a cocktail party after a BCA Research conference in May of 1975 and took an instant delight to this incredibly clever and kindly man. To say that Hank had an important influence on my life is to understate it. He was later to invite me to speak at a TSAASF meeting, where I met future partner Joe Turner. Later, we formed Pring Turner Capital with Bruce Fraser.
There are many branches in technical analysis, and Hank was the “go to” or expert on the Wyckoff Method. There are many ways to contribute to our craft. Hank’s path was through teaching at Golden Gate University and speaking at conferences. He also worked invisibly and tirelessly on many IFTA committees in various positions. He was always deeply involved with the TSAA-SF throughout the many decades I knew him. Hank was also the “go to” person at those after hours get togethers, where his insightful analysis never failed to stimulate. Above all, I remember him for his human characteristics, tremendous personal support and incredible humor. To say that Hank will be sorely missed is to misspeak, he already is. God Bless you, Hank. — Martin Pring, mpring.com, USA (above left)
Once you met Prof. Henry Pruden, you would first remember his great laugh radiating throughout the room. Prof. Pruden was among the first TA teaching professor and I remember the IFTA conference in San Francisco in 1995, when we started to get to know him better. Thereafter, we met regularly each year at the IFTA conferences for the next 15 years and at several MTA conferences, like the MIT conference organized by Prof. Andy Lo in 2005 in Boston.
Besides teaching in San Francisco, Hank was also a great traveler, visiting many capitals in Europe, and even spent a sabbatical year teaching in Paris and the south of France. In 2003, he and Martin Pring enthusiastically supported my idea to organize a conference in Sao Paulo to initiate a new Brazil technical analysis society. Testimony is in this wonderful photo of the three of us seated for lunch at the Bovespa after our speeches, and I can still hear him laughing in the photo. Later he visited Switzerland several times, and was always ready to speak to our Swiss TA association. In 2014 I had the privilege to hand him an honorary membership to SAMT. Prof. Henry Pruden was not only a fine technical analyst veteran, ready to share his knowledge with fellow analysts, he was a great gentleman and a great companion to have around the bar! — Bruno Estier, Bruno Estier Strategic Technicals, Geneva (above right)
gh Hank Pruden, who I called “Henry” much to his annoyance, was a good friend over the many years I knew him. I’ve known him for so long I can’t remember when we first met. He had published some interesting work on Wyckoff and was an ardent promotor, advocate and fan of Wyckoff’s methods. What I liked about him was that he avoided the pomposity of the academic world, had a great sense of humor, and was just plain nice. My last memory of him was a lecture he gave at an MTA Symposium in New York. While we had conversed often in his capacity as a leader of the technical world, I had never heard him speak except to get rewards, honors, and prizes, but this instance was a work of art. His presentation was clever, light but serious, and almost an Oscar worthy performance that played with the audience and I hope taught them something about technical analysis. I shall miss him greatly. — Charles Kirkpatrick, Kirkpatrick and Company, USA
gh Hank was a unique personality. He inspired us in 2006 to set up a course in Technical Analysis and Algorithmic Trading at Uppsala University in Sweden. Hank’s joy of life, brilliance in technical analysis and investor behavior – and his love for his family and friends are things that I will always remember. — Max von Liechtenstein, Former Chairman of STAF, Sweden The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 7
This content was originally published on StockCharts.com and is reprinted with their permission. Bruce Fraser has also given his permission to reprint this tribute to his dear friend, Hank Pruden.
Wyckoff Power Charting
Dr. Henry O. (Hank) Pruden 1936-2017 Bruce Fraser | September 12, 2017 at 06:40 PM Technical analysis education suffered a great loss with the recent passing of Dr. Henry O. (Hank) Pruden. A consummate educator, in 1976, Hank combined his incredible capacity for inspiring students with his personal passion for Technical Market Analysis. The result was the very first graduate course, at an accredited university, in the study of markets using chart analysis. Dr. Zahn, Dean of the Business School at Golden Gate University in San Francisco, said ‘Let the Market Decide’ when Hank proposed this innovative new graduate course. Hank’s electric teaching style and unique curriculum made this class an instant hit. The classroom would fill up every semester and students would take the class again and again. In 1987 Hank took a sabbatical, recharged his batteries, and came back stronger than ever. Hank and I collaborated on the creation of a new class based on the Wyckoff Method (which we team taught). Hank then developed an entire Technical Analysis Certification Program. Students could earn a certificate in Technical Market Analysis or take these courses in conjunction with their M.S. degree.
Dr. Pruden was a prolific writer publishing many papers on technical analysis and peak performance in trading. We have linked to a number of his articles here and hope to make more of them available in the future. In 2007, he published his seminal work “The Three Skills of Top Trading” (Wiley). In it we discover Dr. Pruden’s view of the essential qualities of the consistently successful trader. Dr. Pruden designed the curriculum of the courses at GGU to reflect this world view. In his personal development as a complete trader he came to understand that trading success depends on being competent in these three skills of top trading. His goal was to have every GGU graduate be capable in these important skills. And to have every reader of his book be on the path to trading mastery. His mission was to have every student become the complete trader.
Dr. Pruden is one of those rare people who touched the lives of many in a most personal and positive way. To honor his life and commitment to others, let’s step forward and strive to be the very best complete trader possible. Hank believed we
were serving others by following our passions and doing our very best work. Hank showed us how to touch the lives of others through the metaphor of trading mastery. Celebrate Hank’s life by viewing these two videos:
View a brief bio of Dr. Pruden’s life here. This video was shown at the 2013 International Federation of Technical Analysts (IFTA) Annual Conference in San Francisco when Dr. Pruden received the IFTA Lifetime Achievement Award. (click link)
Dr. Pruden’s last appearance was a video presentation at the Best of Wyckoff 2017 conference in August. The title of the talk is: Using P&F Charts to Find the Present Position & Forecast the Probable Future Trend of U.S. Equity Prices: Applications of the “Wyckoff Law of Cause and Effect”. Thank you to Roman Bogomazov at wyckoffanalytics.com for generously making this video available for all to see. (click link). An accomplished “Wyckoffian”, Bruce Fraser has been teaching graduate level courses on Technical Analysis at Golden Gate University since the early 1990s, where he has been instrumental in crafting the curriculum. At Golden Gate University, Bruce’s teaching has focused largely on Wyckoff Analysis
SAMT and Swiss TV Interviews
SAMT interviews Hank November 2013
Dukascopy TV interview 17 June 2014
Interview with Ron William on Dukascopy TV, 25 June 2014
8 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
The IFTA 30th Annual Conference in Milan: Sailing to the Future Mario V. Guffanti The 30th annual IFTA conference, held this past October, returned to Italy after nine years. While in 1998 the conference was held in Rome, this year the venue was the Excelsior Hotel Gallia in Milan, with a first day kick off in the Milan Stock Exchange building. In our Spring issue we interviewed Francesco Caruso, SIAT Vice President and organizer, who together with the SIAT President Davide Bulgarelli and a good number of SIAT members contributed to the complex organization of this annual event.
Francesco Caruso and Davide Bulgarelli
In our interview Francesco said that the title of the conference, “Sailing to the Future”, allowed them to go beyond the usual themes of Technical Analysis, exploring a sea of opportunities originated by a totally new “quant” generation of technology, markets and instruments. One of the goals was to try to go inside the vast theme of the collaboration between Technical Analysis and other fields of economics (i.e. Behavioural Finance, AI, Big Data, Cryptocurrencies), with top institutional and academic contributions. I attended the conference and I can certainly say that the objectives were achieved.
For three days, 41 speeches were presented in the conference room of the Hotel Gallia. The forty second speaker, Hank Pruden, was remembered by Davide Bulgarelli during the event.
I certainly cannot detail all the interesting speeches that took place, but a brief talk about the main topics with some speakers can be done. About half of the speakers were Italian, and the speeches presented were on well diversified and original topics.
Several speakers - L-R: Mohamed Ashraf, Maurizio Mazziero, IFTA President, Mohamed El Saiid, John Bollinger, Gregor Bauer, Francesco Caruso, Luca Giusti, Gideon Lapian (Chair AATI); kneeling: Eugenio Sartorelli and Giovanni Trombetta)
Among the big American names were John Bollinger, who presented a personal history of technical analysis focusing on the contributions of the individuals that he found most useful in his investment process; Perry Kaufman, showed an interesting method that trades selected Dow components, being more profitable than any of the broad indices, and a short-term market approach applied to the future markets; Robert Prechter, examined people’s natural tendency to extrapolate social trends linearly and showed an alternative fractal model.
On the Quant side we had Kathryn Kaminsky, who spoke about the convergence of Technical Analysis and Quantitative methods, and Prof. Spyros Skouras, who discussed the distinction between technical analysis and quant trading, and the key insights that the first discipline brought to quant community. One theme I have frequently heard was that of trend following techniques: our SAMT Vice President, Alberto Vivanti, and others have spoken on this subject. L-R: Dan Valcu, Spyros Skouras and Perry Kaufman
Also a highly topical issue, the crypto-currencies, was dealt both with individual speeches, such as that of Prof. Ametrano of
The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 9
Politecnico di Milano, and with a round table discussion, with Davide Bulgarelli, Davide Capoti and Matteo Maggioni.
Other subjects were about new indicators (Francesco Caruso presented a new technical indicator, the Composite Momentum, subject of his article in our Spring Journal), Algos, Neural Networks, Artificial Intelligence, Statistical Indicators, Fractal Dynamics, Bifurcation Theory, Market Psychology, Commodity Models, Options, Technical Analysis and macroeconomic trends and new charting techniques. In a room contiguous to that of the conference, two university professors, Eric Guerci and Nobuyuki Hanaki (Université Côte d’Azur, France), conducted a collection of data through two tests that the conference participants could do on computers. It was a work on Experimental finance, an emerging field of academic research that analyse data collected under a controlled experimental setting and try to provide new quantitative insights to questions related to the field of finance.
L-R: Alberto Vivanti, Robert Prechter, Mario V. Guffanti and Ron William)
The conference was closed by the speech of Nazri Khan Adam Khan, about the latest megatrend in trading world. Dr. Nazri Khan is the chairman of the next IFTA conference that will be held next year in Kuala Lumpur.
At the Italian conference he was accompanied by a large delegation of 25 of his fellow countrymen who began to introduce us to their fascinating country where the next conference will be held.
While waiting to fly to Malaysia for the next IFTA conference, I propose you read articles by three of the speakers from the Milanese conference. Perry Kaufman shows us a new model built around defence securities, which allows us to have better control of portfolio risk. Robert Prechter, is interviewed by our Ron William on his Socionomics work. Alberto Vivanti tells us about the following trend techniques in portfolio management.
John Bollinger and Kathryn Kaminski
SAMT members: Samir Jalaleddine and Nico Büchel
Click to watch a memorable video that recaps the Milan Conference.
10 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
Dr. Nazri Khan, speaker and Chairman of the next IFTA Kuala Lumpur conference
SAMT & SIAT members (L-R): Salvatore De Michele, Mario V. Guffanti, Riccardo Ronco, Alberto Vivanti, Andrea Unger and Ron William
The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 11
Portfolio Risk in Uncertain Times
Defense Stocks Required Perry Kaufman We all know that putting uncorrelated stocks, or uncorrelated assets into a client’s portfolio will reduce risk and limit drawdowns. But what does “uncorrelated” really mean, and how much does it help? Better to know now than be surprised in the future.
Think about two men, or women, each flipping a coin. The results are certainly uncorrelated. Statistics show that there is a 25% chance one will show heads and the other tails, and another 25% that the opposite person will show tails and heads. That leaves 25% when both show heads and 25% when both show tails. If we think about it in terms of stock prices, prices will move the same direction 50% of the time, even if those stocks have nothing whatsoever in common. But of course, they do have something in common – they are driven by the economic health of the country. At a minimum, we should expect any two stocks, picked arbitrarily, to move in the same direction more than 50%. Let’s look at some examples. When we construct a stock portfolio, it is traditional that we look for those companies that offer diversification, that is, stocks that don’t move the same way at the same time. We typically measure that using correlations.
Business as Usual
We’ll start by assuming that more diversification is better, and that the simplest way to find the relationship between any two stocks, or a stock and the broad index, is to find their correlation. This is a simple in Excel, but remember that correlations use the daily returns, not the prices.
The correlation, which ranges from +1 to -1, tells us how similar the movement of one stock is to another. A value of +1 means it’s identical, -1 that it’s exactly the reverse. Those cases don’t happen. The value 0 says that there is no relationship between the price movement. My own assessment is +0.50 to +1.00
Very strong correlation
+0.20 to +0.50
-0.20 to +0.20
-0.20 to -0.50
Clear inverse relationship
-0.50 to -1.00
Unlikely to happen
We see strong positive correlations between interest rate vehicles, such as a 2-year and 5-year Treasury note. We also see it when there is a crisis, and investors all redeem their funds at the same time. It’s the money that moves the market. As an example, let’s look at four different stocks, General Electric (GE), Bank of America (BAC), Merck (MRK), and Amazon (AMZN), each quite different. Table 1 shows the correlations against each other and against SPY (the SPDR 12 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
Table 1. Correlations from 1998.
ETF) for the 20 years beginning 1998.
The averages on the right show that the strongest correlation is against SPY, and less when one stock is compared with another. We should expect that because the SPY is an average and picks up the general direction of the market. These four stocks are included in that average.
We don’t usually think of price moves in terms of correlation, instead we think about whether stocks move up and down together. If we just look at the days stocks moved the same way, we get a much higher relationship. In Table 2, we see how stocks moved compared to GE. I think it better represents the way we think about stock movement. Table 2. Rolling correlations, against SPY, during three representative periods.
Looking at the average correlation over a long period hides a lot of information. How much did the correlation change during that time? How did it react during a crisis, such as we saw in 2008? Can we pick stocks to diversify a portfolio from the long-term correlations?
Chart 1. SPY price (top) and the correlations of the four stocks against the SPY (bottom) during July and August 2017.
During July and August 2017, the market moved slowly higher. We can call it business as usual. The dominant news was increasing tensions between the U.S. and North Korea, and Amazon buying Whole Foods and then lowering prices. SPY prices are shown at the top of Chart 1 and the rolling 20-day correlations are in the bottom panel. The rolling correlations give us a much better understanding of how price patterns change. When the SPY rises, we see the correlations increasing. This happened at the beginning of July and the end of August. During the sideways periods, the correlations move towards zero. Each stock moves according to its own fundamentals. These periods alternate, giving us the sense that we have good diversification.
Correlations During a Crisis
It’s not the ordinary market that is the problem, but the extremes. If we’re good at diversifying a portfolio, we would have been protected during the financial crisis of 2008. But we weren’t. Chart 2 shows the rolling 20day correlations of the four stocks against SPY for 2008. Notice that Merck and Amazon both had periods of low correlation, but the overwhelming picture is that correlations hovered around 0.80, showing extremely high similar movement. The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 13
Chart 2. Correlations during the 2008 financial crisis.
Price Movement in a Crisis
It may be easier to see if these occasional, low correlations meant that a stock was rallying while others were declining. In Chart 3 we can see the prices of the four stocks and SPY. Despite the drop in the correlations of Merck, the price chart shows that it declined more than the other stocks during 2008, and all four stocks decline more than the index, SPY. Then choosing stocks based on either long-term or short-term correlations did not prove to be helpful during a crisis. Table 2 shows how the average correlations varied during the three periods we’ve looked at. Chart 3. Prices of the four stocks and SPY, adjusted to 100 on January 1, 2008.
Table 2. Rolling correlations, against SPY, during three representative periods.
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How Do You Select Stocks to Protect Risk?
We’ll be forced to use common sense to create a portfolio that protects an equity investment during a crisis. But first we need to be specific about the crisis, and that is a problem. We know that the attack of 9/11 could have been protected by defense stocks and bonds (the flight to quality). The recent North Korean risk also calls for defense stocks. Then there was the financial crisis of 2008. Again, risk-free bonds would have helped. Do we need to consider anything else? An attack on our power grid? An energy shortage? An assassination? It’s going to be necessary to decide which events would cause a large move in the stock market, what stocks or other assets would protect us, and what portion of the portfolio should they represent.
The 1987 Stock Market Crash
While not the same as 1929, the stock market crash in October 1987 was the largest since the Great Depression. The Dow dropped 27.2% on the Monday and Tuesday of October 18th and 19th. At the same time, bond futures rallied (Chart 4) and gold flopped around (Chart 5).
Chart 4 (Left), Bonds rally while stocks fall. Chart 5 (right), Gold reacts as an immediate hedge, but then collapses.
It’s clear that the flight to quality means government interest rates, but gold failed after years of being touted as an important hedge.
The Internet Bubble
The next event was the internet bubble, which saw the stock market go into a 3-year decline after an incredible rally in Nasdaq stocks in the later 1990s. Chart 6 shows the S&P futures with bond futures, although the S&P did not receive the brunt of the losses. Again, we see bonds as a good hedge.
Chart 6. The Internet bubble bursts. Investors run to bonds.
The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 15
Then there was 9/11. We were all there, one way or another. Stocks plunged, bonds soared. Only the defense stocks, Lockheed-Martin (LMT), Raytheon (RTN), and Northrop-Grumman (NOC), rallied. In Chart 4, we’ve added Boeing (BA) to show that aerospace is not the same as defense. At the same time, Chart 8 shows that bonds did not offset the equity losses. The initial reaction was that yields rose, then ended the year higher (futures lower).
Chart 7. Defense stocks rally during the 9/11 terrorist attacks. Chart 8. US bonds did not provide a hedge during the 9/11 period.
The 2008 Financial Crisis
Although a bit late and a bit sloppy, bond futures finally rallied in response to the severe sell-off in late 2008. Before that, it provided a reasonable hedge as the S&P steadily declined (see Chart 9).
Chart 9. Bond futures provided a reasonable hedge during the financial crisis of 2008, although its reaction at the worse time was sloppy.
Although we’ve seen more conflicts during the past two decades, Iraq and Afghanistan seem less important in light of the tension and escalation of rhetoric between the U.S. and North Korea. The defense stocks seemed to have anticipated the crisis well before it became news. Chart 10 shows the movement of the three defense stocks and Chart 11 shows how bonds reacted.
The combination of the S&P and bonds has served well as a conservative portfolio for years. During a financial crisis or swings in the U.S. dollar, bonds have been the safe haven. During periods of strong economic activity, stocks give the best returns. During a crisis, stocks drop and bonds rise, just as we like. 16 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
But times have changed. Terrorism and geopolitical threats have become too common. Neither bonds or gold show any consistency in protecting portfolio risk. However, defense stocks have filled that gap.
To show that defense stocks are not just a stop-gap, Chart 12 shows the history of those stocks, along with SPY, adjusted to zero at the beginning of the chart. It’s clear that defense stocks are just as likely to perform well, even while having their unique hedging quality.
Chart 10. The defense stocks react to N Korean tensions. Chart 11. Bonds mostly track the S&P during N Korean missile tests.
Chart 12. History of defense stocks from 1998, compared to SPY.
Then, a combination of a broad market index, or diversified individual stocks, bond futures, and defense stocks will be the better choice. But in what proportion?
By the Numbers
Again, we’ll start by looking at a few cases. When the stock exchange reopened on 9/17/2001, the SPY closed down 4.21 points, while LMT, RTN, and NOC closed up and average of 6.03 points. We can’t do this in percent because prices of those stocks have been adjusted for splits. Based on the price moves, a portfolio of 58% SPY and 42% defense would fully hedge the crisis.
