Technically Speaking, April 2015

LETTER FROM THE EDITOR

Once a year, the MTA Symposium assembles a lineup of great speakers yet some of the best technicians attending are in the audience. Symposiums have long been one of the most valuable of MTA membership and now technology makes it possible to extend that benefit to all members. Videos of the presentations will soon be available on line. Discussion forums on the MTA web site allow members to exchange ideas about the presentations or any other topic of interest.  In this month’s Technically Speaking we try to start the conversation about speakers with an article featuring the wisdom of a few presenters.

This month’s newsletter also highlights the fact that chapter meetings offer another way for members to hear from great speakers. Leslie Jouflas, CMT, shares insights from Market Wizard Ed Seykota’s recent presentation in Washington.

We also have new ideas about how to apply the principles of technical analysis in this issue and we’d like to hear from you about how you apply those principles. If you have ideas for articles in upcoming issues of the newsletter, please send me a note at editor@mta.org.

Sincerely,

Michael Carr

What's Inside...

LESSONS IN TECHNICAL ANALYSIS FROM THE ANNUAL SYMPOSIUM

Editor’s note: The MTA’s Annual Symposium always brings together the best minds in technical audience as speakers and attendees. This...

Read More

RECOGNITION AWARD: STANLEY DASH, CMT

The MTA honored Stanley Dash, Vice President of applied technical analysis at Trade Station, in recognition of his all-encompassing generosity...

Read More

BROAD BOTTOM CONFIGURATIONS AND THEIR APPLICATION TO INVESTMENT STRATEGY

Editor’s note: this is an extract of an article that was originally published in Issue 8 of the MTA...

Read More

TECHNICAL ANALYSIS OF ECONOMIC DATA: U.S. ECOMONY LAGGING ANALYST FORECASTS; REVERSAL SIGNALED

Editor’s note: Tom DeMark demonstrated economic data can be analyzed with the DeMark Sequential™ and other indicators at previous MTA...

Read More

PUGET SOUND MTA CHAPTER REVIEW: ED SEYKOTA PRESENTATION

When you are one of the best in the world at what you do, you probably don’t do things like...

Read More

MARKET ANALYST: ADVANCED SOFTWARE FOR ANYONE

At the MTA Symposium, Mathew Verdouw, CMT, CFTe, Global CEO and Founder at Market Analyst International, provided attendees with additional...

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5 STEPS TO GETTING INVESTMENT RISK UNDER CONTROL

Editor’s note: this article was originally published in the Winter 2015 issue of Financial Executive magazine and is reprinted...

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THE ULTIMATE WAY TO BACK TEST YOUR BEST IDEAS: GLOBAL FINANCIAL DATA

Technical analysis is based on the idea that history tends to repeat and to determine if history repeats we need...

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WILT THE STILT GETS THE JILT

Editor’s note: this was originally published at the Global Financial Data blog and is reprinted here with permission.

Global...

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LESSONS IN TECHNICAL ANALYSIS FROM THE ANNUAL SYMPOSIUM

Editor’s note: The MTA’s Annual Symposium always brings together the best minds in technical audience as speakers and attendees. This year’s event, held at the end of March, was no different. Each speaker presented useful trading ideas and attendees left the event with dozens of testable and practical ideas. In this article I am highlighting just a few of the many great insights into technical analysis that I heard over the two days. In the coming weeks, all presentations will be available online so that all MTA members can benefit from the speakers. We will also feature more about the Symposium in future issues of Technically Speaking.

Beyond the Benchmark: Applications for Active Investment Management offered attendees a chance to explore how top money managers outperform the market. Conference presentations showcased unparalleled industry experience and included discussions on how some of the best in the industry implement disciplined approaches to investing using technical tools.

Tom Dorsey kicked off the two-day event with a high energy presentation that highlighted the importance of discipline. Tom co-founded Dorsey, Wright & Associates in 1987 and continues to serve as President. He is the author of Point & Figure Charting: The Essential Application for Forecasting and Tracking Market Prices, Thriving as a Broker in the 21st Century and Tom Dorsey’s Trading Tips: A Playbook for Stock Market Success.

Tom applies discipline to every part of his firm and has done so since its founding. The firm’s approach begins with relative strength analysis of point and figure charts. This is not a widely followed approach to the markets but it intuitively made sense to Tom. He began his career updating charts by hand and then developed automated systems to do exactly what he had done by hand. Computers were used to automate what worked, not to search for new ideas that might work. It was this disciplined focus that allowed Dorsey Wright to grow.

In the past few years, growth has been spectacular. Dorsey Wright’s asset management business has more than tripled in the past three years to nearly $6 billion, including its largest ETF, the $1.6 billion PowerShares DWA Momentum Portfolio (NYSE:PDP), which has significantly outperformed the S&P 500 index since inception in 2007 and achieved a top-decile ranking among ETFs in its Morningstar category. Additionally, DWA launched the First Trust Dorsey Wright Focus 5 (NASDAQ:FV), which became the most successful ETF launch during 2014 by attracting more than $1.2 billion in assets in less than 10 months.

It’s interesting to note that the billions of dollars currently under management are based on finding new vehicles to implement the same disciplined strategy that Tom has used consistently since the 1980s. The lesson for attendees was clearly to stay disciplined.

Discipline can help investors react when the markets behave in unexpected ways, according to another speaker at the Symposium.

“Financial markets are the most perverse thing mankind ever invested – they are perverse because they always do the unexpected.”

 That’s the assessment of one of the brightest minds to ever study technical analysis, Walt Deemer, this year’s MTA Annual Award winner. As that short quote above demonstrates, Walt was also one of the most engaging speakers at the Symposium. Markets do unexpected things because people perform in unexpected ways. Human behavior is at the heart of the market’s actions.

In last month’s newsletter, we reprinted a memo from Walt explaining why early 1980 was the wrong time to sell McDonald’s and Philip Morris at 8 to 9 times earnings. As Walter wrote, the stocks were emerging from “big base” patterns. The big base pattern is the subject of another article in this month’s newsletter. That article is an extract of an MTA Journal article written by Bill Doane, who Walt referenced in his short talk at the Symposium.

At the Symposium, Walt noted how he has also considered McDonald’s and Philip Morris to be ill-timed buys when they were added to mutual fund portfolios. At the time they were bought, the stocks traded with price-to-earnings (P/E) ratios of more than 30. Investors were in the midst of the Nifty Fifty market craze and these were two of the stocks known as “one decision” stocks since they could be bought and never be sold. The one decision was when to buy since a sell decision would never be needed. The selling in 1980 was irrational, as he pointed out, but the buying had also been irrational.

The Nifty Fifty craze is an example of the human nature that makes the markets unpredictable. But, Walt, noted, the general outline of human nature is wholly predictable. Investors and traders are all looking for profits. They tend to pursue profits by thinking in the same way. In the 1960s the Nifty Fifty were all buys. In the 1990s internet stocks were all buys. Both times, high-priced stocks were considered undervalued because the Street had redefined value.

As Walt noted, the cause and the solution to the problem lies in the questions analysts ask. “In our search for answers, we often end up with consensus questions. The answer depends on the question.”

In the 1960s and the 1990s, the consensus questions were “how can we justify buying these stocks everyone loves even though we know they’re overvalued?” The answer involved assuming earnings would continue growing at a rapid pace forever. Had the question been “is it possible for these companies to grow fast enough to meet expectations?” the answer would have been “no” and losses from the bear markets that followed the manias would have been avoided.

As an example of the type of question he has about the current stock market environment, Walt noted, “in March six of 19 trading days closed with a gain or loss of more than 1%. What does this mean?”

Walt didn’t provide the answer but he did encourage the attendees to remember that “markets can do anything they want. For example, the stock market can’t go up six years in a row, but it has.” Walt wished he’d understood this market principle when he began his investment career.

Dean LeBaron, who founded Batterymarch among his many other accomplishments, was a mentor to Walt and a student of Walt’s work. The mutual admiration of these two market legends was on display as they were interviewed by Barry Ritholtz at the Symposium. Dean agreed with Walt’s view that investors must be prepared for markets to do the unexpected.

When asked what he wished he knew at the start of his successful career, Dean said he would have had “a better appreciation of the skills of others. I’d be a better listener. I passed up a lot of learning opportunities by focusing on what I was doing.”

Attendees of the Symposiums have always found listening to the wisdom of investment legends like Walt Deemer and Dean LeBaron to be valuable. Walt also noted that he found Bob Farrell’s work to be an important influence on his thinking. Barry Ritholtz noted he had compiled Bob’s “10 Market Rules to Remember” on his blog.

Bob Farrell retired from Merrill Lynch & Co. in 1992 as chief stock market analyst. Farrell was a legend on the Street and his ten rules still apply.

  1. Markets tend to return to the mean over time.
  2. Excesses in one direction will lead to an opposite excess in the other direction.
  3. There are no new eras — excesses are never permanent.
  4. Exponential rapidly rising or falling markets usually go further than you think, but they do not correct by going sideways.
  5. The public buys the most at the top and the least at the bottom.
  6. Fear and greed are stronger than long-term resolve.
  7. Markets are strongest when they are broad and weakest when they narrow to a handful of blue-chip names.
  8. Bear markets have three stages — sharp down, reflexive rebound and a drawn-out fundamental downtrend.
  9. When all the experts and forecasts agree — something else is going to happen.
  10. Bull markets are more fun than bear markets.

Also at the Symposium, in a breakout session called “The Checklist Manifesto: Fidelity Technical Process,” Mark Dibble and Darren Chabot provided a rare glimpse into how the mutual fund giant operates. Technical analysis is important to Fidelity.

Mark joined Fidelity Investments as a Senior Technical Analyst in March 1994. His success led to his recognition as Institutional Investor’s “Best of the Buy Side” in 2002. Prior to joining Fidelity he was a Vice President of Morgan Stanley in New York, providing technical research to the Global Equity Derivatives and Equity Research Departments. Before joining Morgan Stanley in 1984, he did technical research for Cowen & Company in New York. Mark has 31 years of investment experience. As he explained in his presentation, Mark’s approach combines the traditional pattern recognition discipline of Edwards and Magee with momentum and sentiment indicators. He works with Fidelity equity portfolio managers, analysts, and traders and focuses on the U.S. industrial, technology, telecom and utilities sectors. He has portfolio oversight on the Technical Research Pilot Fund.

Darren joined Fidelity’s Technical Research Department shortly after Mark in 1996. Initially he was a chart designer for the Fidelity Chart Room, a developer of deep history market data and a researcher creating technical studies. He became a technical analyst a few years later after experimenting with various styles of technical analysis. Currently he focuses on financial, health care, consumer staple, and small cap stocks combining old fashioned chart reading and mechanical overbought / oversold screens.

Technical research at one of the largest mutual funds in the industry may have a very different function than it does at boutique shops, investment banks or hedge funds. Emphasizing a theme introduced by Tom Dorsey, both Darren and Mark emphasized the importance of a disciplined approach. Interestingly, these two veteran technicians of the same department in the same firm have each developed distinct and different processes and approach.

Darren spends most of his time researching large cap stocks but worked with small caps earlier in his career. He observed “small caps are not as technically friendly.” Whether looking at large caps or small caps his process is eyeball driven. He is looking for trends about to change because of the timeframe he works within. He is communicating his ideas to large fund managers who will need time to act on his recommendation. He described his approach to the markets as “getting paid starting one month after you make a buy recommendation and stopping one month after the sell recommendation.” This is a unique challenge that highlights the diversity of technical analyst positions.

Day to day, Darren is responsible for analyzing the holdings of three funds. He provides recommendations to the managers of those funds and is largely focused on relative strength. From a technical perspective, mutual fund managers largely rely on relative strength to outperform their peers. He is able to maintain opinions on hundreds of stocks because he can often make assessments within seconds of looking at a chart. A chart can be described as good or bad depending upon whether or not it meets his view of strength or weakness.

As noted above, an article about Fidelity’s approach to technical analysis can be found elsewhere in this issue.

In a presentation called “Why Technical Trading Strategies Often Don’t Generate the Same Results From the Markets, as Compared to their Results During Research” K.D. Angle, CTA, CEO Angle Capital Management LLC., offered several valuable insights into how markets work. K.D. runs a long-term, trend-following, rules-based managed futures program that offers a broadly diversified portfolio using markets traded in the major exchanges in Europe, North America and the Pacific Rim.

K.D. Angle began learning about the futures markets while working for his father’s oil and gas company. It was here that he also learned about the importance of trading the markets conditions that exist right now instead of imposing your own bias on the market. One field his father developed was producing low-quality oil that was difficult to market because of its high helium content. This became the basis of the world’s largest helium plant as K.D.’s father understood the value of the well’s output was the unexpected helium rather than the hoped for oil.

After selling the world’s largest liquid helium plant in November 1978, his father invested $2 million in 2000 gold futures contracts. Six months later this market position was worth $100 million. His father eventually closed the position for a gain of more than $60 million on his first futures trade. After that, K.D. noted there were two $20 million losing positions as his father learned about the markets.

K.D. began trading the futures markets in 1979 and developed his first rules-based trading strategy in 1984. In 1985 he created a newsletter called “The Timing Device” that specialized in making specific trade recommendations in the futures markets. Its market calls were monitored by Commodity Trader’s Consumer Report (CTCR) along with 25 other letters. In early 1987, the newsletter was featured in Forbes magazine as being the #1-ranked letter of 1986 according to CTCR. Within 90 days of the Forbes article, Angle’s subscription base expanded to eight countries.

Despite its successful record, K.D. told the audience at the Symposium, he had a difficult time keeping subscribers. Talking to other successful newsletter writers, he learned that marketing could be more important to attracting and retaining subscribers than the success of the trades. One expert told him he needed to create a problem in the mind of potential subscribers and then explain how the newsletter addressed that problem. This was not appealing to him and after 1995, K.D. retired the newsletter and went into asset management on a full-time basis so he could invest the majority of his time and resources into researching and developing rules-based strategies.

He now manages three programs. The oldest began trading in 2000. In 2003, he introduced a second investment program known as the “Keck Program.” In 2010, he created a third product which combines the Genesis and Keck programs into a single allocation product known as the “AIP Program.” The three programs currently manage approximately $120 million.

K.D. did not discuss the performance of his funds but understanding his performance helps understand the importance of his insights. The Genesis Program has delivered a total return of 767% since inception in April 2000 through February 2015. Over that same time, the S&P 500 gained 45%.

There were a number of important points in this presentation but two stand out and apply to all technicians whether they look at futures or stocks.

K.D. believes the most important part of market research is to identify what you missed in your research. This idea ties back to Walt Deemer’s point that the markets will do what they want to do. K.D.’s point is that through your research, you will know how your strategies will react to the unexpected. To be sure your strategy will react to the market action as it unfolds, your rules must be specific because the computer system does only what you say to do.