For the 2008 financial crisis we need to look at futures. Again, back-adjusting makes it impossible to use percentage returns. From July 2007 through December 2008, the S&P dropped from about 1400 to under 600, 800 points, although we know it was equivalent to about 50%. One contract at $50 per point, give a total loss of $40,000 per contract. Yoiks! At the same time, bonds rallied (more or less) from 55 to 85, settling at 77, up 22 points, equivalent to a gain of $22,000. Then a portfolio that had two contracts of bonds for each one of the S&P would have been fully hedged. If we put together the S&P, bonds, and defense stocks, we get:
27% S&P futures + 54% bond futures + 19% defense stocks = 100% investment where the S&P and bonds are futures. Bonds can vary based on maturity and The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 17
whether they are cash, futures, or ETFs. Using only S&P and bond futures, from 1987, in the proportion 27-54, we get the results in Chart 13. Note that there are no declines in 1987, and after the internet bubble. There is no decline for 9/11 and a sideways period that ends in 2003. There is volatility in 2008 but no significant drawdown. Net gains are 25% better than a portfolio of 60% stocks and 40% bonds, but more important, the risk is far lower.
Chart 13. Comparing a portfolio of 33% stocks and 66% bonds with the traditional 60% stocks and 40% bonds.
Adding 19% defense stocks to the portfolio of stocks and bonds presents an intellectual dilemma (see Chart 14). Performance is not as good as the portfolio without the defense stocks. The returns are slightly lower and the risk is slightly higher. The question becomes “Do I believe that defense stocks with be important in the future?” My vote is “yes.” If you don’t like 19%, then any added allocation to defense stocks will reduce exposure to some future price shocks. Good management is about anticipation.
The other challenge is to reverse the weighting of the industry’s 60% stocks and 40% bonds to a more conservative 33% stocks and 66% bonds. While we tend to stay with the traditional recommendations until it’s too late, reducing leverage will capture more of the gains from the bull market of the past six years. Isn’t it time to change the paradigm?
Chart 14. Portfolio of 27% stocks, 54% bonds, and 19% defense stocks.
Perry Kaufman began his career as a “rocket scientist,” first working on the Orbiting Astronomical Observatory (OAO-1), the predecessor of the Hubble Observatory, and then on the navigation for Gemini, later used for Apollo missions, and subsequently in military reconnaissance. He then transferred the techniques developed in Aerospace for estimating the path of a missile to his systematic programs for trading in markets. His programs serve institutional clients in the financial, forex, energy, and agricultural markets. Perry Kaufman is the author of “Trading Systems And Methods” and “Alpha Trading.” He has been the managing director and general partner of investment funds and the chief architect of their strategies. He is president of KaufmanSignals.com, a website that offers subscriptions to trading strategies and portfolios. He can be contacted via his website, www.KaufmanSignals.com, or by email at firstname.lastname@example.org.
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A Baker’s-Dozen Questions on Socionomics for Robert Prechter,
President of Elliott Wave International & the Socionomics Institute Ron William, IFTA Educational Committee Robert R. Prechter is known for developing a theory of social causality called socionomics and for his career applying and enhancing the Wave Principle, R.N. Elliott’s hierarchical-fractal model of financial pricing. Prechter has authored/edited 18 books on socionomics and finance, including a New York Times best seller. His 2016 book, The Socionomic Theory of Finance, aims to replace conventional financial and macroeconomic theory with a new paradigm based on socionomics. Prechter has presented socionomic theory to academic conferences and universities including the London School of Economics, the University of Oxford, the University of Cambridge, Georgia Tech and MIT. Prechter and colleagues’ paper, “Social Mood, Stock Market Performance and U.S. Presidential Elections” (2012), was the third most downloaded paper on the Social Science Research Network that year. Prechter graduated from Yale University in 1971, joined the Market Analysis Department of Merrill Lynch in New York in 1975 and founded Elliott Wave International in 1979, where he has published monthly market analysis in The Elliott Wave Theorist. Prechter served for nine years on the board of the Market Technicians Association and served as its president in the 1990-91 year. He is a member of the Triple Nine Society and the Shakespeare Oxford Society.
Ron William (RW): What inspired your discovery of socionomic theory? Robert Prechter (RP): I was drifting toward socionomic thinking when I was 19 years old. In early 1969, I wrote a college paper assessing a brief history of popular song lyrics for expressions of attitudes towards achievement and suggested that they portended economic changes. The idea that the stock market and popular culture were linked crystallized in my mind in late 1975, shortly after joining the Market Analysis Department at Merrill Lynch in New York. I was musing about tonal changes in Beatles records that occurred in 1965-1966 while perusing a wall chart of the stock market. I suddenly realized that the tone of popular music overall ebbed and flowed with the stock market. That’s when I first sensed that I had recognized something new. The more I watched the stock market, the more it became obvious to me that news does not lead stock prices, nor is it unrelated. News lags stock prices. Economists claim the reason for this relationship is that investors anticipate the future. I have never met these clairvoyant investors. I concluded that something immediately causal to stock market movement must be producing compatible yet slightly delayed results in economic, cultural and political changes. Knowing something about Elliott waves led me to an answer: waves of social mood. During 1976-1978, I became completely committed to socionomic causality. I have a letter I sent to my dad in February 1979, where I wrote, “The state of business is a consequence of the changes in mood.” I underlined the word consequence and used the term mood. To get a feel for what adopting this The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 19
causality means, you can read the contrasting statements in Table 1. In April 1979, I went independent and started a financial publishing company. In August of that year, I issued my first declaration of socionomic causality. Although the bulk of my time was dedicated to business, in 1985 I managed to publish a long report titled “Popular Culture and the Stock Market,” which was boiled down for a Barron’s article. [These items are reprinted in Pioneering Studies in Socionomics.—Ed.] That sort of kicked things off. By the late 1990s, I had become a purist. I was able to show that every exogenous-cause argument was wrong and that socionomic causality successfully explains the same data. I gathered all my ideas on the subject and came out with The Wave Principle of Human Social Behavior and the New Science of Socionomics in 1999.
RW: If social mood governs social actions, including stock-market pricing, what governs social mood and why? Table 1
RP: Nothing external governs social mood. Social mood is an unconsciously shared mental disposition that arises in humans when they interact socially. It does not spread by contagion, it is not imparted by leaders, and it is not imposed by authorities. It arises holistically from mutual interaction, the way an economic marketplace does. Social mood predisposes members of society toward feeling and expressing through action certain characteristic sets of emotions. Humans’ impulses to herd in contexts of uncertainty allow social mood free rein to prompt social actions. Fluctuations in social mood regulate shifts in overall optimism and pessimism, which are recorded in stock averages. They adhere to a fractal structure, which is common to living forms. R.N. Elliott recognized this fact empirically, and Benoit Mandelbrot confirmed it mathematically. The Elliott wave model describes that structure.
RW: How does the Socionomic Theory of Finance (STF) provide a basis for technical analysis? RP: Technicians, along with Graham-and-Dodd value analysts, have long operated under the belief that fully rational pricing and random walk are wrong and that the stock market provides buying and selling opportunities. They could not always convincingly explain, however, why markets provide such opportunities. STF offers a coherent depiction of financial market causality that justifies technicians’ pursuits. Neoclassical economists and Efficient Market Hypothesis (EMH) theorists believe that economic and financial causality are fundamentally the same, so they have long judged technicians to be delusional. If economic theory pertained to finance, they would be right. Think of it this way: Wouldn’t it be crazy to study the past behavior of shoe prices as if they meant anything about future shoe prices? That’s how conventional economists view technical analysis. Their view follows logically from the premise that the causes of stock pricing and shoe pricing are the same. To eliminate this error, STF delineates a financial/economic dichotomy, 20 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
which distinguishes between the field of (micro)economics, where the law of supply and demand applies within a paradigm of rational utility maximization and external causality, and the field of finance, where the law of patterned herding applies within a paradigm of pre-rational herding and internal causality. Participants in an economic marketplace can consciously maximize the utility of their money because they are relatively certain about how they currently value things such as food, tools and vacations; participants in a financial marketplace unconsciously default to herding, because they are perennially uncertain about how other people will later value things such as stock certificates, debt instruments and cryptocurrencies. Unconscious processes aren’t random but proceed according to mental defaults. For these reasons, financial markets display no evidence of equilibrium or mean reversion but rather produce dynamic patterns such as trends, bubbles, crashes, head-andshoulders formations and Elliott waves. Neoclassical economics and EMH use microeconomic causality for their financial model; STF proposes socionomic causality. Table 2 lists key differences between the two proposals.
RW: The age-old debate about financial market theory continues, led by an affiliation with old-Newtonian based models. To what extent has there been a shift within the industry’s mindset about exogenous-cause, rational-reaction theory, and how much support is your latest book receiving from academia, thus far? RP: There has been little fundamental shift in theoretical orientation to date. What I see is an age-old oscillation. Behavioral finance was all the rage in the first decade of the 2000s but is less so now. Psychological hypotheses about markets were likewise prominent in the 1930s and 1940s but not so much in the 1950s and 1960s. Why is that? I think the trend of social mood determines which view is dominant. As social mood becomes more positive, the stock market rises, the economy grows, and observers increasingly embrace theories of exogenous cause and rational reaction. As social mood becomes more negative, the stock market falls, the economy contracts, and observers increasingly warm to theories of endogenous cause and non-rational action. When mood is positive, the world makes mechanical sense to people; when it’s negative, they become confused and seek out alternative models. This dynamic has played out clearly over the past two decades. In 1999, economists were among the most respected people on the planet. Time magazine put three of them on its cover. The profession was optimistic about the economic future, embraced the idea of rational financial pricing and asserted that central bankers know how to control the economy, which is a staple of the exogenouscause paradigm. That year, I was completing The Wave Principle of Human Social Behavior, which commented, “The extremity of today’s bemusement toward the outmoded idea of social cycles is yet another signal of an approaching major social mood reversal and the beginning of a trend back toward a general interest in patterns of social behavior.” Over the ensuing decade, social mood turned negative, so the stock market declined twice, the economy had two recessions, and behavioral finance became de rigueur, prompting a flood of new books The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 21
What a difference a decade makes 1999
about non-rational financial behavior. In 2002, behavioral finance pioneers Vernon Smith and Daniel Kahneman won the Nobel Prize in economics. By the end of the period, opinion about conventional economists had shifted 180 degrees. In April 2009, Business Week ran a cover asking, “What good are economists anyway?” and in July 2009, The Economist ran a cover showing that modern economic theory had suffered a meltdown. Many people thought the withdrawal of respect accorded economists at that time would be permanent. Since then, however, social mood has trended positively, so stocks have risen, the economy has expanded, and economists and their rational-market, exogenouscause paradigm have regained their halos. It seems unlikely that this oscillation will ever cease. In each era, it seems hard to imagine that people could possibly return to the ideas then considered outmoded, but they always have. Still, good ideas eventually make headway. I feel deeply honored that some brave leading lights in the areas of econophysics, behavioral finance, psychology and applied mathematics have offered kind words about my book. Several professors have incorporated socionomics into their curricula. These things make me feel there’s hope.
RW: Professor Andrew Lo, of MIT University, has been a strong advocate of alternative financial market theories, and in his latest book “Adaptive Markets”, proposes a new model based on evolution. What synergies exist between the Adaptive Market Hypothesis (AMH) and STF, and in which fundamental way do they differ?
RP: I like theoreticians who think outside the box! On the other hand, adopting a model that works in another field can be problematic. Let’s just look at some of the key differences between STF and evolutionary models so you can make up your own mind. In 1950, Armen Alchian proposed that an economy’s players succeed through evolutionary adaptation. In the realm of financial markets, George Soros (The Alchemy of Finance, 1987) proposed what he called “the principle of reflexivity,” under which “investors[’] belief[s] will change the way they invest, and that in turn will change the nature of the markets they are observing.” He posited multiple feedback loops among investors’ fallible logic, market action and external conditions. He said those interactions cause markets to evolve, often in unpredictable ways. The Adaptive Markets Hypothesis (AMH) comes to a similar conclusion through a more rigorous route. If I may summarize the introductory paper (Lo, 2004), AMH argues for the existence of distinct groups of market participants—including retail investors, market makers, hedge fund managers and pension fund managers—who compete for scarce profit opportunities in the manner of different species competing for scarce resources such as food and water in a setting of economic motivation. It proposes that these groups’ investing strategies evolve through learning by way of positive and negative reinforcement, with the goal of achieving financial survival in an environment that constantly changes due to participating agents’ own actions, which feed back into the system as new environmental causes, leading to evolutionary changes in markets, just as reflexivity theory has it. STF agrees with Soros and Lo that financial pricing is subjective, that nonrational beliefs and habits are involved in the financial pricing process, and that humans’ attitudes change economic and social conditions. Yet it does not embrace economic motivation, heterogeneous agency, reflexivity, adaptivity, profit-making knowledge evolution among speculators or any version of selfreferential feedback mechanics. Bear with me a moment, as this is a complex subject. n Under STF, trading decisions made with reference to exogenous data do not fundamentally determine market behavior; rather, the market’s mood determines people’s interpretation of exogenous data as part of the process of rationalizing optimism or pessimism. As an example: A recent statistic 22 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
showed that the economy grew at a 3% rate. Investors in an optimistic mood would declare it a strong quarter compared to a recent weak quarter, whereas investors in a pessimistic mood would say it is weak relative to past stronger quarters. You can see this effect among economists going back decades. I give an amusing example involving the trade deficit in The Socionomic Theory of Finance. n STF recognizes no heterogeneous groups to which one can attach a multiplespecies metaphor. It views speculators as members of a homogeneous group, whose aggregate behavior is motivated by its members’ pre-rational herding impulses. Speculators’ various market-related behaviors do not fit into distinct categories but fall along continua. There is a full range with respect to time horizons, degrees of experience, joining trends vs. looking for value, the application of fundamental vs. technical analysis, etc. This is true not only among all speculators but also with respect to each individual speculator at different times. n Under STF, economic and financial markets are fundamentally different and cannot be equated. Producers and consumers in economic markets on one hand and speculators in financial markets on the other perform two distinctly different roles. Competing for resources does help people thrive in economic markets, but the best way for most people to thrive when it comes to financial markets is to avoid them. Furthermore, financial profit opportunities are not scarce resources to be fought over, because no one uses them up; they are perpetually available to anyone, every minute of every day. n Nature produces many successful species, but as far as I can tell, no class of speculator achieves long-run financial success, so there must be little in the way of adaptation, natural selection or evolution going on within or among such groups. Consider that novices, large speculators and even professional fund managers are all consistently wrong at market turns. As groups, they never learn. In 2008, even the captains of America’s top investment banks, which had been in business for a hundred years, proved they had learned little about capturing profit opportunities when their firms faced bankruptcy. One such investment bank, which comprises some of the savviest financiers on the planet, wouldn’t exist today had the government and the Fed not amended the rules so they could bail it out. The firm’s lucrative ties to government are a successful adaptation, but that is an economic advantage provided by access to a rare resource; it has nothing to do with acquired wisdom about financial markets’ profit opportunities. n Under STF, the reason financial markets cannot evolve is that no group of speculators can entirely escape the primitive dynamic of pre-rational herding. So-called “Commercials,” the only consistent winners in financial markets, are successful precisely because they are not speculating; I talk about that in Chapter 17. n Under STF, there are no feedback loops between market actions and speculators’ beliefs or between market actions and social conditions. The cause is unidirectional, from mood to beliefs to actions, which create conditions. n Theories involving reflexivity, feedback, adaptation and evolution view financial markets as qualitatively mutable. STF proposes that markets are qualitatively immutable and only quantitatively variable. Changes in markets that may seem like evolution—such as shifts in the degree of attention paid to various stock sectors or in the relative popularity of value investing vs. trend following—result from momentary changes of focus within the herd. Such shifts are not a matter of what but of how much. Economists, physicists, biologists, psychoanalysts, game theorists and complexity theorists have all applied their models to finance. These models serve their original purposes well but in my view are not fully transferable to financial markets. So, I think that evolution is evolution and finance is finance. One system does not successfully model the other. STF offers a theory of finance that is not based on some other field’s model.
Investors in an optimistic mood would declare it a strong quarter compared to a recent weak quarter, whereas investors in a pessimistic mood would say it is weak relative to past stronger quarters.
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RW: Can technicians somehow apply aspects of STF and AMH together? RP: I think the difference between them is too deep for that. As my colleague Wayne Parker used to say, “Eclecticism is dangerous.” You can’t mix incommensurate hypotheses; you have to choose one or the other. For example, Lo, Mamaysky and Wang (2000) conducted a wonderful, seminal study statistically validating the famous head and shoulders pattern (H&S) in financial markets. But think about it: If markets did evolve away from recognizable profit opportunities, then H&S, which signals a profit opportunity, could not be a consistent phenomenon. The pattern’s efficacy—if not the pattern itself—would be repeatedly, if not permanently, obliterated by adaptation and evolution. In other words, it seems to me that to remain metatheoretically consistent, one can subscribe to either H&S or AMH, but not both.
RW: Mainstream macroeconomic and financial theories rely upon exogenous cause and rational reaction. So-called triggers for action include “information flow,” “fundamentals” and various “catalysts”, which can translate into highimpact events such as central bank policy, economic releases, or market shocks. How can the application of social mood concepts deal with these influences and help improve our analysis of the financial market?
RP: These concepts are borrowed from physics and chemistry and do not pertain to finance. Chapter 1 of The Socionomic Theory of Finance, titled “The Myth of Shocks,” offers some convincing anecdotal evidence that backs up three exhaustive studies by other researchers demonstrating that even the most dramatic natural and social events do not serve as triggers or catalysts for moves in stock averages. Chapter 2 challenges thirteen widely accepted claims of stock-market causality—involving interest rates, oil prices, central-bank activity, economic data, and so on—deriving from the exogenous-cause model. The evidence there surprises many people. Chapter 23 on bubble theories specifically addresses the ideas of tipping points. In physics, tipping points imply hidden forces building to produce a singular event, typically a shift from one state of equilibrium to another. To begin with, financial markets have no such states of equilibrium. Their prices fluctuate up and down in a condition of perpetual dynamism, at all scales of observation. One would have to postulate an infinite number of tipping points, a formulation that would be devoid of explanatory or analytical value. To analyze financial markets, you have to start with a model that recognizes the market’s independence from exogenous causes. I think the proper model is waves of unconscious social mood that adhere to a hierarchical fractal, specifically the Elliott wave model.
RW: What do your key indicators signal about financial markets now, and how will conditions likely change from what we have experienced in this cycle? RP: Optimistic market opinions, deep commitment to the stock market and its derivatives, extreme linear projections for higher prices, all-time-high levels of debt and a persistent decline in debt 24 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
quality all indicate that social mood today [submitted November 8, 2017—Ed.] is exceptionally positive. I recently displayed 40 sentiment indicators that are at or near all-time extremes. That fact does not tell you for certain where the stock market is going, however, because mood can get even more positive, a development I have observed numerous times. But it does suggest a condition of historically high risk in owning stocks and debt.
RW: Can we derive any socionomic conclusions about the social, political and geopolitical landscape? RP: Trends toward positive social mood lead to rising stock prices, economic expansion, relatively peaceful times and affirmative cultural expressions. Trends toward negative social mood lead to falling stock prices, economic contraction, social and political conflict and contrary cultural expressions. With that knowledge base, you can get a handle on the mood behind any social environment.
RW: Who should incorporate socionomic analysis? RP: Socionomics is for people who want to understand the world around them. Our studies link social mood to some fifty human activities, including stock market trends, employment trends, election outcomes, wars, scandals, drug prohibition, epidemic disease, baby names, hemline heights [see Figure A], skyscraper construction, roller-coaster construction, nuclear weapons testing, procreation rates, automobile styling, automobile horsepower [see Figure B], the quality and popularity of movie genres, the activity of serial killers and the fortunes of pop stars. If those things interest you, then read our two latest books, Socionomic Studies of Society and Culture—How Social Mood Shapes Trends from Film to Fashion and Socionomic Causality in Politics—How Social Mood Influences Everything from Elections to Geopolitics. These books are a lot of fun, and I think they could change the way you view the world.