He also noted that there is often no reason to be in a market.

“How often does a market move vertically? That’s the only time you’re making money. The number of trading days that contribute to the trend is about 12%. Prove to me why I want to be in.”

Over the long term, he’s taking exposure on the long or short side less than 50% of the time to any market. This limits his risk and by trading a diversified portfolio he is able to participate in the largest market moves when they occur. His results, shown above, speak to the wisdom of this approach.

Each speaker contributed unique insights to attendees and we will have more of those insights in the next few issues of Technically Speaking.

Contributor(s)

Michael Carr, CMT

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

RECOGNITION AWARD: STANLEY DASH, CMT

The MTA honored Stanley Dash, Vice President of applied technical analysis at Trade Station, in recognition of his all-encompassing generosity to the academic community, including providing instructional time and charting resources for academic trading rooms.

 Stanley Dash, CMT, combines more than a quarter century of experience in trading and technical analysis with more than 20 years of work as a teacher and public speaker on topics including stocks, futures, options, and technical analysis. Stan began his career on Wall Street in 1975 with E. F. Hutton. While there, and with other nationally known firms, he was involved in institutional and retail trading, management, and compliance. Later, as a member of the Commodity Exchange, Inc., Stan was an active floor trader at one of the leading domestic futures and options exchanges.

In the fields of training and public speaking, he is proud of his long affiliations with such noteworthy organizations as the New York Institute of Finance and the Institute for Financial Markets. Early in 2003, following a long tenure as a consultant to the firm, Stan joined TradeStation’s Client Training and Education department on a full-time basis. His efforts are devoted to developing materials and delivering training to TradeStation users nationwide. These offerings range from basic platform orientation to beginner and advanced EasyLanguage® training. Stan formerly was registered as a Commodity Trading Advisor and was a member of National Futures Association; he is currently registered as an Associated Person (futures) and General Securities Representative.

Contributor(s)

BROAD BOTTOM CONFIGURATIONS AND THEIR APPLICATION TO INVESTMENT STRATEGY

Editor’s note: this is an extract of an article that was originally published in Issue 8 of the MTA Journal in May 1980. The original article includes several other examples of board bottom configurations which are also known as “big bases.”

Low Risk vs. High Risk
The Investor vs. The Speculator
The Conservative Approach vs. The Aggressive Approach
Broad Bottom Configuration vs. Relative Strength / Momentum

Back in the early 1960s this writer interviewed George Chestnutt of American Investors. “Bill,” he said, “how do you pick next year’s winners?” “Well,” I explained, “I’d look for a breakout of a big base pattern and then I would buy the stock as it settled back on top of that broad bottom.” “Fine,” he said, “you’ll probably do a bit better than average. Top performance will come, however, by buying last year’s winners.” I had to give that some thought–and I have pondered that statement ever since.

As we progressed into era– the relative strength/momentum players–and I even began to imitate them. It was easy. All one had to do was follow a select list of volatile market leaders and buy, or recommend to buy, every reaction, The trick was in knowing when to stop. And even if one did know when to stop, he would be wrong only once, and that would be on the reaction that completed the top and reversed the major trend.

The go-go environment of the 1960s seemed to nurture such an approach. Those issues that appeared to be high, went higher, dramatically higher. The early practitioners (later to be called gunslingers) became idols and were worshipped as possessing some uncanny insight.

Imitators increase in number as published works by Levy (The Relative Strength Concept of Common Stocks Price Forecasting by Robert A. Levy, Ph.D., Investors Intelligence, 1968) and Adam Smith (The Money Game, Random House, 1968) gave credence and acclaim to such aggressive techniques. In fact, the simulation models developed by Levy (an academic economist and statistician) proved unequivocally that “the strongest stocks of the recent past are better investment vehicles than the weak stocks of the recent past.”

About this time, however, some pot holes began to appear. The market was no longer bailing out mistakes, losses were being incurred and locked-in positions were being maintained–in “bag holder” fashion. As fads and fashions change within industries and sectors, so do changes take place within investment approaches and techniques. The market seems to have a perverse tendency to send out a smoke screen once too many get the scent of its intentions. This results in erratic performance. If the general market downdrafts of 1962, 1966, 1970 and 1974 fail to entrap the aggressive player of “last year’s winners,” the periodic shift of proper investment strategy will. Institutional results tend to verify this as being true. Quite often the top performing fund of one cycle will fall to the bottom quartile during the next cycle. In this respect, the performance race can be compared to the proverbial race between the Tortoise and the Hare.

Given a lengthy period of measurement, the theoretical results of conservatively managed Windsor Fund and Puritan Fund could conceivably about match that of aggressively managed Enterprise and New Horizons Funds. It is evident from the schematic that if, somehow, one could avoid the losses, or moderate the periods of underperformance, consistently unparalleled results could be attained. Unfortunately, historical evidence has not supported this as being possible no matter how plausible it may appear. High risk is inherent in any opportunity for high gain. There are exceptions, of course, but few know when to stop; few are able to adequately alternate from an aggressive stance to a conservative stance. The ever present human emotions of hope, fear, greed and pride of opinion mitigate against consistent and continued success.

To make this switch from aggressive to conservative, one must have historical knowledge of excesses on the one hand and troughs on the other. This may mean a substantial change of strategy in accordance with the 4-year cycle…a year or two in an aggressive framework followed by a year or two in a conservative mode. Institutions may deem it advisable to look even further out and couple the cyclical with the secular outlook. Dick Stoken in his excellent book on cycles (Cycles: What They Are, What They Mean, How to Profit by Them, McGraw-Hill 1978), makes easy reading out of a complicated subject. In other words, one must set up a workable plan and stick to it devoid of emotion. Action should only be taken when hard evidence suggests that a change of consequence is mandated.

The Hare has proven that the end results are well worthwhile – less glamorous perhaps – but certainly less worrisome and aggravating to the manager as well as owner of the assets.

Broad Bottom Patterns: An Explanation

Before one can erect a superstructure, or a skyscraper, a strong foundation must be constructed. The higher the building, the more solid the base must be. It is the same in the stock market, or any other freely traded, marketable instrument–a base is usually formed. Bases are merely periods of reaccumulation where weak and discouraged holders sell and newer, more informed buyers accumulate. Shapes and sizes vary. Those specifically discussed in this paper are long-term bases, often depressed from higher levels, and normally taking 3, 4, and 5 years to complete. Numerous examples appear in the pages that follow.

As opposed to the speculator not knowing when to stop buying and sell, the investor in broad bottom base patterns often has the problem of knowing when to start to buy. Whereas the speculator getting in late in a volatile situation leaves himself wide open to a substantial loss, an ill-timed purchase of a depressed issue normally results in only a minimal loss.

This question of when to accumulate has been thoroughly researched and recently published in an excellent book on this subject by Ray Hanson and Bob Mann (Non-Random Profits, Freedom Press, 1978). They have found that the duration, or length, of the average base structure is approximately four years (Eleven Quarter Rule). This is measuring from the extreme low, whether it be the initial panic low or subsequent secondary low that completes the bear phase.

It should not be assumed that this approach is the easy road to success. Although it is probably fair to say that 80% of the stocks that enjoy big moves during market upswings possess the technical prerequisites to do so, for every winner, five or six others (with the same characteristics) remained dormant. In this regard, George Chestnutt is right. One will do well, but only “a bit better than average.”

Of course, the ideal strategy to employ is a combination of conservative and aggressive techniques. In the words of Ted Warren(S), an old-timer with a good number of years’ experience with Broad Bottoms, “buy like an investor should and sell like a speculator would hope to.”

Broad Bottom of the 1920s

As stated elsewhere, there are any number of similarities between the mid-70s/early 80s and the 1920s – even economic, political and social. Unfortunately there are not many charts of individual companies available for this period. If there were, undoubtedly any number of them would resemble the one below.

Note the large percentage decline General Motors experienced during the Bear Market of 1920-21. By late 1924

  1. a) It was depressed in price, out-of-favor and associated with a general feeling of disinterest,
  2. b) It had technically completed three years of constructive development. It represented a bargain basement price.

Late 1924, early 1925 represented an ideal “entry” level for the conservative investor. As the “After” chart shows, momentum began to accelerate shortly thereafter as the uptrend got underway. Those who were in early had nothing to worry about. Even the “breakouts” of the three continuation patterns produced good results. But be careful of the pitfalls of the aggressive player. Such perceived continuation patterns can, at any time, develop into distributional patterns. The occurred in 1928-29 as the top floor was reached. Those who did not “get off,” rode the elevator down.

Broad Bottoms: Gold in the 1970s

Although not a depressed, broad bottom in the strict sense and as previously discussed, the chart of the London price of Gold Bullion is a textbook case of the forces of supply and demand. Note the drying up of activity as indicated by the narrowing of the weekly ranges at the extreme lows in 1970. Note also the two attempts to exceed the $44 resistance level followed by the successful and impressive breakout in January 1972.

As we know, the “After” portion of the example only shows a portion of the subsequent move, Gold reached $200 an ounce in December, 1974, declined to $100 an ounce by September, 1976, and then proceeded to confound both Bulls and Bears alike as it skyrocketed to an unbelievable $850 an ounce in

January, 1980.

They say there’s no fever like gold fever. How high is high? How low is low? No one ever really knows. Markets go to extremes–both on the upside and on the downside. They always seem to go further than we are initially able to envision. If one used the same scale as used on the charts below, one would need every page of this article to plot the trend! At times, it is foolish to try to predict a top until a top begins to develop. Tops can be distributional or psychological. In this regard, watching the media and studying crowd psychology of the “tulip era” would have been a great help.

Conclusion

As you have probably surmised by this time, we are believers in the long base pattern as the foundation for large, long-term price trend movements. It is a methodology that we have studied and implemented for many, many years.

There is logic to the pattern. It reflects the movement of stock out of weak hands into strong hands. It is the result of the natural process of correcting extremes– the transitional period between overvaluation and undervaluation. Although some experience is needed in distinguishing between the potential winners (E-Systems) and laggards (LTV) and in fine tuning proper entry/exit points, the rewards in exploitation of the Broad Bottom Configuration, are more times than not, worth the effort.

Contributor(s)

William S. Doane

Bio coming soon.

TECHNICAL ANALYSIS OF ECONOMIC DATA: U.S. ECOMONY LAGGING ANALYST FORECASTS; REVERSAL SIGNALED

Editor’s note: Tom DeMark demonstrated economic data can be analyzed with the DeMark Sequential™ and other indicators at previous MTA Symposiums. This technique was detailed in the March 26 issue of Bloomberg Brief: Technical Strategies. This article is an extract of that analysis.  It is also  possible to apply stochastics or other traditional indicators to economic data to spot potential turning points.

The U.S. economy is underperforming analyst estimates by the most since the 2009 recession, according to the Bloomberg Economic Surprise Index.

The ESI aggregates metrics across six areas of the economy to determine its overall health, including gauges of housing and real estate, industrial production, personal spending, retail, consumer sentiment and the labor market. These are compared to previous analyst estimates collected by Bloomberg.

The chart compares the ESI to the S&P 500. The current trend of the ESI is negative and below -2 standard deviations from the mean, marking the biggest gap between forecasts for the current period and real economic performance in six years.

The red and green spans on the ESI chart measure the number of weeks that pass between trough to peak (green for improving economy) and peak to trough (red for weakening economy).

Source: Bloomberg (Terminal users can click on chart to access a live version or run G BBTA 715)

The current slowdown has lasted 28 weeks so far. The deviation measure and duration of underperformance suggests this is a longer-than-normal downtrend.

Layering two trademarked DeMark   Indicators, the TD Sequential and TD Combo, onto the ESI helps to identify turning points in economic performance. The current purple 13 signal is below the time series, suggesting economic underperformance may be ending. The 9s and 13s shown above the price line signal economic outperformance may be ending.

Paul Ciana, CMT, is an equity and technical analysis application specialist at Bloomberg LP in New York. He can be contacted at PCiana@Bloomberg.net.

Contributor(s)

Paul Ciana, CMT

Paul Ciana is a Managing Director and Chief Technical Strategist at Bank of America, based in New York. He publishes his short- and long-term technical views on Rates, FX, Commodities and emerging markets in the coveted and most read Technical Advantage report...

PUGET SOUND MTA CHAPTER REVIEW: ED SEYKOTA PRESENTATION

When you are one of the best in the world at what you do, you probably don’t do things like most people. Our honored guest and presenter at the February 19, 2015 Puget Sound MTA meeting, Ed Seykota, certainly doesn’t do things like most people.

We had around 100 attendees at this meeting for the opportunity to meet and hear from one of the original Market Wizards brought to the investment and trading public’s awareness in Jack Schwager’s classic book, Market Wizards; Interviews with Top Traders (NYIF, 1989).

Ed Seykota is a long term trend follower trading automated systems that he builds. From Jack Schwager’s interview; “He conceived and developed the first commercial computerized trading system for managing clients’ money in the futures markets”.

Our evening with Ed Seykota was covered in three sections. When the parts are understood and merged together, what Ed presented was a roadmap of the markets, based on his modeling of economics and fundamentals (funny-mentals as he refers to them). All of this is also fully covered in his 2013 book, Govopoly in the 39th day, which can be found at www.tradingtribe.com.

The three parts covered were:

  • Trend following through the song ‘Whip Saw’.
  • Macro conditions that will lead to hyperinflation, thus creating volatility not seen before and large trends. (Govopoly in the 39th day)
  • The mental/psychological components necessary for market participants. (The Trading Tribe, Ed Seykota, 2005)

Ed, being an accomplished musician, started out the presentation on his banjo with the infamous ‘Whip Saw’ song. The lyrics to the song, in Ed’s words, “are all you need to know about trend trading.” The audience joined in as Ed played his banjo and led the sing-along:

What do we do when we catch a trend? -We ride that trend right to the end

What do we do when we show a loss? – We give that gol-durn loss a toss

How do we know we have the right risk? – We make a lot of money and sleep at night

What do we do when the price breaks through? – Our stops are in so there’s nothing to do

What do we do when a draw down comes? – We stick to the plan and pull the trigger

What do we do with a hot news flash? – We stash that flash right in the trash.

Chorus: You get a whip and I get a saw – one good trend pays for them all.

Ed then discussed sections from his latest book Govopoly in the 39th day and humbly presented some of his findings and forward projection modeling on the current state of the U.S. economy and implications for the future both in the United States and worldwide.

In this model he addresses our debts as a nation, our manufacturing, employment, standard of living and personal freedoms.

He believes that something other than the normal, cyclical business cycle is at work. Something longer-term, namely he sees the Assimilation of the assets of the free-competition sector by the Govopoly system, meaning monopoly through government sanction.