RW: What would you recommend specifically for technicians and speculators to learn? RP: Above all, they need to understand STF so they can avoid spending time and energy on peripheral matters that have little or no analytical value. This is not as simple as it sounds. It took me quite a few years to jettison all exogenouscause assumptions, because doing so is deeply counterintuitive. If you read just the first two chapters of The Socionomic Theory of Finance, you should be primed to go. My next suggestion is to learn the Elliott wave model. It takes some practice, but it’s worth it when the waves are clear. For an example of application in real time, read Chapter 22 of The Socionomic Theory of Finance, which contrasts twenty years of analysis in the oil market by five successive practitioners to supplyand-demand analysis from economists. Measures of breadth are helpful, since
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specific wave labels imply certain breadths of participation among individual stocks, stock sectors, stock indexes and—at the highest degrees—allied markets globally, and the two datasets should be compatible. I also recommend maintaining as many sentiment indicators as you can. It is important to realize, though, that technical indicators cannot do the job alone, because wave degrees determine how extreme those indicators will get. I had to learn that the hard way, too.
RW: I have seen some practitioners ignore the rules and guidelines of wave construction, and their posted work is a concern because it implies that the Elliott wave model is subjective. Is there a way to fix that problem? RP: Yes, we have a solution. A team at Elliott Wave International spent several years developing an objective, hands-on test of wave-identification skills. It is entirely computer-administered and graded. It is a tough test, not easy to pass. At the last IFTA meeting, I met one person who had become a Certified Elliott Wave Analyst (CEWA) and another whose colleague did so. If you pass that test, you have proved to prospective clients or employers that you know the subject.
RW: What vision do you hold for the future of socionomic study and the resources for its development? RP: Well, I have spent about 20 years writing books and papers, making speeches and videos, hosting socionomics conferences and launching a monthly publication called The Socionomist. My last two books completed a five-book set on socionomics. One of my goals has been to write three papers that would show the way for further research. The first one would present the socionomic theory of finance, the next would contrast socionomic causality to standard causality statistically to provide a template for future research, and the third would be a comprehensive study validating the Elliott wave model. The first two are done and have been published in peer-reviewed journals. [They are: “The Financial/Economic Dichotomy in Social Behavioral Dynamics: The Socionomic Perspective” (2007; Journal of Behavioral Finance) and “Social Mood, Stock Market Performance and U.S. Presidential Re-Elections: A Socionomic Perspective on Voting Results” (2012; SAGE Open); reprinted in The Socionomic Theory of Finance and Socionomic Causality in Politics, respectively, and available online on the Social Science Research Network (SSRN). —Ed.] I’ll be working with my son Elliott on the third paper, which is in the planning stage. The Socionomics Institute is set to carry on without me, and it’s doing great. Lampert, Hayden and Hall’s “Behavioral Finance Beyond the Markets: A RealTime Case Study of Russia’s Military Resurgence” was published in 2016 in the Journal of Behavioral Finance & Economics, and half a dozen other papers have been published or are in the works. But SI comprises only a handful of people, and there is a lot of work to do. The Socionomics Foundation offers grants to interested academics and welcomes anyone to help further the cause. Validation takes time, but we’re making it happen. For more about Socionomics, visit www.socionomics.net. Related sites: www.robertprechter.com; www.elliottwave.com
We invite you to read the Review of Robert Pechter’s book, The Socionomic Theory of Finance by the late Hank Pruden, Ph.D., which appeared in the Spring 2017 edition of The Swiss Technical Analysis Journal.
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A Key Theme at the 30th IFTA Conference in Milano: Trend Following in Portfolio Management Alberto Vivanti I enjoyed the 30th annual IFTA conference in Milan hosted by the Italian Society of Technical Analysis (SIAT). It was a beautiful experience, both as a speaker and attendee; I met many colleagues and friends, some I hadnâ€™t seen for many years, it was exciting to share our findings in technical research. I discovered that the number of technicians that employ a trend following methodology for portfolio construction is constantly increasing and that the quantitative aspect of technical analysis is more and more important in asset allocation.
The algorithmic analysis is giving an amazing contribution to the portfolio construction, especially when the size of the exposure in the asset classes becomes a key issue for controlling risk and optimizing the reward/risk parameters of the portfolio returns. More and more investment fund managers are concerned about a trend that, deep down, is the basic concept of technical analysis. Momentum-based techniques are becoming an important component of quantitative allocation models: the risk optimization factors in portfolio construction can be improved through trend-based variable exposure. In many presentations, given by valuable, experienced speakers and fund managers at the IFTA conference, there has emerged that trend following techniques, as a filter for protecting portfolios against an adverse environment, reveal to be far more effective than many commonly used methods based on volatility filtering.
One case example: consider the sector indexes that compose the European market, the well-known Stoxx600 series. In figure 1, I have simulated three equity curves: one, composed by a constantly rebalanced equal weighting of 18 sectors. I have not used the Stoxx600 Index because its weighting imbalance among sectors caused by the sharp difference in market capitalization of the index components. The others are two simulated portfolios made of nine sectors each. One is a monthly rebalance of the nine less volatile sectors, showing the lowest values in standard deviation at the end of each month. It is calculated on a six-month window of weekly data. The other is also a monthly rebalance of nine sectors but with the opposite characteristics, the most volatile.
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Table 1 - Equal Sectors Distribution with Volatility Filters
Equal Weighting 18 sectors
9 Low volatility sectors EW
9 High volatility sectors EW
Table 1 – Simulated returns of equal weighting of 18 Stoxx sectors, constantly rebalanced, compared with two simulated portfolios: 9 low volatility and 9 high volatility sectors indexes (standard deviation at 6 months, monthly rebalanced). The low volatility selection provides better results, yet the maximum drawdown is still very high.
The less volatile portfolio was expected to be better and so it is. Return is higher, volatility is lower. Nevertheless, even if better, the volatility figures and the size of drawdowns are still very high, the return provided, even if higher, still does not justify such amount of risk that is not far from that of a buy-andhold strategy.
Figure 1 – The simulated equity curves of €100 invested in 2000 in the three combinations described in Table 1. The curve in the middle is the equal weighting of all the 18 sectors, the blue line is the portfolio composed of the 9 less volatile sectors.
Table 2 - Equal Sectors Distribution with Volatility and Trend Filters
Equal Weighting 18 sectors
9 Low volatility sectors EW
9 High volatility sectors EW
Table 2 – Simulated returns of the three strategies described in Table 1 after the addition of a filter, based on a six months momentum calculated on the equal-weighted index. When negative, all the strategies stay out of the market and the exposure is taken to zero. 28 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
Things change by applying a trend filter to the same strategies, such as a sixmonth momentum, calculated on the equally-weighted combination of the 18 sectors. If at the end of the month, this long-term indicator is negative, then the whole investment is liquidated into cash. The simulated results are shown in Table 2, the equity curves in Figure 2. Things change by applying a trend filter to the same strategies, such as a six months momentum, calculated on the equally weighted combination of the 18 sectors. If, at the end of the month, this long-term indicator is negative, then the whole investment is liquidated into cash. The simulated results are shown in Table 2, the equity curves in Figure 2.
Figure 2 – The simulated equity curves of €100 invested in 2000 in the three combinations described in Table 2 with the addition of a longterm trend filter on the whole investment.
It does not matter how complex and smart a trend-following strategy is. The method described here is very simple and more complex momentum strategies can be employed. The basic assumption does not change: a trendfiltered strategy is much more effective than many traditional methods that are employed in the funds industry to reduce volatility. There is a big diffusion of funds and ETFs aimed to provide less volatile returns. It is true that a standarddeviation filter can improve the quality of returns, but a dynamic allocation exposure obtainable by a trend following strategy can do much better. This is the path that more and more quantitative asset managers are now following.
Alberto Vivanti, Independent analyst, founder of Vivanti Analysis in 2003. Alberto is a technical and quantitative analyst since the early 1980s, with a sound experience as an asset manager with Swiss Institutions. Author of a technical newsletter, lecturer for institutions and instructor in Technical Analysis courses in Switzerland for the IFTA Certification, author of articles and books, he has been co-author of a book with Perry Kaufman. Alberto chaired the IFTA conference held in Lugano in 2006. He has been a speaker at the IFTA Conferences - 1998 in Rome and 2006 in Lugano. Alberto is Vice President of the Swiss Association of Market Technicians, representing the Chur and Liechtenstein Chapters.
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Review: 7th Annual Lantern Fund Forum, Lugano Mario Valentino Guffanti, CFTe The 7th Annual Lantern Fund Forum (LFF) in Lugano, was held on 20-21 November. This most important event in Switzerland which is reserved for financial professionals and focuses on asset management, investment tools and fundamental analysis.
exclusively for the institutional public. For the past three years we have been making a further selection within the institutional public sector segment, distinguishing between those who are buy-side, i. e., those who are potential buyers or investors of third-party funds and who have liquidity to manage and invest, and all the others. We want to focus on the high-end segment which we invited to the Forum for free.
In addition, there have been some phenomena that continue to grow year after year, bringing more and more organizational problems. I am referring to people who register and then, without notice, do not show up to the event, or people who, despite having three months to register, only show up at the start of the event. This causes serious problems for the event organizers (the size of the rooms, the numbers for catering, the number of badges and brochures, the amount of material that sponsors must bring, etc.).
A new format
This year the format was slightly different, in the sense that access to previous Fund Forums have always been free-of-charge, and subject to online registration or on site. This year, however, only the delegates who belonged to the buy-side, i.e., those who are potential buyers or investors of third party funds and who have liquidity to manage and invest had free admission, while the other participants had to pay an entry fee. Another innovation was introduced where we could find an important special session focused on the hottest topics of the moment, not strictly linked to asset management: Fintech, cryptocurrencies and blockchain. I interviewed Riccardo Esposito, CEO of Lantern Fund Forum, to better understand the new format.
MVG: Good morning Riccardo, can you tell our readers why you decided to change from a free to a paid event? RE: For several reasons. First, to make you perceive the event was quality: To pay is the litmus test of quality, of how much you believe in your products and services. My goal is not to make money, but non-payment equals little or no value.
The Fund Forum should be synonymous with quality and prestige, and in this perspective our organization decided from the beginning to eliminate the retail sector and aim
MVG: What have you achieved from this Fund Forum? RE: From the point of view of numbers I am satisfied. Despite the introduction of the entrance fee, we had 1,200 attendees, about 200 delegates less than last year. But if we analyze this drop in attendance, those who were buy-side increased, while the cut took place in the sell-side: we had 60+% buy-side and about 40% sell-side. We hope that this qualitative improvement was noticed by our sponsors, but we have to wait to evaluate the results of follow-ups in the coming weeks to understand their level of satisfaction. Regarding the introduction of topics focused on Fintech, blockchain and cryptocurrencies, I am more than satisfied. Our classical audience followed these topics with great interest. The introduction of new topics to our classic program, enables us to attract a new audience to LFF.
SAMT & IG Bank
This year, SAMT partnered with IG Bank and I conducted a workshop with Andreas Ruhlmann, Premium Client Manager and Market Analyst, at IG Bank. The content was
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about the rules that every trader should know. We spoke about the academic studies that dealt with the importance of technical analysis, how to develop an exit strategy, stoploss versus buy & hold, the best time to take profits or cut losses, how to optimally choose the size of a position and trading psychology. A part of the seminar was dedicated to the operation of the rules described, using the IG platform. The Forum focused on five topics: Fintech, independent financial advisory, alternative investments, social responsibility, and women & finance.
The keynote speaker was John Mauldin, visionary thinker, noted financial expert, New York Times best-selling author, pioneering online commentator and publisher of “Thoughts from the Frontline”. John Mauldin: A journey into the future in the next 20 years will be a world of abundance, but with a very bumpy road.
Mr. Mauldin spoke about what the world will look like over the next 20 years, not just technologically, but from the aspect of how society will change and how economies will evolve. The result was a vision of a happy ending in a new world of abundance, but with a very bumpy road to get there. Mr. Mauldin began by talking about his new book in which he tells how he believes the world will be over the next 20 years. One part is dedicated to technology, which we all see passing before our eyes. But the fundamental points are also linked to other issues, such as the difficulty for humans beings to adapt to the changes that will take place. Another factor that complicates matters is the activity of central banks and governments. They create the rules, but they are not doing so well at the moment.
The current issue of central banks’ operations was addressed through questions that Mr. Mauldin asked the Swiss money-manager audience (at the end of the article, you can read his comments published later in his newsletter). How long will it take for the Swiss National Bank (SNB) to buy more than a trillion dollars in equities and bonds? It currently owns $831 billion and can be considered the world’s largest hedge fund. SNB already owns 3% of Apple. What is SNB doing with 3% of Apple? Norway has almost a trillion dollars of assets in its hands and Japan’s national bank has finished buying bonds, now
it is buying ETFs, not only in Japan, but also in Europe and the United States. The final consideration is how customer money can be handled in a world where central banks and governments buy assets. They buy without making any assessment of the quality of what they are buying. Another question was about the survival of the European Union over the next five years, which saw the audience agree for its continuation.
Mr. Mauldin talked on technology and how it will lead to a better world. The themes were the new technologies that will allow the world to be fully connected with innovative Wi-Fi networks, such as the Goggle balloons with a WiFi receiver that could cover the whole world with their new technology, or the solar drones of Facebook. This could lead to free Wi-Fi connections on iPhones and other mobile phones. Mobile phones will be a thousand times more powerful than today, we will probably wear them and in 20 years they will be connected to our brain. It may seem crazy now, maybe not us, but our children will be able to take advantage of this technology and ask questions that hundreds of expert systems with artificial intelligence can answer and explain how to solve problems using the available resources.
Three billion new people will enter the global system. There will be self-driving cars. The world will be very safe in self-driving cars. With self-driving cars, all the old jobs like taxi or truck drivers will disappear. And with selfdriving cars no human error will cause car accidents, so there will be no need for auto mechanics as there won’t be accidents; no need for insurance or agents, and so on. It seems beautiful, but there are 10 million jobs in the US alone which will be affected. And where will these people work? A truck driver is not ready to do a tech job. Artificial intelligence will take one job after another, and half of the people in financial services will no longer have the job they have today, because in the next 10-15 years their work will be done by a computer.
There are credible people who have estimated that 500 million jobs will disappear in the next 20 years. Some new jobs will appear, but there is a problem. We have gone from the farm, which in 1800 covered 80% of the USA to the current 2%, and we produce hundreds of thousands times more than we did 220 years ago. But we have done this over generations. Slowly but surely, people moved from the countryside to the city. The number one job in Chicago in the early 20th century was to be a butler because the middle class grew and people hired others to take care of the house, laundry and wash the dishes. Thirty years later, washing machines and dishwashers arrived and there was no longer any need for those people who then had to find new work. But it took time. And today this happens in a finger snap, and we are not ready: this kind of change is too fast. Then Brexit, Trump, Macron are part of the frustration of the world’s social mood. There is an epidemic for the first time in US history, they are seeing people dying earlier,
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while others live longer – males 45-55 years old who use drugs, alcohol and commit suicide. This is because they no longer have a job, have no hope, and have given up. In Europe, as people vote, it does not seem that things are getting better. We have a society that is increasingly frustrated and we have to face the two biggest bubbles in world history: the government debt bubble, and the bubble of government promises. They have promised benefits, pensions, health care, roads, but are unable to pay. The USA has an advantage over Europe, taxes are lower and, therefore, there is still room for raising them. In most parts of Europe, taxes have reached 45-50% and there is no more room for raising them. However, pensions and health care continue to grow. According to Mauldin, the solution will be that the money will have to be borrowed. The European Union will exist for the next five years, but the only way to do this is for everyone to hold hands and move away from the abyss together, so that we can take all the debt, all the Italian, German, Spanish and even Greek debt, and give it to the ECB and then sell it to all of us. It is then taken away from the state budget, so that governments have the opportunity to pay and honour their promises. The debt will not disappear, but it would simply no longer be in the government budget. The role of money managers will be to take their customers along this path. There will be another recession. Mauldin thinks that what we will have to do in the future with our clients will be to say the that world has changed: money will not have to be managed as usual in different assets: it’s all a single large pool of risk and everything interacts. Trading strategies will have to be diversified to quantify and indicate where to put money and when. The latest recovery has been slower than in previous years, because the largest debt is an obstacle to growth. Therefore, systems will be needed that indicate where we will grow in the next recovery and we need to have a global vision.
Most of us have a home bias. The Germans want to invest in German equities, the US in American equities, and so on. But what we will see is that growth will be in the emerging countries, not the same, not at the same time, but there will be a need for systems that will allow diversified trading in the markets that rise and then define them. And this will happen where frustration will be increasingly present. In Europe, frustration due to immigration, is in the US, too. Mauldin said that he is part of the Republican party, he knows his responsibilities, but no longer understands what is going on. The Republican party wants less immigration. But there are only two ways to grow the economy: increase productivity, which is not increasing, or increase the number of workers. We will, therefore, have to understand how to have more immigrants. We should recruit young people. One example is Silicon Valley: how many immigrants have created new companies and jobs. We are not thinking properly.
Mauldin’s ancestors were Irish, and in 1850-1860 there were a lot of Irish people in New York, and there were posters
in the bars saying that dogs and Irish cannot enter. They were not the favourites because people thought they took all the jobs. Do you know what the Irish did 20 years later? They tried to prevent Italians from arriving. And years later, the Italians did the same thing to the Norwegians and Eastern Europeans. Every generation says: I’m here and I don’t want anyone else to come because I don’t want jobs to be taken, but that’s not right. All these factors are leading to enormous frustration. Is the end of this story? Yes, despite the problems that will arise from the situations described, the result will be good.
Mauldin is now working with a company that studies a cure for pancreatic cancer and can be used for any other cancer. You will not lose your hair and there will be no side effects. Another company with which he is collaborating is studying a method that within the next 10-15 years, with the use of genetics, a person of 70 will be rejuvenated back 10 or 15 years to 55-60. It is not only a question of extending lives, but also rejuvenating the body’s cells to reverse age back 10 or 15 years. Our food will become increasingly abundant and cheaper. We will soon be able to obtain drinking water from the ocean. The cost of all things is decreasing and we will live in a world of abundance. But until we reach this point, we must be able to understand how we can overcome the obstacles that we are facing, such as politics, public debt and pensions that cannot be paid. Along the way to this new world of abundance where we will live longer and where health will improve, we will have to try to find out how to get in tune with each other. The world is certainly in a deflationary scenario. The cost of things continues to fall, which is a good thing.
We need to understand how we can take our customers from where they are today to where they want to be in 10-15 years. Because when we come to the end of this process to manage, change, frustration, and debt all these problems will be solved. And we will succeed because that’s what the world does, it always pulls forward, it keeps on always pulling forward, then we will have the largest bullish market.
I end this article dedicated to Mauldin’s speech, with the his thoughts published in his newsletter of 25 November.
“I spoke to a number of Swiss money managers and family offices while I was at the conference, and I can tell you there was not a sense of complacency. They were all very nervous and not quite sure what to do – not unlike many of my readers. We took an informal poll, and a majority of the attendees felt that the Swiss National Bank’s balance sheet would top $1 trillion in less than a year. They are goosing it in order to keep a lid on the Swiss franc. Interestingly, 65% of the attendees felt that the SNB should not be buying US equities (it now owns more than 3% of Apple, for instance); and while this audience earns their keep by managing money for mostly non-Swiss clients, they were all concerned about the continued strength of the Swiss currency and wondering how long it can remain so strong.”
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interesting for this reason. The regulators are allowing the experiment because the situation is new, but in a few years and maybe after some major fraud, the regulators will certainly have to intervene.
The model suggested to banks to enter the cryptocurrencies business is to focus on the security issue, which is fundamental. Service outsourcing is not recommended. It is better to have an internal structure, to be built with real experts and best practices, as the sector is still very young. So the solution is to work internally with external experts.
A good technical analyst needs to know in which market the money flows go so that he knows where to invest. The same can be said about new topics that will interest people in the coming years and will create future trends. So in 2 days at the Forum, I tried to understand which were the conferences that had the most public flow. It was not very difficult: in fact, there were only two lectures that completely filled the hall with standing room only, and these were the two panels on cryptocurrencies:
Cryptocurrencies, a New Trend to Watch
Panel #1 – dedicated to cryptocurrencies and banks: how banks may play a winning role?
The real masses of money on cryptocurrencies will be seen when responsible investment vehicles will be created. There are still no real investment funds. It is not easy, for those who are not yet in the blockchain community, to understand what is happening now.