His theory presents that the Assimilation of assets of our free-competition sector by the Govopoly system explains our current situation and indicates the likely path forward. His Assimilation Model shows that the free-competition sector starts out growing rapidly. The Govopoly system starts out smaller and grows faster by assimilating the free-competition sector.

Through objective modeling, he also presented some alterations to the projected outcomes and briefed the audience on the pros and cons of the outcomes. He does not present the information to scare listeners, but rather to present ideas to prepare and prosper from the conditions and outcomes this model presents.

Ed is one of the rare individuals who can take a very mathematical, programmatic detailed concept and convey it in a simple and often humorous way that anyone can understand regardless of their degree of knowledge of markets, economics or government policy.

Ed is known for his rigorous back testing and keen quantitative algorithmic approach to the markets. His analysis was as always insightful, sobering and thorough projected outlook.

Ed has also studied extensively the field of psychology and is an expert in traders’ psychology and founded the Trading Tribe, www.tradingtribe.com, which is a world-wide network of traders who have a deep commitment to trading well and having good relationships with others. He helps traders in the process of creating new patterns. Trading Tribe participants follow a process to help each other and uncover problems that are holding them back in both professional and personal areas. His famous quote ‘everyone gets what they want out of the markets’, may have brought new meaning to many audience members  and sparked many questions from the audience during the question and answer session.

As a rare opportunity, the attendees had over 1 hour to ask Ed anything. And they did. Discussion over the emotional mindset of a trader, Trading Tribe, past performance, “rocks”, and various technical analysis aspects were openly covered.

Here are a few of his thoughts from some questions:

  • Stick to your system when emotions tell you not too
  • Stock systems have to have a purge system (so they don’t clog up with positions that don’t move.)
  • When asked about measuring sentiment in markets, he replied that he uses magazine covers to help determine extreme optimism or pessimism. He said that the covers have to have certain emotional content, such as portraying or saying something certain is going to happen.
  • There is no such thing as a trade having a probability of working. He said each individual trade is either 0% or 100%.
  • He also discussed that there is no such thing as “trend”. Trend does not exist in the now. It only exists from an historical perspective.

At the end of the evening Ed had given our audience an excellent roadmap for steps to build a trend trading system, a model to study to understand the next/coming hyperinflation environment and the mental tools to learn to follow a well researched and tested system to prosper.

Here are a few books and trading styles that Ed mentioned as influences on him:

  • Reminiscences of a Stock Operator, Edwin Lefevre
  • Extraordinary Popular Delusions and the Madness of Crowds, Charles Mackay
  • Richard Donchian, one of the first to write about a quantified trend-following trading system.

Contributor(s)

Leslie Jouflas, CMT

Leslie Jouflas, CMT holds the Chartered Market Technician designation, the worldwide gold standard for technical analysts. She has been a technical analyst, educator and private trader for over 23 years. She is founder of www.TradingLiveOnline.com, providing programs for trader education in Pattern Recognition,...

MARKET ANALYST: ADVANCED SOFTWARE FOR ANYONE

At the MTA Symposium, Mathew Verdouw, CMT, CFTe, Global CEO and Founder at Market Analyst International, provided attendees with additional information on Market Analyst, an analysis platform that offers powerful features while being easy to use. Mathew has been at previous Symposiums and attendees will remember that he has demonstrated the most advanced features of MA. This year he focused on how easy it is to use. At the Symposium I had a chance to speak to Mathew.

I understand Market Analyst can do anything I want to do as a technical analyst – it creates unique charts, allows me to run quant screens and does back testing in addition to having other capabilities. Let’s talk a little about each of these capabilities.

All the standard charts plus a whole section that we call Market Intelligence (based on the concept of Business Intelligence). These charts (from Columns, to 3D Maps, to RRG to 3D scatter plots) allow the user to view collections of securities and easily identify the ones that deserve their attention. Quant screening is done through the Watchlists. This is the veritable analysis hub where any quantitative measure can be tracked.  

They sound very advanced.  How would someone get started using these features? Do you need to be a programmer?

 This is what we see as our primary job, making the software incredibly simple to use, but also allowing for deep quantitative analysis just below the surface. Most of our clients do not want to program, and they don’t need to. When they do have custom requirements, our team of specialists is able to help them.

Your charting capabilities go well beyond the standard offering. With built-in tools, I can do Elliott Wave or Gann analysis. Could you tell our viewers a little bit about the Gann capabilities that are built into Market Analyst?

Gann is the path where I personally was introduced into financial analysis. The best way I can describe it is an observation of the patterns of the market. Just like Fibonacci and others observed patterns in nature, Gann was one of the Fathers of observing how mathematical patterns exist in the market.  In MA, we cater to as many of the Gann principles that we can find, either those we read directly from his books or those that have been derived using the power of computers that he just did not have available. One of our goals is to make a way to statistically quantify various Gann techniques as I feel that has not been properly done so far.

Are there any other unique charting capabilities like that?

One of the ones I am most excited about is the RRG and how Julius has allowed us to apply our research and engineering to extract information that has never been seen before. In fact it allows us to create a quantitative process around a very graphical chart. The other one is our Breadth Engine.  We all know about Breadth and how it can be an early warning of a turn, but why limit it to advances and declines. The chart we built allows people to experiment with their own rules for what is defined as positive and negative.

On the quant side, I can screen for stocks meeting certain criteria such as moving average crossovers. How many indicators are built in?

We have 350 unique tools and indicators available. Most of those include options that allow the user to switch between styles so it can be 10 tools wrapped up in the one. For example, the humble Moving Average tool has 9 different styles.

What are some of the more unusual indicators that are built in?

Without a doubt the more advanced Astronomical modules. Over the last hundred years there have been continual explorations into identifying correlations between astronomical cycles and our markets. The difficulty is always the sheer size of the cycles that are involved. While it is a very specialized module for a small group, it highlights our willingness to work on modules which are just a bit unusual.

And I can create screens that combine these various indicators to find trading opportunities in any time frame?

Often yes. Sometimes the interpretation is very subjective and difficult to quantify in a watch list, but we are continually looking for ways to do so. Typically we see that there are three levels of analysis that coincide with the Long term, medium term and short term trends.  Quant models are great at the short to mid-term, but how do you overlay long term trends? We have all seen approaches to the market that worked great in a Bull market fail in a Bear. That is where the more esoteric and subjective analysis comes in. The lower the time frame, the more quantitative the approach needs to be. We try to help our clients to find the balance that suits their style and time horizon.

Based on talking to your users, which features do they use the most?

That depends on which group I am talking to. In the institutional world it is the publishing, including chart headers that allow for any script to be positioned where they want. In the retail world it is the simplicity that they are able to add charts and manage their work, in the Gann world it is the access to methods that save them hours of hand calculations.

Will there be any new products or upgrades to watch for in the coming year?

We have a lot of projects sitting on the schedule and ready to start. The big ones to watch for soon (assuming all is going to plan) are the Signal Tester and the new Back Tester. This will allow users to do true portfolio testing. There will be some overhauls to existing modules as we continually improve things and an extension to make the Scripting Language even more powerful.

 Is the software for beginners, expert technicians, day traders or what group would benefit the most from Market Analyst?

We cater for all so long as they have a strategic approach to the market. People that are expecting their software to be a magic wand are doomed to fail. We are a magnifier in that we can help with work-flow and analysis management, but for the complete novice, MA is not where they start. They need to learn about financial analysis, set up rules to follow and then we can help.