Panel #2 - cryptocurrencies and traditional assets: a winning strategy or a dangerous mix?
Panelists: Maurizio Mazziero (Consulting Partner, Pro Aurum Schweiz AG); Paolo Barrai (CEO, Cryptolab); Leendert Van Hoeken (Managing Partner, Colin & Cie, Switzerland); moderated by Paolo Cavatore (Swiss Finance + Technology Association).
In fact, cryptocurrencies were created as an alternative to payment instruments, but also as an alternative to traditional investment assets. Banks could play the role of custodian and asset manager.
The research indicates that Bitcoin are decorrelated in comparison to other asset classes, except for some short periods when the correlation has been low (around 0.1 0.2). But the track record is very short, only five years. In fact, Bitcoin was created nine years ago, but the first four years were seen as pioneering. Bitcoin were born in 2009, as a response to the structural crisis, with rising public debt. The latter may be erased in the future, but Bitcoin, by its own nature, remains outside this problem and could be very interesting.
Therefore, banks could act as custodians and provide investment services for those wishing to invest in Bitcoin. Cryptocurrencies are a decorrelated asset and, therefore,
Not only Bitcoin but its technology, consisting of blockchain, could also be exploited in other fields, such as the banking sector, where increasingly expensive regulatory controls for KYC (Know Your Customer) could
Is Bitcoin a business opportunity for banks or a poison pill?
Panelists: Giacomo Zucco (CEO, Blockchainlab); Lars Schlichting (Partner, KPMG); Lucas Betschart (President, Bitcoin Association Switzerland) and Daniele Bernardi (CEO, Diaman). All the panelists agreed on the paradox that, despite cryptocurrencies, were born as a means for bypassing banks, the latter could be very useful in providing services in their world.
However, it is necessary that the phenomenon of cryptocurrencies be regulated by the legislators in order to understand if Bitcoin can be defined as a security and consequently regulated. In the case of transferable security, it is necessary to identify who takes responsibility for the asset and takes responsibility for any losses. In Europe, the mentality seems to be more open than in the US.
Bitcoin appears to be more decorrelated than gold – gold works very well in times of geopolitical and market shock, and with inflation. But currently Bitcoin cannot be seen as a real reserve of value due to its volatility. A future regulation and listing with futures could lead to a lowering of this volatility.
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take advantage of this technology, which would bring considerable added value.
The part about cryptocurrencies ended with an original presentation where a number of selected Crypto-Projects introduced themselves for three minutes each.
A part of the Forum was dedicated to Fintech and Artificial Intelligence, with a panel moderated by Luca Maria Gambardella, Director, Dalle Molle Institute-USI/SUPSI.
A final presentation by Carlo Terreni, CEO, NetComm Suisse, who has been a witness of the radical change that technology has brought to the fashion industry with the birth of e-commerce and other digital applications that in a few years has revolutionized this sector. I close my report with thanks for the collaboration of translators and Cristina Valtorta of Unimoney.
Lantern Fund Forum founder, Riccardo Esposito and Keynote Speaker, John Maudlin
Lantern Fund Forum Gala Dinner
Photos by Makro Photographers
Mario Valentino Guffanti, CFTe is a Financial Advisor, Certified Financial Technician and Researcher and also a lecturer in Technical Analysis. He is the Vice President of the Swiss Italian Chapter of the Swiss Association of Market Technicians (SAMT).
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An Interview with Dr. Van Tharp, founder of the Van Tharp Institute Ron William, CMT, MSTA Dr. Tharp is the author of Trading Beyond the Matrix: The Red Pill For Traders, published by Wiley & Sons, in addition to four acclaimed books published by McGraw Hill: Super Trader, Trade Your Way to Financial Freedom, the New York Times Bestseller, Safe Strategies for Financial Freedom, and Financial Freedom Through Electronic Day Trading. Tharp is the only trading coach featured in Jack Schwager’s bestselling book, The Market Wizard’s: Interviews with Great Traders. He has been featured in Forbes, Barron’s Market Week, Technical Analysis of Stocks and Commodities, Investors Business Daily and Futures and Options World, and Trader’s Journal, just to name a few.
Dr. Tharp has collected over 5,000 successful trading profiles by studying and researching individual traders and investors, including many of the top traders and investors in the world. From these studies he developed a model for successful trading and investing in which other people can adopt and learn. He has developed a five-volume Peak Performance Home Study Course, teaching the results of this ten-year study. He also developed the Investment Psychology Inventory Profile to help people better understand their strengths and challenges in relation to trading or investing.
Ron William & Dr. Van Tharp at the VTI workshop in London, October 2017
workshop, which I had the pleasure of just attending here at your Van Tharp Institute (VTI) headquarters in Cary, North Carolina, USA, during April 2017. My opening question is about your life story. Dr. Tharp, what inspired you on the path of trading psychology and transformation? Dr. Tharp: Well it’s interesting, because I just learned that Mark Douglas, author of Trading in the Zone, passed away last year. He and I both started at the same time as preeminent people within the world of psychology and trading. Mark was inspired by the Seth Material and I was inspired by A Course in Miracles. I started working through A Course in Miracles in 1982, and by the time I finished, this business was pretty much full-time operation for me. Even then, when I didn’t know that much about transformation and it didn’t happen that quickly, it still felt like my mission was transformation through a trading metaphor.
RW: How clear was it this would be your mission in life? Dr. Tharp: It was more fuzzy then but I knew then that it was my bliss and that I really got a lot out of helping people. Now it’s really obvious. In those days, it was more about simply enjoying that I was helping people transform. In contrast, I didn’t like doing research or working for the government and that type of thing. Going in this direction was very much part of my destiny.
RW: What can you tell us about your background in Psychology and Modelling techniques?
Dr. Tharp: I always wanted to develop models. Psychology is where I started, but I didn’t get my modelling techniques out of psychology. I learned to develop models from studying Neuro Linguistic Programming (NLP). The idea He published the Market Mastery newsletter for over 10 behind NLP modelling is that you find a number of people years, and now publishes a weekly e-newsletter, Tharp’s who do things well and find out what they do in common. Thoughts. Dr. Tharp wanted to get the vital information Once you have the common tasks, then you need to find that traders needed to as many people as possible; the three ingredients of each task which are beliefs, mental therefore, he decided to offer his newsletter at no charge. states, and strategies. Before that subscriptions to his newsletters were as much I modelled the trading process, the process of developing as $249 per year. a trading system that fits you, and how to use position RON WILLIAM (RW): Thank you Dr. Tharp for this sizing to meet your objectives. There are probably now follow up interview opportunity, on behalf of the Swiss around 115 Tharp Think concepts™, which do not make Association of Market Technicians (SAMT), affiliated with a model exactly but they are more of a set of beliefs which the International Federation of Technical Analysts (IFTA). came from numerous sources. These concepts really help We also want to recognize many leading financial market people transform and perform well as a trader. The infinite professionals around the world that are interested in your wealth model™ is another one. We are working on market life’s work on trading psychology and transformation. I types which demonstrates that it’s really insane to design must also congratulate you on a truly amazing Peak 101 a system that is expected to work well across different He also has developed a course on How to Develop a Winning Trading System That Fits You, and written and published The Definitive Guide to Position SizingTM.
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market types. If you understand one market type really well, it becomes much easier to develop a system that works well in that market type.
in his 800+ page book called Science and Sanity. What’s funny is that he devoted 800 pages of words to explain why words tend to mislead us into an illusion. My plan is RW: Going back to the conventional principles of to write a book about this topic tentatively entitled: Matrix psychology, how would you explain the trappings of so- Thinking: Going from Force to Power to Awakening. Thus, called conventional “black-box” thinking, as stated during this question is not easy to answer in a short paragraph or two, but let me summarize a few principles: your workshop. • Our experience of the world comes through our sensory Dr. Tharp: I’ve always wanted to know how the human receptors. I’ve read that our senses only experience mind works, whereas the world of psychology has been about one trillionth of the frequencies in the universe. focused on developing itself as a science. Thus, everything Furthermore, the firing of a particular sensory receptor needs to be objective and measurable. The problem with is not the same as the energy wave that stimulates that approach, however, was psychologists didn’t think that that receptor. For example, a long light wave which the processes going on inside the mind (such as thoughts stimulates the long wave length cones in the retina, and beliefs) were measurable so they chose not to look at produces an electrical signal in the brain— it does not such processes. It’s interesting because psychology in those produce a long light wave length. The electrical signal days was modelling itself as a science, after Newtonian then produces a sensation that we label red. physics, which had already become obsolete. That seemed • When we label the sensation red, however, we remove amusing to me. the experience of the sensation by one more degree. RW: Walking in the hallways of the Van Tharp Institute, And then we might remove ourselves further from that I appreciated the inspiring gallery of famous influencers experience by judging it — red is an aggressive color within the field of psychology and the financial markets. or an angry color. The removal from the experience Around the corner from the Godfathers of psychology, continues when we think thoughts like red cars are Carl Jung and Sigmund Freud, was what I understand to more likely to be pulled over for a speeding ticket than be one of your most favorite thinkers of that time; Alfred cars of other colors. Korzybksi. He became known for his signature mantra “the map is not the territory”, a concept that you taught about • We use language to codify our experience and this process actually shapes the brain as we grow through extensively in your course. Please explain to our readers our first five years of life. Through language, we learn to what this means and why it is so important for traders to separate the world into subjects, objects and verbs. But understand? this is just a map, the world is not made up of subjects, Dr. Tharp: A number of religions say that our experience objects, and verbs. of the world is made up. Hindus and Buddhists call it • When we name a thing, we think that we know it. But “Maya.” The whole proposition in A Course in Miracles is naming it almost guarantees that we don’t know it. If that “nothing real can hurt you and nothing unreal exists. I say dog, what sort of dog do you picture? Whatever Herein, lies the peace of God.” The book teaches students it is, it’s probably not the picture I imagined. Further, of the course to forgive yourself for having a dream and what qualities do you give to a particular dog? I call then forgive your brother for being part of the dream. my dog Tigger athletic, but that label is based upon a Korzybski explained how we create our illusion of reality few things he does only occasionally, such as catching a squirrel. Most of his days are spent sleeping and begging for food (he’s actually overweight). • We also take verbs and turn them into nouns in a process called nominalization. I’ve done that a number of times in this explanation. Here’s a good example, “the market” is a nominalization. Rather than a thing, it’s an ongoing process of buying and selling perhaps best represented by tick dots for the price of each transaction. But then we turn tick dots into a candlestick and then we turn candlesticks into moving averages and all sorts of other indicators – all so we can make some meaning out of the market. But that meaning is entirely made up. • Finally, when you understand that you make it all up, you tend to become more and more “awake” in the spiritual sense. You can also use this information to move toward peak Alfred Korzybski (1879-1950), independent scholar performance knowing that some beliefs (even though who developed a field called general semantics, as they are made up) can be much more useful than other framed at the Van Tharp Institute. 36 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
beliefs within the context that we deem important (i.e., the context for the market might be making money).
became clearer to me. I understand now how the brain has to transform. RW: What have been the key changes in your work The best way to explain the process so that everyone can since the first interview that you gave in 1989, as part of understand is to remember that key phrase “the map is Jack Schwager’s best-selling book, The Market Wizard’s: not the territory”. This means that our internal model of how everything is and works has nothing to do with the Interviews with Great Traders? external world. We are never going to know the external Dr. Tharp: In 1989, I was still just scratching the surface world at all but instead, we can have more or less useful of the impact of psychology as it applies to trading and models on the inside. one’s life. Since then, I became an NLP modeler and have modelled several areas previously mentioned such as More to the point, let’s talk about the financial markets. system development, position sizing™ strategies and There is no such thing as the market. It’s only a word market types. Now we talk about levels of consciousness that describes a process. At best you can say that it’s a or oneness, as part of a log scale of combination of price ticks that occur all day long and so it levels of awakening going from zero to means something different to everybody. The analysis can evolve from observing a single raw price tick, to candle a thousand. patterns, then to moving averages and oscillators. We keep There are about 18 stages of awakening. transforming the data unless we can make some meaning The first stage is where problems out if it and yet none of it is real. But given that we know disappear, because you start to think the map is not the territory, we can come up with useful about solutions more. I remember that it beliefs. No belief is true without a context. But when you used to be a big deal to be pronounced find and adopt useful beliefs, then you can become a peak awake and get your first number. Now performer. I have a pretty high number and at the same time it doesn’t mean anything RW: What would be a useful belief for traders that want to me anymore. The biggest shift probably happened to successfully make money? around 2008 when I became a blessing giver and then the Dr. Tharp: These would be ideas liketransformations started accelerating. • Always know where your trade idea is wrong when RW: What guidance do you suggest to skeptical or you get into a position. We define that as your initial conventional market participants which are too focused risk or 1R (a positive risk multiple). on their bottom-line trading performance, and might be new, or initially resistant, to the idea of applying spiritual • Another one could be — don’t enter a position unless your potential reward is at least around three times the transformational techniques to become successful traders? size of your initial risk. So that would mean your gains Dr. Tharp: Well, here is an interesting thought. What should be 3R and your losses should only be -1R. if when you died you basically had a conversation with your higher Self (or with God or an angel), in which you RW: Can anyone be a successful trader? I ask this as part reviewed your life with a spiritual (oneness) perspective. of the classic age-old debate, popularized by the legendary Suddenly you could understand your limitations, charges commodity traders Richard Dennis and William Eckhard and non-useful beliefs. As you looked back at your life, in the early 1980s, with their Turtle Trader experiment, to you could see how you kept repeating the same mistakes prove that anyone could be taught to trade. over and over again. The next thing you know, you end Dr. Tharp: Well first, the Turtle experiment didn’t really up in another body (with no memory of your past life), prove anything. Dennis and Eckhard interviewed a massive trying to get through it without making those same types number of people, immediately rejected something like of mistakes. That cycle may be your condemnation to hell. 90% of them, and from the remainder selected a few Turtles. If that seems like the case, then why not recognize your Even then, the Turtles had huge differences in their results mistakes now and get rid of them so you don’t have to causing some of them to say that even with the thorough repeat the cycle? selection process, the experiment didn’t really work. RW: How would you explain the importance of these NLP practitioners used to say that if one person can do changes to some of our readers that might be new to something, then anyone else can do it too. I’m one of the this type of transformation work, both for themselves and few people, however, to have done a massive modeling their trading? project in just one area – trading (others include Robert Dr. Tharp: There are 7 billion people in the world and 7 Dilts who has modeled leadership and L. Michael Hall billion paths to awakening. Most people start by taking the who has modeled coaching). When you have created a knowledge path of awakening. But for me, things that I massive modeling project for a particular field, you realize didn’t get at all at first, now seem like second nature. A that someone has to change their identity and take on a Course of Miracles for example said “its all an illusion”. new role. An identity such as that of a trader, a leader, or I studied the course for four years and I still didn’t a coach. I’m now pretty convinced that not everyone can understand that statement. It wasn’t until I read Gary become a great trader, in large part because they are not Renard’s book (The Disappearance of the Universe) that it willing to change who they believe themselves to be. The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 37
RW: In your own extensive research, including the various game in a way which no one would want to trade in trader psychometric tests that you have developed (e.g. real life. the Tharp Trader Test) and happiness tests, what certain • The game is about expectancy and probability. qualities do good traders tend to have that others don’t? Dr. Tharp: Sure, it’s similar in some ways to the Myers Briggs test. Probably 90% of the people in our Super Trader program are NTJ profiles using Myers Briggs. N means they are intuitive. They tend to focus more on the big picture than the details. T means that they are thinkers (in their head) rather than feelers (in their body). Most of our traders are men and 75% of all men are Ts (thinkers) while 75% of women are Fs (feelers). The last one is J (for judgers versus P or perceivers). Perceivers tend to get caught up in what they are doing (lost in the moment so to speak; and as a result, they are much more likely to be compulsive gamblers). Judgers tend to be more organizers. These principles, however, are just general observations. I also find that people who are likely to succeed tend to be committed, willing to work on themselves, and good at math, they understand basic statistics and simple probabilities. They are also good at strategy type games. If I were recruiting good traders, I’d search for these qualities.
RW: One of my absolute favourite practical exercises in your Peak 101 workshop was the playing marble game, based on risk and money management techniques. Although, our team were in the lead for the majority of the game, we unfortunately slipped into second position after being perhaps too conservative after compounding a big win. It was during that experience, I learnt first hand that fear aversion was not only something that traders feel when their position is under water, but also when they are making money, and at risk of being too greedy. How important is it for traders to find that “zero state”, and what would you say are the biggest lessons that students can learn from the game? Dr. Tharp: Okay, you’ve asked another question that could take at least a book chapter to answer but let me try to be brief. First, we play games in workshops because I believe that games reflect behavior and that people learn best through experiences. There might only be a few dollars at stake in a game, but people have the same experiences in the games as in real-life trading, so they can learn a lot without risking a large amount of money. In the game, I want people to develop their awareness. I want people to become aware of their thoughts and feelings while they are playing the game. Acknowledging and identifying what you are experiencing helps you improve your awareness. When you become aware of the beliefs that shape your experience and the feelings that might be linked to those beliefs, then you are in a position to gain control over the situation. This is probably the major lesson —awareness. But there are hundreds of lessons to learn about trading in that game. Let me tell you a few more • Objectives are all important. • You achieve your objectives through your position sizing strategies. You can do that even trading in a
• There is a huge edge in the game that would bankrupt any casino (and finding such edges is one secret of great trading). • There are actually two games going on with different expectancies (as happens in real life for money managers). • You need to plan ahead of time when you might want to change your rules. • Worst case scenario planning is essential to survival. • Having objectives and a good plan tends to lessen the effects of emotions in trading. • You probably have a lot of non-useful beliefs at work in the game that impact your real-life trading. And as I said, there are many more.
RW: Can you tell our readers who might be relatively new to your work, about the Super Trader program? What was the inspiration behind its founding? How much of a transformational experience has it been for students over the years, and what is your vision for this advanced training program in the future? For example, I understand that you have a goal for Super Traders to reach of a state where consciousness is permanently over 600 on the Hawkins scale. Dr. Tharp: I (and all my staff members) experience great joy out from helping people transform. Transformation through a trading metaphor is why I have my business and why I created the Super Trader program. What does that look like? There are a number of variations but, say someone who is unhappy being a doctor comes to us and they want to become a trader instead. The beginning lessons for the Super Trader program require that they initiate some massive personal transformations. I want to help them change their belief structure and their brain enough to produce a massive shift in consciousness. I referred to the Hawkins scale which we use to help people understand the process of transformation. Dr. David Hawkins developed his useful model of consciousness and said his scale correlated highly with happiness. We don’t measure consciousness but we do measure happiness. We have a test that scores happiness from minus 55 (depressed and probably non-functional) to plus 85 (happy all the time for no reason). Much of the time, people start the Super Trader program with a score of plus 20 or less. By the time they complete the transformational phase of the program, they have increased their happiness score to at least 60 and they are pretty much happy for no reason. So sometimes, our unhappy doctor has become a happy doctor in the process and no longer has the desire to be a trader— but I’m okay with that. Most of our Super Traders continue on, however, and trade after doing more work. During the second phase of the program I want people in the program to develop a handbook for their trading business.
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able to trade at 95% efficiency and better. Making the program better just makes working on it more fun for me.
RW: The Van Tharp Institute is continuing to engage on a worldwide tour, bringing the best of your workshops across Europe and Asia. I understand that you have already firmed up plans for 2018. For those interested in attending, please share what key experiences you feel people can expect to learn?