How can our readers learn more?

Head over to MAV7.com/MTA for more information.

Contributor(s)

Amber Hestla-Barnhart

Bio coming soon.

5 STEPS TO GETTING INVESTMENT RISK UNDER CONTROL

Editor’s note: this article was originally published in the Winter 2015 issue of Financial Executive magazine and is reprinted here with permission.

Without risk none of us would make any money in the markets, but managing trading risk is a lot more complicated that one might like. Often risk limits are set arbitrarily. Risk is impacted by trader performance, units traded, the typical risk per trade, and whether the risk per trade is statistically appropriate, or too high, or too low.  If the risk is too low that means you constantly see your stops hit and exceeded, only for the market turn back in your favor. If the risk is too high, you will stay in losing positions too long, and give up too much on profitable exits. Proper stops cannot be placed purely on one’s comfort level, or budget, but are dictated by market conditions and volatility.

Step One: Understanding Trader Performance Risk, and Percent Chance of Loss

Everything else equal, a trader with a good track record has a lower probability of losing the risk limit than a trader with a poor one.

Corporate traders are often inexperienced, as I was when I was transferred into international energy trading from engineering in 1983, just as the crude oil contract was introduced. So it’s not always a valid assumption professional traders are competent or well trained. Back then, energy traders only used fundamentals (defined as research combined with speculation), and setting risk limits as a gross value made sense. The best computing power we had on the trading floor was an 8086, and streaming data evaluation was not within reach.

Many years later, computing power can push lots of streaming data through very sophisticated algorithms evaluating risk in portfolios, even if they contain thousands of securities, in the blink of an eye. Thus we can set risk limits and control risk in a much more granular and nuanced way. It’s not a question of either fundamentals or technicals. Today, there’s no excuse not to do both.

This first step involves understanding the math that underlies basic risk calculations, and how they are impacted by trader performance. Here are the key formulae, derived from game theory.

 

                A = RL / {In(P) / [ln(1 – W) – ln(P * R)]}

                RL = A * (ln(P) / [ln(1 – W) – ln(P * R)]}

                P = ((RL * ((ln(1 – W)) – ln(P * R))/A))

Where:

                A = Amount of Risk per Trade

                RL = Risk Limit, or Total Capital Willing to Lose

                P = Percent Chance of Losing RL

`               W = Percentage of Time “Win”

                R = Win-to-Loss Ratio

Let’s assume the risk limit is $100,000. The trader will be “shut down” if that much is lost. However, there’s a big difference between a 25 percent chance of losing $100,000, versus a more tolerable 0.25 percent. The question becomes how much of a chance is being taken of losing “everything”. W is the percent of times a profit is made. So if 100 trades are taken and 55 are winners, the W value is 55 percent, or 0.55. R is the ratio of the average wins, say $10,000, and the average losses, say $5,000, with R, then the win to loss ratio equal to 2.0. Using various inputs with a range of P values produces the following:

The table shows that risk of ruin can be by cut by a factor of 10 but cutting the risk per trade by half. Dropping from 1 percent risk to only 0.25 percent, a 75 percent further reduction, only takes a 24 percent cut. So, cutting the amount risked per trade has outsized payouts relative to the probability that the RL will be lost. Also cutting risk per trade by about one-third, would allow a more conservative assumption of W = 1.5:1 for untried traders.

With a % wins of 40%, the win to loss ratio would have to rise to 3.67 for the same results. Many pundits point out that it’s possible to have a winning track record with less than 50% wins. The problem is that this scenario usually relies on outlier wins, as opposed to steady wins in the 3.5 to 4.0 range. Since outliers are not typical, one must be wary of such assumptions.

Kase has developed more complex Monte Carlo models to simulate outliers both in wins and losses and in the percent wins for a more nuanced view, and to track trader performance against statistical expectations. However, plugging the formulae above into a spreadsheet is a good place to start.

Step two involves translating risk per trade into risk per unit and units per trade in a non-arbitrary fashion.

The risk per unit is the point at which a stop would be placed. It’s necessary to assess the degree of risk inherent in a given market, and design a stop suitable for it. Being ex-Navy, I like to use a nautical analogy. Sailors must evaluate conditions, such as the height of the waves, wind speed, direction, and the like. You risk ruin choosing to sail a dinghy in a raging Atlantic storm, because it’s price is consistent with your budget.

Step Two: Measuring Risk

The primary measure of trading risk is True Range (usually written TrueRange), and it is this value that should be the basis of setting stops. TrueRange is proportional to local volatility. TrueRange = maximum(H, C[1]) – minimum(L, C[1]). This is similar to a bar’s high low range, but includes gaps between the previous bar’s close and the current bar’s high or low. The chart below on the left shows a TrueRange (red) and High-Low range (blue) as identical. The middle chart shows the low of the TrueRange at the previous day’s close, below the low, and to the right also at the previous day’s close, above the high.

TrueRange versus High-Low Range

Nomenclature is H = high, L = low, C = close, and [n] = bars back when n is the number of bars back. This bar is always bar[0], though it’s customary to drop [0], as it’s understood.

TrueRange may be calculated over multiple bar lengths, such as sets of three bars.

Step Three: The amount of risk per unit, or setting stops is next.

Simple stops might be based on a multiple of Average TrueRange (ATR). Typically a value of around 1.7 would be considered a warning, or even an exit if broken on a closing basis, and a multiple of 3 to 4 works for a must-exit point. Depending on your exit strategy a stop using a multiple between 1.7 and 4 should be used to determine the risk per unit.

ATR is available in most, if not all, charting platforms, but if yours doesn’t have it, it’s easy to calculate on a spreadsheet.

Because the standard deviation of TrueRange varies, Kase developed an improved stop called the DevStop. It uses multiple standard deviations over the mean TrueRange based on two-bar sets, corrected for right hand skew. Using two-bar calculations helps capture some directional information on whether bars are overlapping due to oscillations or moving directionally. Because these stops are statistical, a setting of 1.0 standard deviations over-the-mean captures about 84 percent of the observation on a cumulative basis, etc.

Google Daily ATR Stop (Multipliers 1.7, 3) & Kase DevStops (Standard Deviations 1, 3.6),
Created with TradeStation. ®TradeStation Technologies, Inc. All right reserved.

The chart above shows the ATR stops in the top pane and DevStops in the lower, both based on 30 periods. The placement of the stops on the chart is driven by a 10- and 21-period simple moving average. When the 10-period moving average is below the 21-period moving average, the stops “trail” the lowest low, to which they are added, and vice versa.

Step Four: As Time Frame Rises, Risk Rises on a Diminishing Rate

Even though you might be looking at a daily chart, weekly charts are often preferable, and sometimes even monthly, as the oscillations in many if not most securities are statistically too large to manage on a daily. Remember the market doesn’t care about your budget. If the market dictates a monthly chart, so it is. The key is to know how large your stop must be and set your other risk limits accordingly. Keep your risk profile at the same or a lower level by adjusting units traded.

By doing this, keeping risk the same, you might actually be improving performance because you will be hitting stops and churning much less often, and price action smoothes out.

A big benefit in moving up in time is that the risk only increases proportionally to the square root of time, and in real life, often less. So moving to a higher time frame has diminishing costs.  Moving up by a factor of 25 only increases risk by 5, and from a daily to a monthly about 4 or so. Again, in actuality the impact of outliers is often felt less by the bigger bars, so the risk increase might be dampened.