I think this is the most important document that any trader can have. It’s a blueprint for building a successful trading business and we actually have a Blueprint Workshop that teaches much of what should be in the handbook. In this phase, they develop their business objectives and apply all of the important lessons learned in the trading game to their trading plans. In the third phase of the program, Super Traders develop three trading systems that fit them and that work in different market types. Once they have these, they then need to prove to me that they can trade their systems at 95% efficiency for more than 100 trades. By this I mean that they make no more than one mistake in 20 trades. Mistake free trading is critical to succeeding as a trader. Someone could actually develop a whole coaching program based upon the idea of reducing mistakes. One professional trader told me, “You’ve shown me how I can manufacture R in my trades just through minimizing mistakes. That’s a huge edge.” And you asked about my vision for the future of it. I’m pretty happy that we are meeting our objectives of helping people really transform their lives and achieve massive increases in happiness. One of my goals is to continue to make the program more and more effective where more people get massive transformations and more people are
Dr. Tharp: Our plans for 2018 include presenting workshops on nearly a monthly basis at our North Carolina learning center in the US. In March 2018, I will travel to Sydney, Australia where we will present our Peak Performance 101 workshop, our How to Develop Trading Systems Workshop, and our Infinite Wealth Workshop. October 2018 we plan to return to London to present three workshops. We will teach the Peak Performance 101 Workshop again (a prerequisite for most of our other psychology workshops). For the first time in the UK, we will teach Peak 203, our Happiness Workshop (which requires attending Peak 101 first). My goal in this workshop is to raise each person’s happiness score about 20-25 points over the three days. We are usually successful because I designed a lot of exercises and processes to help people develop a lot of awareness and remove a lot of self-sabotage. That workshop dramatically changes people’s lives. Lastly, we will teach our Blueprint Workshop which helps people understand and plan for everything that’s required for a successful trading business. This is the workshop I mentioned earlier that helps you develop a very thorough business handbook which will include a lot of areas which most traders don’t even consider when they think about a trading business. To help people understand the outcomes intended for each workshop, here are the objectives from each of the workbooks:
Peak Performance 101 Learning Objectives:
• Learn the ingredients of success: mental states, beliefs, and mental strategies. • Learn how to change your mental state at will. • Learn how to examine your beliefs to determine if they are useful and in what context.
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• Learn the tasks of trading well that separate the best traders from the average traders and help you eliminate mistakes. • Learn awareness through the trading game we play (plus the many other lessons outlined). • Learn to overcome inner conflict (a exercise in avoiding self-sabotage). • Learn how to release stored feelings so that the charged emotions to no lock non-useful beliefs in place.
Happiness Course (Peak Performance 203)
The primary objective of this course is to understanding that your natural state is joy and that if you are not there, then some self-sabotage is going on inside you.
• We then help you discover that that self-sabotage might be so that you can overcome it. • In addition, we introduce people to a number of techniques to increase their happiness and their awareness.
• We help you learn awareness of shadow parts, disowned parts that really cause great self-sabotage. Our goal is for you to overcome at least 20 such influences in your life in this workshop and the subsequent homework. • We give you a six-week program to take home with you to both improve your happiness and your trading profitability.
Finally, our Blueprint workshop has the following objectives:
• Day One focuses on your overall business and developing appropriate plans and strategies for that. • Day Two focuses on systems and how to design systems that fit you and different market types. • And Day Three focuses on psychology so that you better understand yourself, and overcome your self-sabotage. • But the overall workshop is to help students 1) learn about all aspects of trading success and what is really involved and 2) develop a business handbook that will become your best friend as a trader.
Interview: November 2017, London
Supertrader Summit 2016
We invite you to read the interview with SAMT VP Mario Valentino Guffanti and Dr. Tharp on his book, Trading Beyond the Matrix: The Red Pill For Traders, which appeared in the Winter 2013 edition of The Swiss Technical Analysis Journal.
Ron William, CMT, MSTA, is a market strategist, educator and trader, with 18 years of financial industry experience, working for leading economic research and institutional firms; producing macro research and trading strategies. He specializes in macro, semi-discretionary analysis, driven by cycles and proprietary timing models. Ron is a board member of the International Federation of Technical Analysts (IFTA), part of their education committee,Vice President & Head of the Geneva Chapter of the Swiss Association of Market Technicians (SAMT) and Honorary member of the Egyptian Society of Technical Analysts (ESTA); holding both the MSTA and CMT professional designations. He is also co-founder of the SAMT CFTe Immersion Course and SAMT Journal.
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Investors Make Bottoms; Traders Make Tops An Analysis of Technical Indicators that Show How the Attitudes and Activities of Stock Market Participants Aid Forecasting Philip J. Roth, CMT PREFACE My intention is not to defend or deny the theories of “Behavioral Finance”. It has long seemed a matter of certainty to technicians that the notion of “Random Walk” has been refuted and that fundamental analysis is inadequate; hence, to us it was a matter of time before academia would be searching for new explanations of stock price movements. Chan, Jegadeeish, and Lakonishok wrote in 1999: “What has caught the attention of many investors recently is the predictability of price changes over intermediate horizons (three months to a year). In the medium term, stock prices exhibit momentum (continuation in a price direction). So, for these horizons, what goes up tends to keep rising and vice versa.”1 Some equity market participants believe that stocks trend because information comes to the market place incrementally or that investors only act on the information incrementally. However, technicians are primarily concerned with the observation that stocks do trend. Prechter and Parker wrote: “In finance, uncertainty about valuations by other homogeneous agents induces unconscious, non-rational herding, which follows endogenously regulated financial fluctuations. This dynamic produces non-mean-reverting dynamism in financial markets, not equilibrium.”2 It is my intention to show that many technical indicators are, indeed, behavioral.
Good news for technical analysis has come in the form of supportive academic studies of those trend and momentum indicators. Such research goes back at least to 1989 when Andrew Lo and Craig MacKinlay showed with a “simple” statistical test that “Stock Market Prices Do Not Follow Random Walks…”.3 Subsequent research has found “information” (in the statistical sense) in stock market trading volume ( Lo and Mamaysky, 2000)4, support and resistance, including “stickiness” at round numbers and previous significant new highs and lows (Osler, 2000, 2001 and 2003; Mizrach and Weerts, 2007)5, moving average trading rules (Papailias and Tomakos, 2011)6, patterns and trends (Lo, Mamaysky, and Wang, 2000; Osler and Chang, 1995; Savin, Weller, and Zvingelis, 2007; Weller, Friesen, and Dunham, 2007)7, momentum indicators, specifically RSI and MACD (Lachhwani, Hitendra, and Khodiyar, 2013)8, and Elliott wave analysis (Magazzino, Mele, and Prisco, 2012)9. Magazzino, Mele, and Prisco wrote: “The major findings of our analysis show that, in the case of turbulent financial markets (such as we have been experiencing) technical analysis and the Elliott’s theory adequately reflect the realities of the financial markets”.
Professors Lo, Mamaysky, and Wang wrote after testing ten technical patterns: “While human judgment is still superior to most computational algorithms in the area of visual pattern recognition, recent advances in statistical learning theory have had successful applications in fingerprint identification, handwriting analysis, and face recognition. Technical analysis may well be the next frontier for such methods.…for NYSE/AMEX stocks, 5 of the 10 patterns HS, BBOT, RTOP, RBOT, and DTOP, yield statistically significant test results…”10
Technical Analysis is based on three premises, which those studies support. They are: (1) Information is absorbed incrementally in the market place; it is not “efficient”. Hence, (2) stocks trend; it is not a “random walk”. And, (3) those trends are, in the words of Robert Prechter: “probabilistically determinable”. After writing his book of conversations with technical analysts (Heretics of Finance, 2010)11, MIT professor Andrew Lo said in a presentation to the Market Technicians Association he had a hard time trying to quantify the technical indicators, but he was convinced traders were seeing something useful in the charts and he believed the computers may eventually figure it out. It’s like facial or pattern recognition. Humans have little trouble distinguishing a cat from a dog, but teaching a computer to do that has proven to be very difficult (albeit, probably not impossible) .
Long before fundamental analysis was codified and no one had heard of “quantitative analysis”, excessive valuations were noted in markets. In 1841 Charles Mackay wrote about the South-Sea Bubble in London, which occurred in 1711, and Tulip Mania in Holland, which occurred in 163714. Those, of course, were examples of “irrational exuberance”, centuries before the 1990s, which prompted Alan Greenspan to coin that phrase15 and Robert Shiller to write a book with that title16.
Technicians have called behavioral indicators “sentiment” or “psycho-logical” indicators, and “supply/demand” or “money flow” indicators. I will not be concerned with such aspects of Behavioral Finance as the endowment effect, loss aversion, anchoring, etc.12 I will only be concerned with optimism and pessimism on equities per se. In other words, I am interested in who is bullish or bearish, and how bullish the bulls are and how bearish the bears are, not why they may be bullish or bearish. I will refer to “stock” most of the time in this discussion, but the theory applies to anything that trades continuously in an open market, such as bonds, commodities, options, futures, and currencies.
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INTRODUCTION By now most equity market participants have learned about the notions of over-popularity and over-pessimism. They have read “too much bullishness” after big advances. One such example is the book, Dow Jones 40,000 by David Elias. This book was published in June 1999, after nearly two decades of rising stock prices, and only six to nine months before the end of the secular bull market, and before ten years of no price progress, interspersed with two lengthy, deep bear markets. People have read about “too much pessimism” after big declines. An example was the famous (infamous?) “Death of Equities” cover of Business Week magazine on August 13, 1979.17 The bearish article followed a decade and a half of no price progress in stocks. In fact, it appeared near the lows for the next three years and was followed by the great secular bull market of the 1980s and 1990s. However, those are anecdotal examples and they imply “everyone” (I exaggerate, of course; “most participants” is what I mean) is too bullish at tops and too bearish at bottoms. This is not the case, nor can it be the case. Someone is selling the stock that traders can’t get enough of late in bull markets and someone is buying the stock traders are disgorging indiscriminately late in bear markets. My sentiment (i.e., behavioral) indica-tors are of two types: those that deal with equity market participants who have short term horizons and those that deal with equity market participants who have long term horizons.18 I have found that “investors make bottoms” and “traders make tops”. That is not to say investors are smart and traders are dumb. There is such a thing as “rational exuberance”. Both savvy investors and savvy traders can and do show good results. But they are motivated by different things and dominate the buying and selling in the market at different times. Hence, my sentiment analysis is not about “contrary opinion”, unless of course, we make it clear who to be contrary to and when to be contrary.
During lengthy trends most equity market participants get the message and are on the right side much of the time. However, near turning points we usually find traders on the wrong side for a while and investors on the right side. In fact, I define a bottom in the market to be that period when stocks are stabilizing after a lengthy decline and investors are buying stocks from traders, and I define a top in the market to be that period when stocks are faltering after a lengthy advance and investors are selling stocks to traders. Of course, there is no way to know what all investors and what all traders are doing, but we do have indicators that suggest what the majority are doing. Why should it be that traders are often on the wrong side at turning points and investors are usually on the right side? Investors are motivated by price and value. As a result, they get more bullish and more active on the buy side when prices are declining and when stocks are “low”. The opposite is also true; they get more bearish and more active on the sell side when prices are rising and stocks are “high”. Traders are usually not concerned with value; they are motivated by trends.
When stocks have been rising traders get more bullish; when stocks have been declining traders get more bearish. Traders (I am including professional buy-side and sellside traders, as well as individuals trading for their own accounts) cannot recognize a final high price as it occurs. In fact, technicians go to great lengths to emphasize that a “top”, a reversal of an advance, is not a point in time; it is a process that entails a series of weakening rallies and growing declines. As the process of a top, a distribution pattern, develops, traders will begin to get the message and move from a bullish posture to a bearish one. Investors who have been bearish, who have been supplying stock, will continue to do so, because prices are still “high”. The supply/demand balance will be tipping to the negative side. A bottom, of course, is the opposite process. Traders, who have been bearish because of declining stock prices, need some time to recognize a stabilization pattern becoming a base, an accumulation pattern. Investors who have been bullish, who have been accumulating stocks, will continue to do so, because prices are “low”. The supply/demand balance will be tipping to the positive side.
Therefore, importantly, my sentiment indicators distinguish investor attitudes and actions from trader attitudes and actions. “Attitude” indicators include polls and anecdotal information, i.e., what participants are saying. “Action” indicators are transactional measures, i.e., what participants are doing. Together, indicators of attitudes and actions form the framework for what market technicians call “Sentiment Analysis”, which I believe is a key element in behavioral economics or behavioral finance. I will start with the longest term indicators and end with the shortest term. While we have all heard many times that technical analysis is “arcane”, or worse, “divination”, I believe sentiment analysis is reasonable and obvious to knowledgeable market participants. When I have asked my students, “Whose advice and actions would you rather follow, Warren Buffet or the famous hedge fund manager they just saw on CNBC”, they always picked Warren, the consummate successful long term investor. And most of them took my course because they wanted to learn how to become a good trader.
Sentiment indicators cannot be used in isolation from market movements. Sentiment indicators are useful only with respect to the trend. During much of a trend, up or down, most people get the message and are on the right side. Too much (trader) optimism is only a negative when price progress is struggling after an advance (and investors are supplying stock). The opposite is also true. If you bought the first time you heard “There’s too much pessimism” in a bear market, you would probably be buying in the middle of the decline. The late money manager, Martin Zweig, was known for saying, “Don’t fight market momentum and don’t fight the Fed”, simple but excellent advice. We would add “Don’t ignore the buying and selling proclivities of investors and traders, especially when prices have stalled after lengthy rises or falls.
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PART ONE Very Long Term (i.e., Secular Trend) Indicators
I have found that different sentiment and supply-demand indicators are useful for different time horizons. Indeed, the very long term indicators are useless for short term horizons. The indicators we follow are useful for the very long term (the secular trend, the trend that encompasses two or more economic cycles), the long term, (the cyclical trend; one complete bull-bear cycle), and the medium term, (intermediate term, usually one leg of a bull or bear cycle, often three to six months). Sentiment indicators are usually not helpful for the short term (near term, from a few weeks to a few months) and are unlikely to be useful at all for the very short term, such as day trading. On rare occasions, however, very extreme readings in medium term indicators may be helpful for the short term.
1. Our first indicator is the most basic one, the Net Change in Supply of Stock.19 The data, derived from the Federal Reserve Flow of Funds series (FRB FOF, table F.4, line 12), is quarterly data, at a seasonally-adjusted annual rate (SAAR)20. The data is released with about two-and-a-half month delay. With just four data points a year and a long lag it is obviously only useful for a very long term perspective. What does it tell us? When the series is on the plus side, it means new equity financing (IPO’s plus new stock sold by existing public companies) exceeds corporate buybacks. Who is perhaps the most important long term investor? The corporation itself. Corporate activity in its own stock is by its nature a very long indicator. When a corporation comes into the market to buy back its own stock, it is almost a permanent reduction in supply. Corporations’ action in their own stocks is investment activity. Chart 1 shows that the market tends to do relatively well when buybacks exceed new offerings. The supply of stock is decreasing. The second half of the 1990s and 2004-2007 are great examples. The market tends to do relatively poorly when new equity financing exceeds buybacks. The supply of stock is increasing. 20012002 is a good example. Net corporate investment demand is bullish and net corporate supply is bearish. It is Economics 101. Keep the demand for something fixed and reduce its supply. What happens to the price? It rises. Keep the demand for something fixed and increase its supply. What happens to its price? It falls. The Flow of Funds reports are also useful in determining whether individuals and institutions own “a lot of stock” or “a little bit of stock”, compared to other financial assets. Chart 2 shows the value of financial assets for Households20 and the equity percent (equities plus mutual funds) of those financial assets (FRB FOF, Series L. 100, lines 1, 18, and 19), compared to the S&P 500. The Federal Reserve’s Household series is a proxy for the public. It is not exactly the public, since foundations are included in the totals; nevertheless, it is a useful gauge. Note that the secular bull market in the 1980s-1990s started with a “low” equity percentage (13%-15%) and ended with a “high” percentage (30%33%). The bear cycles of 2000-2002 and 2007-2009 resulted in retracements
Chart 1 Flow of Funds: Net Change in Supply of Stock
Chart 2 Flow of Funds: Households
All the charts in this paper have been produced from my personal data, made available to the Market Technicians Association Educational Foundation and its educational programs.
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Chart 3 Flow of Funds: State & Local Government Employee Retirement Funds
in the equity percentage of no more than half the rise and the post-2009 bull market has taken the percentage back close to the highs. Assuming that Households are long term investors (much of the public financial assets are held in 401K and IRA retirement accounts), we interpret the equity percent series to be a limiting factor to equity potential. In the 1980s-1990s the great stock market rise was a function of a lot of new money coming into stocks and money flowing from fixed income investments into stocks. Therefore, this class of investors is probably less of a demand force: new cash flow is still a factor, but asset switching (from fixed income investment to equities) is probably much less of a factor. This indicator belies the popular notion that the public has been eschewing stock. 2. Institutional categories give a similar message. Chart 3 is the series for State & Local Government Employee Retirement Funds20 (FRB FOF, L. 118, lines 1, 13, and 14). It shows that the equity percent (equities plus mutual funds) of financial assets soared from below 20% to above 80% in the 1980s-1990s and that percentage stands at 52% now. We draw the same conclusion as for Households, namely that a great secular bull seems unlikely with this class of investors never having shown a complete washout from a “high” percentage of financial assets in equities. The series for Private Pension Funds (FRB FOF, L.117, lines 1, 12, and 13), Life Insurance Companies (FRB FOF, L.115, lines 1, 13, and 14), and Property-Casualty Insurance Companies (FRB FOF L.114, lines 1, 12, and 13) look similar. This is not to say stocks can’t have cyclical bull markets with such a background, but a secular rise like the 1980s-1990s seem unlikely, with important classes of investors already highly committed to equities.21 Since there are only 141 observations and the data lag by up to two-and-a-half months, testing revealed no predictive power for the total financial asset series. The equity percent would be “predictive” if there were no lag. The equity percent predicts the next quarter returns (the coefficient for chgep = 0.01897, significant at the 1% level).
PART TWO LONG TERM (I.E., CYCLICAL TREND) INDICATORS A. International Flows
Indicators in Part One dealt with the change in total supply of stock and equity allocations, indicators useful for secular trends. In Part Two I will discuss measures that deal with flows and valuations, indicators useful for the cyclical trend, the long term trend. The long term trend is the trend associated with the economic cycle, usually four-to-five years. The first series of three charts deal with international flows. The data come from the U.S Foreign Activity Report from SIFMA, the Securities Industry and Financial Markets Association22. SIFMA refers to U.S. Investors and Foreign Investors. Those so-called investors act like traders, however. The data show the participants seem more motivated by trend, than value. The biggest net buying has occurred late in uptrends, while actual net selling has been seen near major market bottoms. It seems that the further participants are removed from markets the more likely they are motivated by the previous market action, i.e., they are more likely to be trend followers than value seekers.
Chart 4 is net foreign buying of U.S. equities. Foreigners are more tradingoriented in the U.S. market than U.S. participants are in the foreign markets (Chart 5). Foreigners barely did any buying in the U.S. 1980s-1990s secular bull
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market until the last couple of years. They were sellers near the market lows in 2002. Foreign net buying has been erratic since the 2007 market top. The spates of selling in recent years during the post-first-quarter-2009 bull market by these trading-oriented participants is one argument for extending the life of the rise. That is, we expect to see traders very optimistic and, accordingly, sustained buying late in a lengthy advance.
Chart 5 shows U.S. buying of foreign stocks for the last 34 years. Note that U.S. net buying was virtually non-existent in the 1980s bull market and, except between mid 1998 and mid 1999 (which encompassed a sizable market correction), buying was greatest after the mid 1990s. That sustained net buying was a record at the time. Buying and selling were in balance in the 2000-2002 bear market, with the biggest selling occurring near the lows in the third quarter of 2002. Post-2002, the figures show the biggest buying just before the market highs in 2007 (the four quarters from the fourth quarter of 2006 through the third quarter of 2007) and record selling in the fourth quarter of 2008 near the market lows. So far, in the current up cycle the biggest buying was in the first quarter of 2013, after nearly four years of market rise, and the second quarter 2013 net buying was the fifth largest ever. Chart 6 is the most interesting of the three. It shows net purchases of U.S. stocks by foreigners minus net purchases of foreign stocks by U.S. participants, “Net International Flows” into the U.S. Market. Since foreign buying and selling the U.S. market dwarfs U.S. activity in the foreign markets, this chart has the same shape as Chart 4, but it gives a clearer picture of excess. It shows sizable net outflows from the U.S. market until 1997 and record (at the time) net inflows just prior to the market top in 2000. It shows net outflows at the market bottom in 2003 and little net inflows until the 2007 market top. There have been no sustained inflows in the post-first-quarter 2009 bull market and there were record outflows in the first half of 2013. When we compare international flows in the last decade to previous decades it seems clear that traders (i.e., trend followers), especially U.S. participants, are dominating the flows and investors (i.e., longer-term, valuemotivated participants) are reticent. I interpret that to mean we should expect the market to look more like the post-2000 experience, rather than the 1980s-1990s experience. Bigger bulls and bigger bears occur when traders produce the advances and declines, and investors are not very active. That is, little investment selling in the face of trader demand and little investment buying in the face of trader liquidation results in bigger swings.
Chart 4 Net Purchases of U.S. Equities by Foreign Investors
Chart 5 Net Purchases of Foreign Equities by U.S. Investors
Chart 6 Net Purchases of U.S. Equities by Foreign Investors MINUS Net Purchases of Foreign Equities by U.S. Investors
This quarterly data, like the Flow-of-Fundsderived indicators, did not lend itself to statistical testing. There are not enough data points. Nevertheless, I stand by the notion
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that international flows do provide useful insight into equity demand.
B. New Supply
Chart 7 New Equity Financing
The very first chart was the Net Change in the Supply of Stock. The data is quarterly with a lag of two-and-a-half months. It would certainly be useful to have more current information. The “New Equity Financing” series, while just the supply half of the “Net Change in Supply of Stock”, is available monthly with less than a two-week lag, from Source Media (www.sourcemedia.com). New Equity Financing includes initial public offerings (IPO’s), as well as new stock sold by existing public corporation. Chart 7 shows the monthly total and a moving 12-month total, compared to the S&P 500. The 12-month total mirrors the S&P 500, answering the question, “When do most IPO’s come out, at bottoms or tops?” The monthly totals almost always peak just before market peaks and show near zero or zero readings at market bottoms. There is a secular rise in the data and it is not normalized because I want the magnitudes to be clear. When prices are elevated corporations (market participants with long term horizons) want to sell new stock and are able to do it because the outside investors and traders have increased appetites for stock. I note that the longer an advance persists, the more the demand comes from speculative sources (i.e., traders, short-term-oriented participants. See “Margin Debt”). After protracted market declines corporations do not want to sell new stock (i.e., they perceive their stock to be “too cheap”) and speculators are not inclined to buy down-trending stocks. Note the low levels of new equity financing in 1990, 1998, 2001-2002, and 2008-2009, with most of the lowest readings occurring at or near the ends of major declines. Even 1994, which was essentially a sideways correction resulted in near zero readings near the end. Note the very high readings (i.e., the peaks in the 12-month total) in mid 1983, mid 1987, early 1994, mid 1998, and early 2000. The only exception of sizable new equity financing occurring during a major decline and early in a major advance was during the 2007-2009 bear market and in the post-2009 bull market; this was because of the major recapitalization in the banking industry imposed by the Fed in the aftermath of the financial crisis. Testing showed the data to be useful. A regression of the next 3-month and 12-month returns revealed negative coefficients for both (i.e., returns were lower following higher new equity financing), although the 3-month return was not statistically significant (p = 0.112).
The 12-month results were better, with the coefficient significant at the 5% level (p = 0.037), with a higher R-squared.
“Secondary Distributions” is a smaller series than “New Equity Financing”, but even more current. It is weekly data with little lag. My source for “Secondary Distributions” is Barron’s weekly financial publication, which gets it from Dealogic LLC, New York City23. Secondary distributions are often used by major stockholders to eliminate or reduce holdings, i.e., “investment liquidation”. Chart 8 shows a 52-week moving average of secondary distributions, compared to the NYSE Composite Index. With one big anomaly, 2008, the chart mirrors the market, with a very low reading just after the 2003 market low and very high readings near market tops. The anomaly is 2008-2009, when, similar to the 46 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
“New Equity Financing” data, financial companies were forced to finance in a depressed market. Dealogic evidently classified some of the bank financing as “secondary offerings” rather than “primary offerings”. Aside from the 2008-2009 experience, the message is clear: investment activity (in this case, corporations’ activity in their own stock) shows liquidation at high prices, usually against a strong trend.
Chart 8 Secondary Distributions: Value
C. Speculative Demand
Margin debt is a measure of speculative demand. Traders, professionals (including hedge funds) as well as the public, often execute their trades on margin, using the leverage to increase their exposure. While a portion of margin debt does reflect short selling, the bulk of the movement in margin debt is a function of traders expanding and contracting long positions. Since traders are motivated by the trend (my premise), I expect to see this series mirror the market, with “low” readings near market bottoms and “high” readings near market tops. Chart 9 shows the market value of margin debt and margin debt as a percent of market value, compared to a proxy for total market value, the Dow Jones U.S. Total Stock Market Index.24 The total margin debt series and margin debt as a percent of market value both show a secular rise. We expect to see the margin debt percent to rise more sharply late in a market up-cycle, reflecting increased speculative demand.25 In the period from 1950s through the 1980s (not shown in the chart) the margin debt percent tended to move between 1% and 2% most of the time, troughing near the highs of bull markets and peaking close to the lows of bear markets, the performance I would expect. Since the 1990s, the margin debt percent peaks were 1.96 in February 2000 and 2.60 in July 2007 (the high so far in the advance from the March 2009 low was in April 2013). We note that the low after the 2007 peak was a 1.94 in July 2012, just under the 2009 low of 1.97, registered in July 2009.
D. Investment Demand
Insider activity represents investment demand (and supply). Insiders, who acquire the bulk of their stock through new incorporations, rarely buy in the open market. As a result, there is almost always more insider selling than insider buying, with an average of more than four times as many sales as purchases in the last 10 years. But when they do buy in the open market, it is usually at a “low” price. Insiders, by law, are longer term investors; if they take a short term profit, they must return it. Chart 10 is an 8-week moving average of the sell/buy ratio of insider transactions, compared to the S&P 500 (red line).26 The bearish and bullish lines are 1 standard deviation above and below the 10-year average. With the 10-year average rising from below 2.00 to above 4.00 since 2000,
Chart 9 Margin Debt versus % of Market Value
Chart 10 Insider Activity
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the bullish line has risen from under 1.00 to almost 2.00 and the bearish line has risen from under 3.00 to over 6.00. Hence, the indicator does not have to fall below 1.00 (i.e., actual net insider buying on an 8-week basis) for bullish readings. Note that the indicator is based on insider transactions, not shares or value. The reason is the number of insiders is considered more important. That is, 10 insiders buying 10,000 shares each is more significant than 1 insider buying 100,000 shares. All the low points are near medium term or major lows. Bullish or near bullish readings were registered in the fall of 2001 (an interim low in the 2000-2002 bear market), late 2002-early 2003 (a major low), late 2008-early 2009 (actual net buying at a major low), and the fall of 2011 (an interim low in the post-2009 bull market). There is one anomaly, with net buying at “high” prices in early 2000, a function of the huge number of call options granted in the 1990s and the scramble to exercise them prior to expiration. The bearish extremes are usually just prior to medium term or major tops. Clearly, these investors tend to be on the right side during reversal periods.27 I tested the data by regressing future 8-week returns for the S&P 500 on two dummy variables. The first dummy (BULL) is 1 if the 8-week reading is below the bullish threshold and 0 otherwise. The second dummy (BEAR) is 1 if the 8-week reading is above the bearish level and 0 otherwise. Hence, the formula is:
The results showed average 8-week returns of 2.9% for BULL and -1.3% for BEAR. Both coefficients are statistically significant at the 1% level, very good. BULL is more than twice as effective as BEAR, conforming to my belief that insider net buying is rare and usually bullish when it occurs, while insider selling is normal and less interesting.
E. Potential Demand
Chart 11 Individual Buying Power Index
“Potential Demand” is buying power on the sidelines and funds in other financial assets, notably bonds. Part One discussed the equity part of the equation. In this section I look at cash and money market fund assets as a source of demand for stock. I have created an ‘Individual Buying Power Index” from the Federal Reserve Flow of Funds statistics, using the Households and Nonprofit Organizations (L.100) as a proxy for individuals. Individual Buying Power is money market fund assets (Line 6) divided by money market fund assets Line 6) plus equities (Line 18) plus mutual funds (Line 19).19 Bonds are not included in the formula under the assumption that a shift in asset allocations (See Part One, Section 2) is a different decision than putting ready cash to work. Logically one would expect buying power to be highest near major market bottom areas and lowest near major market top areas and that appears to be the case. The secular rise in the buying power index is a function of the secular rise in money market fund assets as public “investment” funds were moved from stocks and bonds. Nevertheless, Chart 11 shows rising cash into the market bottoms in 1982, 2002-2003, and 2008-2009, and cash falling into the 1999-2000 and 2007 tops. The 15.84% reading in March 2009 was a record, well above the 13.00% reading of September 2002, near the beginning of the 2002-2003 market bottom. After declining steadily (and expectably) since the 2008-2009 market bottom, the latest available reading of 5.34% in mid 2013 is the lowest since mid 1984. On the margin, there appears to be “public investment” selling at “low” prices and “public investment” buying at “high” prices, contrary to my belief that investors are usually on the
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right side near market turning points, but that activity is probably dominated by professionally-managed foundations and some public trading. The vast bulk of the money, 85% to 95%, remains in stock. Note that the biggest shifts in the buying power index occur in the months right before and after major market lows when traders (i.e., short-term-oriented market participants) panic out and rush back into stocks.
Chart 12 Equity Mutual Funds: Cash % of Net Assets
F. Potential and Actual Demand Through Mutual Funds
Charts 12 to 18 are derived from the monthly Research and Statistics report from the Investment Company Institute (www.ici. org).28 The data show cash and cash flows for equity mutual funds. The bulk of the buying and selling in equity mutual funds is investment (i.e., long term) activity, but just as I noted for the Individual Buying Power above, there does appear to be some trading (i.e., short term) activity. In other words, while the bulk of equity mutual fund activity is conducted for long term objectives (demand for IRA’s and 401k’s, for example), a small portion is trading activity, usually by traders liquidating stock during bear markets and replacing stock during bull markets. Chart 12 is cash as a percent of net assets, compared to the S&P 500. The chart shows rises and falls during bear and bull markets, as one would expect. The cash percentage generally swung from 5%-6% at market tops to 12%13% at market bottoms in the 1970, and 1980s, with earlier data in the 1950s similar.29 There is one big anomaly. Cash fell from 12.93% in October 1990 to 4.04% in March 2000, but since then at market bottoms the figure has been no higher than 5.90% (in February 2009, during the 2008-2009 market bottom). Fund managers have been “directed” by superiors to stay much more fully invested and investors have to make their own decisions to raise cash or not, by switching from equity mutual funds to money market funds. Nevertheless, the data still show “high” cash at market bottoms and “low” cash at market tops, in a new range of 5½% to 6% at market bottoms and 3% to 3½% at market tops. It troughed at 3.5% during the mid 2007 market top and has been as low as 3.3% in 2013. Cash on hand is important, but cash flows are even more important. Most of the time cash flows into and out of equity mutual funds represent public investment activity. Chart 13 shows sales (including reinvested dividends) and redemptions for equity mutual funds, compared to the S&P 500, and Chart 14 shows net sales (sales including reinvested dividends minus redemptions plus net issuance of exchange-traded funds, ETFs), compared to the S&P 500. Both sales and redemptions were in a secular rise in the 1990s (and earlier data shows a secular rise in the 1950s-1980s as well). All three series, sales (including reinvested dividends), redemptions, and net sales (including
Chart 13 Sales (including Reinvested Dividends) and Redemption of Equity Mutual Funds
Chart 14 Net Sales of Equity Mutual Funds (Including Reinvested Dividends) (Net issuance of ETFs after 12-07)
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Chart 15 Net Exchanges into Equity Mutual Funds
Chart 16 Equity Mutual Funds: Total Flows (including ETFs as of 01-08)
Chart 17 Equity Mutual Funds: Net Purchases of Common Stock
reinvested dividends plus net issuance of ETFs) have flattened out since the 2000 market top. Importantly, since the 2007 market top the net sales has barely been positive, erratically swing from positive to negative. Chart 14 also belies the notion that ETF demand has offset the sluggish demand for traditional equity mutual funds. ETF demand (i.e., the net issuance of ETF shares), which I contend is very much the bailiwick of professional traders, has only partially offset lack-luster equity mutual fund demand. In 1994-2007, net flows into equity mutual funds, including ETFs, averaged $16.5 billion per month. In 2008-2012 (and 2013 through September), those flows averaged just $6.5 billion. Net public investment demand (through mutual funds and ETFs) in the post-2003 bull market is far below previous cycles, less than 40%. Chart 15 shows net exchanges. “Net Exchanges” is the flow from fixed income funds (bond funds and money market funds) to equity funds, a positive flow, or the flow from equity funds to fixed income funds, a negative flow. Typically, during advances, especially late in advances, positive flows are predominant, while during declines, especially late in declines, negative flows are predominant. In the up-cycles in the1990s flows were positive most of the time, but the post-2003 up-cycle does not show persistent flows into equities, a similar pattern to net sales previously discussed. However, there were four consecutive months of positive flows in 2013 (May through August), suggesting some trend-following demand.
Chart 16 is total flows into equity mutual funds: sales including reinvested dividends minus redemptions plus ETF net issuance (starting in January 2008; prior ETF data is not significant) plus net exchanges. The picture shows persistent inflows in the 1990s up-cycles and in the 2003-2007 up-cycle, but only erratic inflows in the post-2009 up-cycle. The implication is that equity mutual fund managers had sizable impact on the demand for stock in the 1990s and during the 2003-2007 up-cycle, but little impact since. Chart 17, the net purchases of common stock by equity fund managers, below, shows just that. Hence, insofar as most activity in equity mutual funds represents public investment activity, such demand has been minimal in recent years. The rest of the equity mutual fund data reflect public trading activity late in up and down cycles and professional trading in ETFs.
The last indicator in the long term (i.e., cyclical) category is valuations. Traditionally, valuations have been the purview of fundamental analysis. Certainly, for the equity market price/earnings ratios (P/E’s) have been and remain a critical input for fundamental analysts and strategists, 50 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
and to a lesser degree, dividend yields are important. For the market as a whole, fundamentalists believe that a measure of “fair value” in terms of P/E’s can be approximated by analyzing earnings growth rates and the level of interest rates. Fundamentalists would argue that valuations reflect “investment” attitudes. The market is “worth” a particular P/E multiple for each combination of growth rates and interest rates. The so-called “Greenspan Model” is one such measure, comparing the earnings yield of the S&P 500 to the rate on the 10-year treasury note.30 There are other models that include inflation in the formula. Nevertheless, there are valuation levels that cannot be explained by those “fundamental” investment formulas. During periods when traders are extremely bearish or extremely bullish, they overwhelm investment attitudes and behavior.
Chart 19 shows price/earnings multiples and yields on the S&P 500, compared to the S&P 500 index, from 1976 to 2013. Note that in the 1990s the P/E multiple rose to the mid 20s in the middle of the decade and then soared to unprecedented levels above 40 at the end of the decade. Historically, there were periods when interest rates were comparable or lower and earnings growth rates were higher, yet P/E multiples were far lower. The 1950s is one example. A clear message is that there is an important psychological component to valuations. In the early 1980s the S&P 500 P/E multiple was below 10 and its yield was over 6%; in 1999-2000 the P/E multiple was in the 30-40 range and its yield fell to barely over 1%. Why? Despite “investment” formulas that said stocks were very cheap or very expensive, “short term” participants, motivated by very weak or very strong trends, sold “cheap” stocks or bought “expensive” ones. Hence, there are periods when cheap stocks stay cheap and periods when expensive stocks get more expensive. Knowing who the traders are and what they are doing, and knowing who the investors are and what they are doing trumps the formulas.
Chart 18 S&P 500 Index Dividend Yield vs. Price/Earnings Ratio
Chart 19 ISE 10-Day Index Put/Call Ratio
PART THREE MEDIUM TERM INDICATORS A. Option Volume Indicators
By definition, participants in the options markets are making short term judgments, trading judgments. Even though they may be using options to augment a longer term strategy, the limited life of an options contract (most of the activity in the options market is in contracts that are just two or three months from expiration) means most participants are making some kind of a short term decision when they buy or sell a call or a put. I recognize that option activity includes hedging trades and not just directional trades, so the movement of put/call ratios is not just a function of market psychology. Nevertheless, on the margin, I believe shifts in put/call ratios should have a distinct sentiment component. If option traders are becoming bearish, they would be expected to be more interested in puts than calls. If option traders are becoming bullish, they would be expected to be more interested in calls than puts. That appears to be the case. Put volume increases relative to call volume The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 51
Chart 20 ISE 10-Day Equity Put/Call Ratio
Chart 21 CBOE 10-Day Index Put/Call Ratio
when trend-motivated option traders grow more bearish and put volume decreases relative call volume when option traders grow more bullish. Under my assumption that short-term-oriented equity market participants are likely to be overly bearish near lows of consequence and overly bullish near highs of consequence, put/call volume ratios should often be found high near bottoms and low near tops.31 In other words, puts garner more attention “too late” in declines and calls garner more attention “too late” in advances. Remember, this is not to say options participants cannot be successful; as a group they can be successful most of the time. However, their trend-motivated activities results in “too much” bullishness near tops and “too much” bearishness near bottoms. Much of the activity in the options markets is professional; professionals succeed by getting back on the right side quickly. The four put/call option volume charts have necessarily limited history since they are daily charts; they start at the beginning of 2011 and end in the third quarter of 2013. Earlier data is similar. I have drawn bull lines one standard deviation above oneyear averages and bear lines one standard deviation below one-year averages to give some approximation of high and low levels, not because any particular level is a clear buy or sell signal. The indicators should be used to increase or decrease confidence in the trend outlook, not to anticipate imminent reversals. The charts are 10-day moving averages, plotted along with the S&P 500. The four charts are equity options (opening transactions only) on the ISE exchange (www.ise.com/ market-data/isee-index), index options (opening transactions only) on the ISE exchange, equity options (all transactions) on the CBOE exchange (www.cboe.com/ data/mktstat.aspx), and index options (all transactions) on the CBOE. I have excluded the trading in VIX options since it can be argued that buying VIX calls is a bearish bet, not a bullish bet. Note that even though the ISE data are just open-buy-puts divided by open-buy-calls and the CBOE data also include sell-to-open calls and sell-to-open puts, the ratios are similar. The charts show that put/call ratios usually spike near bottoms, bullish readings, implying the option participants are very negative. The ratios are usually in the neutral-to-low regions during advances (i.e., the option participants are getting the uptrend message correctly). The combination of low ratios and faltering price action are bearish readings. Chart 19, the ISE 10day Index Put/Call Ratio, gives the least useful information, although the mid September 2012 very low reading occurred at the inception of a two-month market correction. The very high readings in mid July 2011 and in the first half of July in 2013 were not helpful. Chart 20, the ISE 10-Day Equity Put/Call ratio, was quite helpful, with all the very high readings occurring near important or interim market bottoms. The sustained high readings from mid August 2011 through late November 2011 coincided with the intermediate bottom in the market in 2011. The sustained very high readings between mid May 2012 and mid June 2012, and between late June 2013 and early July 2013 coincided with interim market setbacks.
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Chart 21, the CBOE 10-Day Index Put/Call Ratio, shows two clear, sustained high readings, late August 2011, at the beginning of the market basing pattern that year, and in the second half of May 2012, at the end of an interim correction. Once again, I find low index put/call ratios near interim tops, such as mid March 2012 and mid May 2013, but also in the second week of August 2012 (the middle of an upleg) and in mid December 2011 (early in an upleg).