The penalty is on the downside. If you want to cut the risk inherent on a daily chart by a factor of 3, you need to trade 9 bars per day. Dropping down to a lower time frame, where an oscillation on a daily bar becomes a trend on an intraday is an option, but with diminishing value. The impact of outliers and gaps hits harder. Also this requires active trading that is impractical for moderate to large volumes.

Case Study: AAPL

Let’s look at two AAPL (Apple Inc.) charts. The one on the left is a daily and on the right a weekly chart. Each shows price activity from about March 2014 to January 15, 2015. Each chart has double-sided DevStops, with a 10 and 21 simple moving average in the bottom pane. Here the “normal” stops, in the direction crossover, are shown in brown. If there’s a “mini trend” in the opposite direction, those are shown in aqua. The reason these mini-trend stops are shown is that sometimes traders reverse positions before the moving averages do, and therefore, traders have asked us for stops in that case.

Looking at the stops changing color and the moving averages crossing back and forth, it’s clear that although AAPL was trending, the daily was choppy. The weekly only had one back and forth cross early and then moved up cleanly. One might argue that it’s only natural for a weekly to have fewer crossovers than a daily, but in this comparison, the daily is quite choppy for a trending market, regardless. (I call this type of market “hybrid” as it is choppy and trending at the same time). Note the moving averages are used to illustrate the point that regardless as one’s choice of system, there would be a fairly high churn on the daily, anyway. I count about 16 moving average crossover, 12 hits on the first level stop, six of which followed through to hit the second level. Only the early December hit was significant.

AAPL Daily and Weekly with Kase DevStops, and Double Simple Moving Average (10, 21)

On the weekly, price action looks fairly clean, and the first level stop was hit only twice: once during a corrective period in the fall, and then on the week ending December 12, 2014, at which time there was a close below it. Thus the weekly would have allowed the trade to run at least until then. This is not to suggest that the daily be ignored altogether, just that it should not be the primary chart monitored.

Take a closer look at the smallest of the purple values on the right of each chart. The daily’s value is 6.56 and the weekly’s is 10.44. This is the first stop’s risk. If we assume that one would exit on a close below this stop, the daily has eight exits, while the weekly only one, on December 12. However, the risk on the weekly is only 1.6 that of the daily even though the time has increased by a factor of 5, with a predicted increase of 2.23 (Ö5) or less. As noted above the actual increase is usually less.

The fact that risk of about $10.44 per unit must be taken might be unpleasant. However, if you look at the red arrows, it’s obvious that the statistically based stops inherently match up with highs and lows on the weekly, not the daily. Finally, look at the small number in green between the risk values. This is a gauge of chaotic activity, based on a ratio of standard deviation versus average range measures, called the Kaos ratio. The reading on the daily is 0.39 versus the weekly 0.33, showing that with the lower value, the weekly is probably a better choice.

Step Five: What to do next is crunch the numbers.

You can determine the risk per unit using the simple stop calculation, and put it into the formulae in Step One to figure out how many contracts to trade. The stop also can determine loss triggers at which you will monitor trades and traders more closely.

To cut out the spreadsheets, I’ve designed an algorithm that automatically pulls the largest stop value and does all the calculations. Based on a risk limit of $100,000, win-to-loss of 1.5, wins of 55%, and 0.25 percent, 0.50 percent, 1.0 percent, and 2.5 percent risk of ruin (as inputs into the algorithm), the risk per trade and units per trade are returned in the charts below.

AAPL Daily and Weekly with Kase Double Sided DevStops with Risk of Ruin Calculator

Comparing (on either chart) the “red” at a 2.5% risk of ruin with the “blue” at 0.25, a 10 fold reduction, a decrease of 38%, or a factor of 2.6 is required. Modest reductions in trading volume disproportionately diminish risk.

The risk per trade is the same on both charts, as it is purely driven by the manual inputs into the model. The number of units per trade is the risk per trade divided by the risk per unit (the stop value) pulled from the chart by the algorithm. Here there has been a five-fold increase in the bar length, but the units per trade drop by less than 30 percent.

In summary, simple risk of ruin calculations can be used to determine risk limit parameters. The key is to use TrueRange based stops to gauge the risk per unit. Risk does not increase at the same pace as bar length increases, which is favorable to using longer bar lengths. Reductions in trading volume disproportionately decrease risk. If stops are hit often, then try higher bar lengths. With the five steps done correctly, you can use stops to monitor individual trades and traders, as well as performance over time.

Contributor(s)

Cynthia A. Kase, CMT, MFTA

Cynthia A. Kase, CMT, MFTA is the president of Kase and Company, Inc. CTA, founded in 1992. With a BS UMass and an ME Northeastern, both in chemical engineering, she worked in that field for 10 years before beginning her trading career at...

THE ULTIMATE WAY TO BACK TEST YOUR BEST IDEAS: GLOBAL FINANCIAL DATA

Technical analysis is based on the idea that history tends to repeat and to determine if history repeats we need to study the past. It’s probably an understatement to say that Global Financial Data can help analysts study the past with data series that covers centuries.  As an example, U.S. government bonds have been traded for more than 220 years. A comprehensive backtest could be done using the entire data series to determine what happens in war and peace, high inflation and low inflation or any other economic, political or social conditions.

Before the MTA Symposium, I spoke with Dr. Bryan Taylor, President and Chief Economist for Global Financial Data.

Bryan, can you tell us a little about the data that GFD offers?

Global Financial Data offers the most extensive, historical database available anywhere.  We specialize in providing long-term data series on economic and financial data that are unavailable anywhere else.  We have gone to libraries, archives and other resources throughout the world to collect data from historical publications to extend series whose history is limited by electronic resources.  We have continuous data on the US Stock Market and Bond Market back to the 1700s.  Data for the UK goes back to the 1600s. Our goal is to provide our customers with the most extensive histories available in order that they can backtest their models and analyze charts over the maximum number of market cycles.

The Global Financial Database includes data on over 200 countries going back several centuries.  Our gold series goes back to 1252, inflation data go back to the 1200s, exchange rates back to the 1300s, interest rates back to the 1500s, economic data back to the 1100s, and equity indices back to the 1600s.  We also have the most extensive databases on individual stocks for the United States and the United Kingdom that are available anywhere.  The US database covers over 50,000 securities back to the 1700s. Our database avoids the survivorship bias because it includes delisted stocks.  It also avoids the exchange bias because it provides complete coverage of the NYSE, AMEX and regional exchanges back to their inception.  Our UK database includes data on individual companies back to the 1600s when trading at the coffee houses in London began.

As an example, Global Financial Data could allow us to see how commodity prices performed during a global epidemic like when the bubonic plaque swept through Europe in the 1300’s?

There are numerous series that would allow a user to analyze the impact of the bubonic plague on the economy.  We have data on inflation, exchange rates, commodity prices and population going back to the 1300s which you could use to analyze the impact of this epidemic.  If you want to analyze the Napoleonic Wars or the U.S. Civil War, we also have stock indices and stock data to analyze how individual battles affected the markets.

One of our most recent additions is our Events in Time tool.  This covers over 60,000 events over the past 2000 years.  If you see that the market declined in 1907 and don’t know why, you can use this tool to find out the headlines that impacted the market at that point in time.  You can search by date, by topic, or by country.  You can find out when bull and bear markets occurred, when recessions occurred, what wars were going on and which battles occurred between the countries at war, changes in currencies, who the leaders were, and so forth.  This tool enables you to understand what factors influenced markets in the past if you don’t have a Ph.D. in history.  You can find out what happened to the Latin Monetary Union or the Scandinavian Monetary Union.  You can find out when countries were on the Gold Standard and when they left.  Global Financial Data goes beyond the data to provide you information on the causes of changes in financial markets.