Chart 22 CBOE 10-Day Equity Put/Call Ratio
Chart 22, the CBOE 10-Day Equity Put/Call Ratio, shows sustained high readings three times, mid June 2011 (an interim low, albeit close to a medium term market top), mid August 2011, at the beginning of a medium term market base, and the third week of May 2012, at an interim market bottom. There are low readings in late March 2012, in the third week of September 2012, an in late May 2013, all near the beginning of interim market tops. There are a series of low or relatively low readings between mid October and mid December 2013, all near minor tops, but no high readings near the interim minor bottoms in that period. I tested the put/call ratios, regressing 10-day returns for the S&P 500 on the 10-day averages for each of the four ratios, the using the Newey West (1987) procedure. The equation is:
As I expected the equity ratios showed much better results than the index ratios (which I attribute to much hedging activity in the index options and little hedging in the equity options). For both the ISE and the CBOE data, high equity put/call ratios were associated with higher next 10-day returns. For the CBOE equity put/call ratio, the coefficient is positive (0.045) and significant (p = 0.025), and the R-squared is higher than the index put/call ratio (0.0135, compared to 0.003). The results were similar for the ISE ratios.
Chart 23 Options Clearing Corp. (OCC) Equity Options Premium Put/Call Ratio (with Bull/Bear Line)
A separation of the data into four quartiles by levels of the put/call ratios also showed useful results. Low put/call ratios led to low returns and high put/call ratios led to high returns.
B. Option Premium Indicators
Option premium indicators are analogous to option volume indicators. I noted above that if options traders are bearish, put volume is expected to increase relative to call volume, and if option traders are bullish, call volume is expected to increase relative to put volume, and the data show both do occur. Likewise, traders should be expected to pay relatively more for puts than calls when they grow bearish and they should be expected to pay relatively less for puts than calls when they grow bullish. Put/call premium ratios should move similarly to put/call volume ratios, and they do. For those comparisons, I use the weekly option premium data from the Options Clearing Corporation (OCC) (www.optionsclearing.com/webapps/weekly-volume-reports). The OCC provides a put/call premium ratio for the equity options and a put/call premium ratio for the index options. Charts 23 and 24 show 4-week averages The Swiss Technical Analysis Journal â€˘ Autumn-Winter 2017 â€˘ 53
Chart 24 Options Clearing Corp. (OCC) Index Options Premium Put/Call Ratio (with Bull/Bear Line)
of the put/call premium ratios compared to the S&P 500. I have drawn bull lines one standard deviation above the 10-year average of the put/call ratios and bear lines one standard deviation below the 10-year averages. Chart 23, the equity premium ratio, shows several spikes in the 4-week average during the 2000-2002 equity bear market near interim lows. Very high readings were recorded several times during the late 2002-early 2003 basing period and again at the beginning of the late 2008-early 2009 basing period. Readings were expectably low during the steady market advance from the 2009 lows through 2013, although there was one increase to very high readings in late June-early July 2013, associated with the end of a temporary market correction. Chart 24, the index premium ratio, is similar, but gives an even clearer message. It shows spikes to high levels near interim lows during the 2000-2002 equity bear market (and one at a temporary low during the 1999 topping process) and near the lows of temporary setbacks in the post 2009 bull market. The highest readings, the very high sustained ratios were registered during the major bases in late 2002-early 2003 and late 2008-early 2009.
C. Sentiment Polls
All of the indicators I have discussed above are “transactional” indicators, indicators that measure what investors and traders are doing in the market place. There is another class of sentiment indicators, surveys of equity participants, polls that measure what those participants are saying about the equity market. As it turns out, all the polls are surveys of equity market participants with short term time horizons. Based on my assumption that short-term-oriented equity market participants, traders, are likely to be wrong at turning points, I would expect the polls to show excessive pessimism at market bottoms and excessive optimism at tops. And they usually do. I will discuss four polls that are widely followed in the financial media: the poll of stock index futures traders conducted by Consensus, Inc., the poll of stock index futures traders conducted by Market Vane, the poll of market letter writers conducted by Investors Intelligence, and the poll of investment club participants conducted by the American Association of Individual Investors.32
Consensus, Inc. and Market Vane, both polls of futures traders, have a slightly different methodology so the readings are usually slightly different. Both surveys are polls of professionals, but Market Vane weights the readings by its opinion of the impact of the pollee, while Consensus, Inc. gives equal weight. Notably, both surveys are polls of professional traders, people who make their living trading stock index futures; nevertheless, they both show “excessive” optimism at market peaks and “excessive” pessimism at market troughs. Each week the services poll the traders and report the percentage of bulls. In the last 10 years Consensus, Inc. reports an average bull reading of 55.2%, with one standard deviation of 15.5%, and Market Vane reports an average bull reading of 58.4%, with one standard deviation of 9.9%. The Consensus, Inc. data in Chart 25 shows a four-week M.A. of the bullish percentage, with a bullish line drawn in one standard deviation below the 10-year average and a bearish line drawn in one standard deviation above the 10-year average, compared to the S&P 500. As I would expect, weak markets (and, therefore, market lowpoints) are characterized by little optimism and strong markets (and, therefore, market highpoints) are characterized by great optimism. After the major stock market top in 2007, the indicator reached extremely low (i.e., bullish) levels as early as the end of January 2008 and remained at or near such levels throughout the 54 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
2007-2009 bear market. The absolute lowest reading of 20.3% was registered a week after the final market low was reached in March 2009. Expectably, the indicator has been neutral most of the time during the post 2009 bull market, with all but one of the “excessively optimistic” readings leading to consolidations or interim setbacks, including October-November 2009, April-May 2010, January-May 2011, mid January-mid May 2012, and September-October 2012. The exception was mid February-mid June 2013, during which the market zigzagged higher. Interestingly, the only fully bullish (i.e., “excessively pessimistic”) reading during the post 2009 bull market was after the biggest correction in that bull market, late August-late October 2011. The Market Vane readings have been far less volatile in recent years, although they have a similar pattern to Consensus, Inc. Chart 26 shows that the Market Vane bullish percentage reached its most negative levels during the 2006-2007 major equity market top, remaining at “excessively optimistic” levels from October 2006 through June 2007. It did not reach bullish (i.e., “excessively pessimistic”) during the 2007-2009 bear market, but its lowest reading was 34% right at the bottom in March 2009, very near the bullish band, which stood at 33% at the market bottom. This indicator has been neutral all the time during the post 2009 bull market, with a brief bearish exception, 1% above the bearish band in the spring of 2013.
The oldest of the popular sentiment polls is the survey of market letter writers conducted by Investors Intelligence. For over 50 years Investors Intelligence has been reviewing market letters weekly and assigning a bull or bear or neutral opinion rating to each comment and a giving aggregate figures. I have constructed a four-week average of the ratio of bulls to bears; it is shown in Chart 27, compared to the S&P 500. The bearish line is drawn one standard deviation above the 10-year average bull/bear ratio and the bullish line is drawn one standard deviation below the 10-year average. I call readings outside the bands “extreme” or “excessive”. This indicator is popular because of its long, readily available record (it was carried in Barron’s for most of its history) and because it has often been a contrary indicator. For example, Chart 27 shows extreme pessimism (i.e., a bullish reading) during the market basing patterns in late 2002-early 2003 and late 2008-early 2009. Most letter writers claim to be giving long term advice, but clearly they are very influenced by short term moves in the market. The chart shows that as the market started up from those two bases, letter writers quickly became more optimistic, typical of short term traders. The charts do show extreme optimism at the 2000 and 2007 tops, but they also show periods of great optimism prior to interim setbacks during the 2003-2007 and post 2009 bull
Chart 25 Consensus Inc. Bullish Percentage: Equities
Chart 26 Market Vane Bullish Percentage: Equities
Chart 27 Bull/Bear Ratio from Investor’s Intelligence
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Chart 28 Bull/Bear Ratio from American Association of Individual Investors (AAII)
markets. Hence, no “formula” is going to provide a clearly profitable trading rule. Nevertheless, I believe this indicator can be used like other sentiment indicators, to increase or decrease confidence in a trend interpretation. In other words, when excessive optimism appears after an advance, look for a setback. If the ratio drops quickly back to levels of extreme pessimism, it probably means the setback was a minor, temporary affair. Such was the case in the summer of 2010 and in the fall of 2011. The opposite is also true. When excessive pessimism appears after a decline, look for a rebound. If the ratio rises quickly back to levels of extreme optimism, it probably means the rally is a minor, temporary affair. Such was the case in July 2001, in January 2002, and in April-May 2002. The final poll is the survey of investment club participants conducted by the American Association of Individual Investors (AAII). The AAII surveys investment clubs weekly and tallies the number of bulls, bears, and neutral. The poll is “flawed” compared to the other three polls above, because the list of pollees is not the same every week. Nevertheless, since the results are similar to the other polls and because of the accessibility of the data (it appears in Barron’s every week), I include it in this paper. Chart 28 shows a four-week moving average of the bull/bear ratio compared to the S&P 500. The bearish line is one standard deviation above the 10-year average and the bullish line is one standard deviation below the 10-year average. The results are comparable to the other polls with one exception. It is true that the most sustained negative readings (i.e., excessive optimism) were recorded around the 2000 major stock market top and sustained positive readings (i.e., excessive pessimism) were recorded at the major bottoms in late 2002-early 2003 and late 2008-early 2009, and the indicator did not reach levels of extreme pessimism (falsely) during the 2000-2002 bear market. However, this indicator did not give a negative signal (i.e., did not register extreme optimism) at the 2007 major equity market top. To test the series, I organized the 1987-2014 weekly data by deciles for both the percent of bulls and the percent of bears. The top decile of bulls and the bottom decile of bears were considered “excessive optimism”. The bottom decile of bulls and the top decile of bears were considered “excessive pessimism”. I tested short term periods (1, 2, 4, and 8 weeks) and, with some overlap, intermediate term periods (1, 2, 3, and 6 months). The results were favorable for both bullish and bearish signals in the intermediate term horizon and favorable for the bullish signals in the short term horizon. Only the bearish signals in the short term horizon showed unfavorable results. The best results were for bullish crossovers in the intermediate term horizon, with 80% of the signals registering positive results, for a cumulative gain of 459% in the whole time period. If a few anomalous readings in August-October 1987 are disincluded, the short term bearish signals would have been much better. Those results were in line with my expectations: I believe bottoms form quickly and tops are drawn out.
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SUMMARY AND CONCLUSION
The Relationship Between Sentiment and Supply/Demand Indicators and Other Technical Measures It was not my intent to produce a stock market trading model based solely on sentiment and supply/demand indicators. They are, after all, supporting indicators to the basics of technical analysis, trend and momentum indicators. I would never “require” all the sentiment indicators to reach extremes at the same time to reach a conclusion, nor would I expect them to do so. They are, after all, samples of attitudes and behavior, and any indicator could at times be distorted and misleading. I believe there are four kinds of technical indicators. Trend and momentum indicators, the most important technical indicators, illustrate the direction and force of a move. But they often do not tell much about where the market is in a trend. The second or third leg of advance, or the final leg of advance, may not look much different from the first leg. Sentiment and supply/demand indicators, the second and third kinds of technical indicators, give such insights and, accordingly, add to or subtract from confidence in a trend. The sentiment and supply/demand indicators discussed above underscore the difference in attitudes and behavior between investors and traders. The indicators show extreme investor optimism and excessive trader pessimism at stock market bottoms and they show excessive investor pessimism and extreme trader optimism at stock market tops. The best example of that notion is to compare insider activity (see Chart 10) to option activity (see Charts 19-24). Bottoms of consequence are all characterized by insider optimism, low insider sell/buy ratios, and option trader pessimism, high put/ call ratios. If traders stay cautious and investors stay bullish in an advancing trend, I have more confidence
in the durability of the uptrend. If those indicators look more or less like they did at the inception of an uptrend months after the uptrend began (I.e., bullish investors and reluctant traders), I continue to have high confidence in that uptrend. If a setback results in a quick, sizable increase in trader pessimism, I have high confidence the setback is likely to be a brief, temporary affair. If traders are slow to get pessimistic in a downtrend after a stock market top is completed, I have high confidence in the sustainability of the downtrend. If the indicators look more or less like they did at the inception of a downtrend months after a downtrend began, I continue to have high confidence in that trend remaining down. If a rebound results in a quick, sizable increase in trader optimism, I have high confidence the rally is likely to be a brief, temporary affair.
There is a fourth kind of stock market technical indicator, with which I am not concerned here: intermarket analysis, the relationship between equities and bonds (actually all fixed income investments), commodities, and currencies. All other things being equal, the change in price of an alternative to equities will change the supply/demand picture for stocks. For example, if the price of bonds goes up, the supply/demand curve for stocks will rise (i.e., bonds become relatively more expensive); if the price of gold goes down, the supply/demand curve for stocks will fall (i.e., gold becomes relatively cheaper). A thorough discussion of intermarket analysis can be found in John Murphy’s Intermarket Analysis, and additional comments are available in John Murphy’s Technical Analysis of the Financial Markets, and Martin Pring’s Technical Analysis Explained.33
FOOTNOTES 1. Chan, Jegadeesh, and Lakonishok, Financial Analysts Journal, November, 80-90, 1999. 2. Prechter and Parker, Journal of Behavioral Finance, 84-108, 2007. 3. Lo and MacKinlay, May 1989. NBER Working Paper No. w2168. 4. 3. Lo and Wang, The Review of Financial Studies Summer 2000. Vol. 13. No. 2, pp.257-300 © 2000 The Society for Financial Studies. 5. 4. Osler, Carol L., Economic Policy Review, July 2000, Osler Carol L., Journal of Finance, Vol. LXIII, No. 5 October 2003, and Mizrach and Weerts, Highs and Lows: A Behavioral and Technical Analysis (November 27, 2007). 6. Papailias and Thomakos, September 2011. 7. Lo, Mamayski, and Wang March 2000, NBER Working Paper No. w7613, Osler and Chang August 1995 FRB of New York Staff Report No. 4, Savin, Weller, and Zvingelis Journal of Financial Econometrics Spring 2007, and Weller, Friesen, and Dunham University of Nebraska-Lincoln August 2007. 8. Lachhwani and Khodiyar, Quest-Journal Of Management and Research August 2013.
9. Magazzino, Mele, and Prisco, Journal of Money, Investment, and Banking March 2012. 10. Lo, Maymaski, and Wang: Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. Journal of Finance v55 (4 Aug.) pp. 1705-1765. HS=Head-and-Shoulders, BBOT=Broadening Bottom, RTOP= Rectangle Top, RBOT=Rectangle Bottom, and DTOP= Double Top. The other patterns tested in which they found no significant results were IHS=Inverted Headand-Shoulders, BTOP=Broadening Top,TTOP= Triangle Top, TBOT=Triangle Bottom, and DBOT= Double Bottom. 11. Lo, Andrew: Heretics of Finance, 2010. 12. A brief discussion of academia’s Behavioral Finance terms can be found in Thinking, Fast and Slow by Daniel Kahneman, pages 119-128, 154, 284, 289-299, 349, 417-418, 427-430, 444, 445, and 471-472. Farrar, Straus, and Giroux, 2011. Nobel Laureate Kahneman is generally credited with being among the first to define “anchoring”, along with Amos Tversky. 13. A fascinating look on the life of Jesse Livermore is Reminiscences of a Stock Operator by Edwin Lefevre. Wiley 1997.
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14. Mackay, Charles, Extraordinary Popular Delusions and the Madness of Crowds. pp. 28-50 and 50-54. 15. Greenspan, Alan, Speech to American Enterprise Institute, 1996. 16. Shiller, Robert, Irrational Exuberance, 2000. 17. Paul Macrae Montgomery (Montgomery Capital Management) popularized the notion of the magazine cover indicator. Paul Krugman was quoted on March 28, 2009: “Whom the Gods would destroy, they first put on the cover of Business Week”. 18. Commentary on many of my key indicators outside the behavioral area, such as trend, momentum, relative strength, and intermarket measures, can be found in an interview with me in Technically Speaking, by Chris Wilkinson. Traders Press 1997. 19. Kirkpatrick & Dahlquist, Technical Analysis, Second Edition, pp. 181-182. 20. The data for charts 1, 2, 3, and 11 come from the Federal Reserve Statistical Release, Z.1, Second Quarter 2013. 21. I credit investment strategist Michael Sherman, with whom I worked in the 1980s at Shearson Lehman Hutton, for pointing out to me the utility of some of the Flow of Funds statistics from the Federal Reserve. See also Technically Speaking, p. 324. 22. SIFMA U.S. Foreign Activity Report Second Quarter 2013. 23. Kirkpatrick & Dahlquist, Technical Analysis, p. 110, p. 180. 24. New York Stock Exchange, Fact Book, 2013. 25. Pring, Technical Analysis Explained, pp. 501-505; Kirkpatrick & Dahlquist, Technical Analysis, pp. 110-111 and 179; and Wilkinson, interview with Philip J. Roth, Technically Speaking, p. 332.
26. Vickers Stock Research, Weekly Insider Reports. 27. Pring, Technical Analysis Explained, pp. 492; Kirkpatrick & Dahlquist, Technical Analysis, 118; and Wilkinson, interview with Philip J. Roth, Technically Speaking, p. 336. 28. Investment Company Institute, Research & Statistics, Monthly Trends in Mutual Funds Investing; and Monthly Exchange-Traded Fund Data. 29. Pring, Technical Analysis Explained, pp. 499-501; Kirkpatrick & Dahlquist, Technical Analysis, pp. 108-109; and Wilkinson, interview with Philip J. Roth, Technically Speaking, pp. 326 and 328. 30. Kirkpatrick & Dahlquist, Technical Analysis, p. 192, attributing its discovery to Ed Yardeni (www.yardeni. com). Additional comments on valuation as a measure of psychology can be found in Pring, Technical Analysis Explained, p. 522 and Wilkinson, Interview with Philip J. Roth, Technically Speaking, pp. 324-325. 31. Kirkpatrick & Dahlquist, Technical Analysis, pp. 97100; Pring, Technical Analysis Explained, p. 506; Pring, Investment Psychology Explained, pp. 134-153; and Wilkinson, interview with Philip J. Roth, Technically Speaking, pp. 333-334. 32. Consensus Inc., Independence, Missouri; Market Vane, Pasadena, California; Chartcraft, Investors Intelligence, New Rochelle, New York; American Association of Individual Investors, Chicago, Illinois. 33. Murphy, Intermarket Analysis; Pring, Technical Analysis Explained, pp. 559-661; Murphy, Technical Analysis of the Financial Markets, pp. 413-431.
REFERENCES n n n
n n n n n
AAII Index, American Association of Individual Investors, 625 N. Michigan Ave., Chicago, IL 60611. Barrons, Market Laboratory, Weekly, 1978-2013. Bauer, Richard J. and Dahlquist, Julie R., Technical Market Indicators: Analysis and Performance, John Wiley & Sons 1999. CBOE www.cboe.com/data. Chan, Jegadeesh, and Lakonishok, The Profitability of Momentum Strategies, Financial Analysts Journal, November 80-90, 1000. Consensus, Inc., P.O. Box 520526, Independence, MO 64052. Davis, Ned, The Triumph of Contrarian Investing, McGrawHill 2003. Edwards, Robert D., Magee, John, and Bassetti, W.H.C., Technical Analysis of Stock Trends, CRC Press 2013. Elias, David, Dow Jones 40,000: Strategies for Profiting from the Greatest Bull Market in History, McGraw-Hill 1999. Federal Reserve Statistical Release, Z.1, Financial Accounts of the United States, Flow of Funds, Balance Sheets, and Integrated Macroeconomic Accounts. Second Quarter 2013. www.federalreserve.gov. Frost, John A. and Prechter, Robert R., Elliott Wave Principle: Key to Market Behavior, Elliott Wave International 1998. Greenspan, Alan: Speech entitled “The Challenge of Central Banking in a Democratic Society” at the Francis Boyer
n n n
Lecture of the American Enterprise Institute for Public Research Policy December 5, 1996. Hoffmann, Arvid O.I.; Shefrin, Hersh; and Pennings, Joost M.E.; Behavior of Portfolio Analysis of Individual Investors (June 24, 2010). Investors Intelligence, Chartcraft Inc., 30 Church St., New Rochelle, NY 10801. ISE www.ise.com/market-data. Kirkpatrick II, Charles D., and Dahlquist, Julie R., Technical Analysis: The Complete Resource for Financial Market Technicians, Second Edition, FT Press 2010. Lachhwani, Hitendra and Khodiyar, Bhavesh Vishanji, Profitability of Technical Analysis: A Study on S&P CNX Nifty (August 15, 2013) Quest-Journal of Management and Research, 3(2), 31-41. Lee, Cheng-Few; Shih, Wei-Kang; and Chen, Hong-Yi; Technical. Fundamental, and Combined Information for Separating Winners from Losers (April 15, 2010). Lo, Andrew and Hasanhodzic, Jasmina, Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis, John Wiley & Sons May 2010. Lo, Andrew and Hasanhodzic, Jasmina, Financial Prediction from Babylonian Tablets to Bloomberg Terminals, John Wiley & Sons September 2010. Lo, Andrew W. and Mackinlay, A. Craig, A Non-Random
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n n n n n
n n n
Walk Down Wall Street, Princeton, NJ, Princeton University Press. Lo, Andrew W., The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective, Journal of Portfolio Management. August 2004. Lo, Andrew W. and Wang, Jiang, Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory. The Review of Financial Studies Summer 2000 Vol. 13 No. 2, pp. 257-300. © 2000 The Society for Financial Studies. Lo, Andrew W., Mamaysky, Harry, and Wang, Jiang, Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation (March 2000). NBER Working Paper No. w7613. Lo, Andrew W. and Mackinlay, A. Craig, Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test (May 1989). NBER Working Paper No. w2168. Mackay, Charles, Extraordinary Popular Delusions and the Madness of Crowds, London 1841. Magazzino, Cosimo; Mele, Marco; and Prisco, Giancarlo; The Elliott Wave Theory: Is It True During the Financial Crisis? March 2012 Journal of Money, Investment and Banking, 24, 100-108. Market Vane, P.O. Box 90490, Pasadena, California 91109. Mizrach, Bruce and Weerts, Susan, Highs and Lows: A Behavioral and Technical Analysis (November 27, 2007). Montier, James, Behavioral Investing, A practitioner’s guide to applying behavioural finance, John Wiley & Sons, 2007. Murphy, John J., Intermarket Analysis, Profiting from Global Market Relationships, John Wiley & Sons, 2011. Murphy, John J., Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications, New York Institute of Finance 1999. NYSE www.nyse.nyx.com. Options Clearing Corp. www.optionsclearing.com. Osler, Carol L. and Chang, P.H. Kevin, Head and Shoulders: Not Just a Flaky Pattern (August 1995). FRB of New York Staff Report No. 4. Osler, Carol L., Support for Resistance: Technical Analysis and Intraday Exchange Rates. Economic Policy Review, Vol. 6, No. 2, July 2000.
n n n
n n n
Osler, Carol L., Currency Orders and Exchange-Rate Dynamics: Explaining the Success of Technical Analysis (April 2001). FRB of New York Staff Report No. 125. Osler, Carol L., Currency Orders and Exchange Rate Dynamics, An Explanation of the Predictive Success of Technical Analysis, Journal of Finance, Vol. LXIII, No. 5 2003. Papailias, Fotis and Thomakos, Dimitrios D., An Improved Moving Average Technical Trading Rule (September 2011). Prechter, Robert R. Jr., The Wave Principle of Human Social Behavior and the New Science of Socionomics, Gainesville, GA: New Classics Library, 1999. Prechter, Robert R. Jr. and Parker, Wayne D., The Financial/ Economic Dichotomy in Social Behavioral Dynamics: The Socionomic Perspective, Journal of Behavioral Finance 2007, Vol. 8, No. 2, pp. 84-108. Pring, Martin J., Investment Psychology Explained: Classic Strategies to Beat the Markets, Wiley November 1995. Pring, Martin J., Technical Analysis Explained, Fourth Edition, McGraw-Hill, 2002. Savin, N. Eugene; Weller, Paul A.; and Zvingelis, Janis; The Predictive Power Of Head-and-Shoulders Price Patterns in the U.S. Stock Market (Spring 2007). Journal of Financial Econometrics, Vol. 5, Issue 2, pp. 243-265. Schiller, Robert J.: Irrational Exuberance, Second Edition, Princeton University Press, 2005. Securities Industry and Financial Markets Association (SIFMA) U.S. Foreign Activity Report Quarterly, through the second quarter of 2013. Source Media, New York, NY www.sourcemedia.com Vickers Stock Research, a subsidiary of Argus Research Group (www.vickers-stock.com) Weller, Paul A.; Friesen, Geoffrey C.; and Dunham, Lee M.; Price Trends and Patterns in Technical Analysis: A Theoretical and Empirical Examination (August 2007). University of Nebraska-Lincoln Finance Department Faculty Publications. Yardeni, Edward; Abbott, Joe; Johnson, Debbie; and Qunitana, Mali: Stock Market Indicators: Fundamental, Sentiment, & Technical, Yardeni Research Inc., October 31, 2013.
Philip J. Roth, CMT, was the chief market technician at Miller Tabak + Co., Morgan Stanley, Dean Witter, Shearson Lehman, EF Hutton, and Loeb Rhodes. He is a current Board member and three-time past President of the CMT Association (formerly Market Technicians Association-MTA) and a former director of the New York Society of Security Analysts (NYSSA). Roth is currently Vice President of the MTA Educational Foundation (MTAEF). He is an Adjunct Professor in the Graduate School of Business at Fordham University and at the IE Business School in Madrid. He received a BA in Economics from the University of Notre Dame in 1965 and his MA in Economics from Rutgers in 2015. This paper is his master’s thesis. Phil can be reached at email@example.com.
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The SAMT Zurich chapter held a joint event with Global Advisory Partners (GAP) on 21 September. The event was organized and moderated by Henrik Mikkelsen (SAMT Vice President).
Martin Goersch, (below ) Head of Trading at Global Advisory Partners LLC, who spoke about Futures Screenings and a short term outlook which included: Criterias and methodology used based on COT’s Advanced Strategies. Presented with the AgenaTrader platform.
SERIES Q3 2017
Medium- and Long-Term Outlook - Rolf Bertschi (Financial Market Advisor) gave an inside look in his analysis methods and an market outlook based on his current technical work.
SAMT’s second Q3 event, in Zurich and Geneva (10-11 October), came ahead of this year’s IFTA Milan conference. Robin Griffiths, Head of the Multi-Asset Research & Advisory team at the ECU Group; a London-based global macro hedge fund, shared market insights for the final quarter of 2017 and beyond, based upon his signature “Roadmap” cycles model. SAMT members can access Robin’s presentation on the website. Below: Après the Geneva event.
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SAMT BOARD OF DIRECTORS & OFFICERS Patrick Pfister, CFTe President & IT Webmaster firstname.lastname@example.org
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The Swiss Association of Market Technicians Established 1987
The Swiss Association of Market Technicians (SAMT) is a non-profit organisation (Civil Code Art 60ff) of market analysis professionals in Switzerland, founded in 1987. SAMT is a member of the International Federation of Technical Analysts (IFTA). Technical analysis is the study of prices and markets. It examines price behavior on an empirical and statistical basis. It extends to the study of all published information on price trends, volatility, momentum, cycles and the inter-relationship of prices, volume, breadth, sentiment and liquidity. A comprehensive understanding of technical analysis requires a knowledge of statistics and pattern recognition, a familiarity with financial history and cycles. SAMT encourages the development of technical analysis and the education of the financial community in the uses and applications of technical research and its value in the formulation of investment and trading decisions. SAMT has a wide range of activities including: n Organising meetings on a broad range of technical subjects encouraging the exchange of information and knowledge of technical analysis for the purpose of adding to the knowledge of its members. n
Technician (CFTe) exams and the Masters level degree Master of Financial Technical Analysis (MFTA) in Switzerland. These exams are controlled by IFTA. Developing CFTe preparatory courses which are given twice yearly in advance of the IFTA exams.
62 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
THE SWISS TECHNICAL ANALYSIS JOURNAL
We would especially like to see contributions that draw from areas not previously examined, and/or topics tangential to technical analysis. The topics list is just a guide and should in no way be considered restrictive. We wish to make the Journal open to new and innovative ideas from all areas of technical analysis and those that connect with it.
The Swiss Technical Analysis Journal is a quarterly publication established by The Swiss Association of Market Technicians (SAMT). It is compiled by a committee of SAMT colleagues. The Swiss Technical Analysis Journal is essential reading for academics, students and practitioners of technical analysis in all arenas. It is an excellent reference source for anyone interested in technical analysis, containing a wealth of resource material.
Credibility And Recognition
The Swiss Technical Analysis Journal has original contributions from its members covering developments in technical analysis in global markets. The Journal’s aim is to reach leading practitioners and students of technical analysis throughout the world. The Swiss Technical Analysis Journal is a professional resource. Its online publication on the SAMT website will make its work available as a future resource to the community of technical analysts.
SAMT is seeking papers that cover developments impacting, either directly or indirectly, on the field of technical analysis; they may be drawn from such areas as: • Basic market analysis techniques • Indicators—sentiment, volume analysis, momentum, etc.
• Global and intra-global technical analysis • Styles of technical analysis • Data
• The changing role of technical analysis in the investment community.
Submission of contributions to mario.
Material deadline for the Spring 2018 issue
Contributions must be submitted in English with British grammar required.
15 March 2018
Papers should be written in a thesis style.
All texts referred to in the paper must be appropriately referenced with a bibliography and endnotes (footnotes will not be accepted.) Responsibility for the accuracy of references and quotations is the author’s. We expect the authors to check thoroughly before submission. All references are to be included as endnotes. No separate list of references or bibliography should be provided.
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Illustrations and charts must be referred to by Figure Number and source (when applicable). Tables must be referred to by Table Number and source.
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The Swiss Technical Analysis Journal is a quarterly publication which is published in A4 size, in pdf format only. SAMT will accept advertisements in this publication if the advertising does not interfere with its objectives. The appearance of advertising in SAMT publications is neither a guarantee nor an endorsement by SAMT.
Advertising is subject to approval by SAMT. All advertisements must be non-discriminatory and comply with all applicable laws and regulations. SAMT reserves the right to decline, withdraw and/or copy edit at their discretion. Every care is taken to avoid mistakes, but responsibility cannot be accepted for clerical error.
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The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 63
The Swiss Association of Market Technicians (SAMT) is a not-for-profit organization that does not hold a Swiss Financial Services License. It is the aim of the SAMT to promote the theory and practice of technical analysis, and to assist members in becoming more knowledgeable and competent technical analysts, through meetings and encouraging the interchange of materials, ideas and information. In furthering its aims the SAMT offers general material and information through its website and publications therein. The information provided on the SAMT website has been compiled for your convenience and made available for general personal use only. SAMT makes no warranties implied or expressly, as to the accuracy or completeness of any information contained on the SAMT web site. The SAMT directors, affiliates, officers, employees, agents, contractors, successors and assigns, will not accept any liability for any loss, damage or other injury resulting from its use. SAMT does not accept any liability for any investment decisions made on the basis of this information, nor any errors or omissions on the SAMT website. This web site does not constitute financial advice and should not be taken as such. SAMT urges you to obtain professional advice before proceeding with any investment. The material may include views and statements of third parties, which do not necessarily reflect the views of the SAMT. Information on this website is maintained by the people and organization to which it relates. The SAMT believes that the material contained on this website is based on the information from sources that are considered reliable. Although all care has been taken to ensure the material contained on this website is based on sources considered reliable we take no responsibility for the relevance and accuracy of this information. Before relying or acting on the material, users should independently verify its accuracy, currency, completeness and relevance for their purposes. Before making any financial decision it is recommended that you seek appropriate professional advice. The SAMT website may contain links to other websites, these are inserted merely as a convenience and the presence of these links does not constitute an endorsement of the material at those sites, or any associated organizations, products or services.
The Swiss Association of Market Technicians Established 1987
SAMT encourages the development of technical analysis and the education of the financial community in the uses and applications of the technical research and its value in the formulation of investment and trading decisions.
Benefits of Membership
• The organisation of meetings on a broad range of technical subjects encouraging the exchange of information and knowledge of technical analysis for the purpose of adding to the knowledge of the members. • These meetings provide an excellent opportunity to meet and socialise with other traders in your local area and thus develop friendly and professional relations among financial market specialists. • The organisation of presentations from guest speakers from around the world.
• SAMT is affiliated with the International Federation of Technical Analysts (IFTA). All SAMT members are, therefore, colleagues of IFTA and are entitled to attend the annual IFTA conference at reduced rates.
• The “IFTA Update” - the quarterly newsletter from the International Federation of Technical Analysts. • The possibility to sit for the Certified Financial Technicians (CFTe) at a discounted rate. These exams are controlled by IFTA.
• Members receive discounts on a range of products and services related to technical analysis, including software, tuition, seminars and reference books. • Only fully paid-up members have access to the member area and SAMT events.
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• Initial one time registration fee of CHF 50.
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64 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
IFTA CERTIFIED FINANCIAL TECHNICIAN (CFTe) PROGRAM
Passing the CFTe I and CFTe II culminates in the award of an international professional qualification in technical analysis. The exams are intended to test not only your technical skills knowledge, but your understanding of ethics and the market. Level I: This multiple-choice exam consists of 120 questions covering a wide range of technical knowledge, but usually not involving actual experience. In preparation for the exam, candidates should use this Syllabus and Study Guide (CFTe I). This exam is currently offered in English, German, Spanish and Arabic. It will be offered in Chinese at a later date. Download the CFTe I practice (mock) examination (English) or CFTe I practice (mock) examination (Arabic). Level II: This exam incorporates a number of questions requiring an essay based analysis and answers. For this, the candidate should demonstrate a depth of knowledge and experience in applying various methods of technical analysis. The exam provides a number of current charts covering one specific market (often an equity) to be analysed, as though for a Fund Manager. The CFTe II is a paper and pencil exam that is offered in English, French, Italian, German, Spanish, and Arabic, bi-annually, typically in April and October. It will be offered in Chinese at a later date. This exam regularly
takes place in major cities throughout the world. Additional fees apply to candidates requesting the exam in a non-English language or non-IFTA proctored exam location. IFTA will attempt to accommodate any exam location request. In preparation for the exam, candidates should use this Syllabus and Study Guide (CFTe II). Register here for the next CFTe II on 19 April 2018. The deadline to register for this exam is 9 March 2018. No registrations will be accepted after this date. No registrations will be accepted after this date. Download practice (mock) CFTe II examination.
The program is designed for selfstudy. Local societies may offer preparation courses to assist potential candidates. n
Individuals who have successfully completed IFTA accredited certification programs through: Australian Technical Analysts Association (ATAA), Egyptian Society of Technical Analysts (ESTA), Nippon Technical Analysts Association (NTAA), and Society of Technical Analysts (STA) are exempt and may proceed directly to the MFTA program. See below for more details: Individuals who have successfully been awarded the Diploma in Technical Analysis (DipTA) by the Australian Technical Analysts Association (ATAA) are considered to have the equivalent of the certificate and may apply for the MFTA Program.
Individuals who have successfully completed Levels I, II, & III of the Certified ESTA Technical Analyst Program (CETA) through the Egyptian Society of Technical Analysts (ESTA), and have been awarded the CETA diploma, are exempt from both levels and may proceed to the MFTA Program. Individuals who have passed Level I and Level II of the certification program offered by the Nippon Technical Analysts Association
(NTAA) and have been awarded the designation of Chartered Member of the Nippon Technical Analysts Association (CMTA) are also exempt from both levels and may proceed to the MFTA Program.
January 2013, individuals who have passed the STA Foundation and Diploma Courses offered by the Society of Technical Analysts (STA) and have been awarded the designation of Member of the Society of Technical Analysts (MSTA) are eligible to receive the CFTe certification (please contact STA’s Administration for procedures) and may proceed with IFTA’s MFTA Program. Prior to January 2013, holders of the Society of Technical Analysts (STA) Diploma are exempt from Level II, but must pass Level I (a multiplechoice test) before qualifying for the CFTe certification.
Additionally, n Individuals who passed the Market Technicians Association (MTA) Chartered Market Technician (CMT) levels I and II on, or before, 28 June 2013, are eligible to receive the CFTe certification. Please submit an application and provide a pass confirmation from the MTA, including dates attained. There is a one-time application fee of $550 US. No future fees or membership requirements apply.
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For more information on the program please email firstname.lastname@example.org
The Swiss Technical Analysis Journal • Autumn-Winter 2017 • 65
Swiss CFA Society
Groupement Suisse des Conseils en Gestion Indépendants (GSCGI)
CSCGI is a group of economic interests formed by specialized independent financial intermediaries who are confirmed professionals in the financial services industry. The group is open to contacts with any person interested in the business of wealth management seeking to promote dialogue with the banking partners and authorities at all levels. Their goals are to: n Promote contacts between professionals motivated by the same desire for independence, wishing to maintain and develop relationships with counterparts. n Find common ground for exchanging experiences and ideas, a field where diversity and novelty are prevailing. n The enrichment of the links that can be forged on a friendly and professional level within a well defined and recognized framework to favour professional consultation and close dialogues. www.gscgi.ch
The Swiss CFA Society boasts over 2,400 members in Switzerland, against barely 100 in 1996 at inception. It is the largest CFA Institute society in continental Europe. With more than 2,000 candidates taking the rigorous Chartered Financial Analyst® (CFA®) exam in Switzerland each year, the society’s impact on the Swiss investment community is selfevident. It was the first society of CFA charterholders in the EMEA region to be directly affiliated with the prestigious CFA Institute, which includes more than 110,000 members in 139 countries. The vision of the Swiss CFA Society is to be a leader in fostering the highest level of knowledge, professionalism, and integrity in the investment business. www.cfasociety.org/switzerland
Swiss Futures and Options Association
The Swiss Futures and Options Association (SFOA), previously the Swiss Commodities, Futures and Options Association, was founded in 1979 as a non-profit professional association for the purpose of promoting derivative financial instruments, particularly standard futures and options contracts on financial instruments and commodities, to the widest possible audience, and to serve the interests of its members. SFOA serves users of commodity and financial derivatives, as well as professionals, their institutions and the exchanges. www.sfoa.org
International Federation of Technical Analysts (IFTA)
IFTA is a non-profit federation of 26 individual country societies who individually and jointly dedicate themselves to
Research, education, camaraderie and dissemination of technical analysis of world markets. The IFTA societies support sharing technical analytical methodology that at its highest level is a valid, and often-indispensable element in the formulation of a reasonable basis for investment decisions. n Promotion of the highest standards of professional conduct, international cooperation and scholarship between all its Member and Developing Societies within all arenas of technical analysis. n Providing centralized international exchange for information and data of various financial centers while respecting individual country and Society business practices, legal structures and customs. n Encouraging the standardization of education and testing of its constituent members in technical analysis, making sure that each individual country’s security analyst licensing, legal and language /communication priorities continue to be individually accepted. n Fostering the establishment of individual societies of technical analysts without bias in regard to race, creed or religion. It supports the need for maintaining a free and open worldwide markets under normal, and in particular crisis periods. As a growing bridge of communication worldwide, IFTA remains open to methods of technical analysis, while encouraging the consideration and support of membership for both developing and established societies. n
Journal Media Sponsor Training: www.technicalanalyst.co.uk/courses/calendar/ Awards: www.technicalanalyst.co.uk/awards/the-technical-analyst-awards-2016/ Research: www.technicalanalyst.co.uk/research/ 66 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
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The Swiss Association of Market Technicians ZÜRICH • GENEVA • LUGANO • CHUR 68 • Autumn-Winter 2017 • The Swiss Technical Analysis Journal
Great analysts who were speakers at the IFTA conference appear in this edition: Perry Kaufman (page 12), Robert Prechter (page 19) and Alber...
Published on Dec 13, 2017
Great analysts who were speakers at the IFTA conference appear in this edition: Perry Kaufman (page 12), Robert Prechter (page 19) and Alber...