You provide data on stock and commodity markets. What other data is available?

In addition to our coverage of financial markets, we have extensive financial data.  We provide long-term series on GDP, inflation, the money supply, imports and exports, real estate data, and related topics.  Since the current “Great Recession” has had such an impact on the global economy, economists have tried to find parallels in the past and we have incorporated the results of much of this research into our database.  You can find out how periods of low growth, low interest rates, free trade, and other factors that did exist in the 1800s impacted markets back then, and possibly today.

And all of this data can be charted with your platform?

All of our data are available on line through our website.  We also provide API access as well as Excel worksheets to download the data.  Our charting tools not only allow you to overlay numerous technical indicators on the data, but you can also compare multiple series.  We also provide overlays for world leaders, recessions, bull and bear markets and other variables so you can see if these changes impacted the indicators you are analyzing.

Finaeon would allow me to overlay economic data with market data, for example seeing how interest rates respond to changes in the economy over the past few hundred years?

Finaeon is our newest product that provides software capabilities that are unavailable on the Global Financial Data platform.  The charting tools have been updated and improved.  You can calculate CAPE Ratios at different maturities from over 50 countries.  You have access to the Events in Time tool which allows you to get information on over 60,000 historical events over the past 2000 years to see what caused market changes.  Finaeon allows our Global Financial Data users to step up to the next level.

You also have current market data. How often are your data bases updated?

The Global Financial Database and US Stocks Database are updated on a daily basis.  Whether you are looking at interest rates, exchange rates, stock market indices, commodity prices, data on individual stocks or other aspects of financial markets, the data is available each day.  We also get extensive data feeds from the Federal Reserve, Eurostat, the World Bank and other resources so economic data remain up-to-date and is available as soon as governments calculate the data.

What kind of analyst would benefit from your services?

All analysts would benefit from our data because we provide data on more market cycles than is available anywhere else.  Our users backtest their models with our data to generate algorithms that help them to outperform the markets.  Analysts also provide reports to their clients and they love to impress their clients with data from our database in order to prove to their clients that they have a better understanding of the markets than their competitors.  Our goal is to provide our customers with the data they need so they can do their own analysis and don’t have to waste their valuable time trying to find the data they need.

To learn more about Global Financial Data, email Pierre Gendreau at pgendreau@globalfinancialdata.com.

Contributor(s)

Amber Hestla-Barnhart

Bio coming soon.

WILT THE STILT GETS THE JILT

Editor’s note: this was originally published at the Global Financial Data blog and is reprinted here with permission.

Global Financial Data has stock histories for over 50,000 securities.  Some of these stocks cover centuries of data; others are quite short, lasting only a few months or even a few days.  One of the most interesting of these is Wilt Chamberlain’s Restaurants, Inc. for which there are only two days of data.

Wilt the Stilt

Wilt Chamberlain was one of the greatest, some would argue the greatest, basketball players in history.  He holds 71 NBA records, and he is the only person to score 100 points in a single game. He averaged more than 40 to 50 points per game in some seasons, and in one season he played every minute of every game. Chamberlain was 7-foot 1-inch and played for the University of Kansas, Harlem Globetrotters, the Philadelphia/San Francisco Warriors, Philadelphia 76ers and the Los Angeles Lakers. He dominated the NBA between 1959 and 1973 and left a legacy that few can match.

After Chamberlain left the NBA, he promoted the short-lived International Volleyball Association, wrote a book, appeared in the movie Conan the Destroyer, and tried several business ventures.  One of these was Wilt Chamberlain’s Restaurants, Inc.

Wilt Chamberlain’s restaurant opened on December 20th, 1990 in Boca Raton, Florida. The restaurant was a sports-themed, casual-dining family restaurant. The goal of the restaurant was fine food and service for families, a sports bar for drinkers and an entertainment complex for kids of all ages. The restaurant had over fifty televisions broadcasting sports events for its patrons as well as a basketball court and hoops where customers could shoot a few shots while enjoying their drinks or waiting for a table. The restaurant had a live arcade with over 40 games, and a redemption center where customers could either cash in their tickets or purchase sports-related goods.

The IPO That Went PU

The restaurant was successful, so in 1992, Wilt Chamberlain decided to go public.  The Hard Rock Café had begun its expansion in 1982 and in 1991 Planet Hollywood was founded by Sylvester Stallone, Bruce Willis, Demi Moore and Arnold Schwarzenegger.  There was no reason why a sports-themed restaurant shouldn’t succeed as well, but to do so Wilt Chamberlain would need more capital.

Chamberlin contracted with New York-based brokerage Meyers, Pollock, Robbins Inc. to go public.  The goal was to raise $8 million through the initial public offering (IPO) in order to fund the restaurant’s expansion. The company filed an SB-2 registration in which the brokerage firm said they would offer 1.4 million shares, priced at $6 to $8. The company was had reported a profit of $222,706 for the nine months ended Sept. 30, 1992.

Wilt Chamberlain signed an agreement to allow his name, likeness and persona to be used in connection with marketing the company. Although there was only one Wilt Chamberlain restaurant at the time, the company had plans to open additional restaurants across the United States and possibly worldwide. The company would trade under the symbol WILT.

Unfortunately, the IPO was a disaster.  Although Meyers, Pollock, Robbins, Inc. had been in business for 50 years, they had never underwritten an IPO. Their inexperience contributed to the fiasco, and they botched the placement completely.  Most investment bankers will guarantee the IPO price for 30 days after the debut, but shares in Wilt Chamberlain Restaurants, Inc. stayed above the offering price for only a few minutes.

Wilt Chamberlain Restaurants, Inc. went public at $7 a share on Thursday, February 11, 1993. The first trade in the stock was at $7.50, but the price fell to $6.75 a few minutes later.  Over one million shares traded on its opening day, and the stock price closed at $6, $1 below the IPO price. This brought on short-sellers when it was apparent the firm could not or would not support the stock. By the next day, the stock closed at $4.625 a share.  Everyone who owned stock in the company was losing money, many of whom were clients of Meyers, Pollock, Robbins, Inc.

IPO Money on the Rebound

Meyers, Pollock, Robbins, Inc. had gauged the price of the stock inaccurately and were unable to support the stock when it fell below its offer price. Over the weekend, the company and the underwriter huddled to figure out a strategy for their failed IPO. Before trading began on Tuesday, Meyers, Pollock, Robbins, Inc. announced that they would cancel the public offering. The underwriter had the legal right to do this because an IPO can be cancelled if the shares have not been distributed and no trades have settled.

What made the cancellation unusual was that the only reason given for pulling the IPO was the fall in the stock price. Cancellation of IPOs is rare, but does happen. An IPO in July 1992 was cancelled when the Central Garden and Pet Co. of Lafayette, CA suffered a warehouse fire on the day of their public offering.  The most infamous case of a failed IPO was when the BATS electronic platform IPO’d their own stock in 2012 and computer crashes on BATS caused the stock to fall to pennies from its initial $15 IPO price.

Wilt Chamberlain died in 1999. The restaurant remained popular and changed hands several times over the next few years, ultimately ending up in the hands of Joel Kron. A dispute between the landlord and Kron led to the closure of the restaurant in 2007.  Though the restaurant is gone, memorabilia from the restaurant can still be found on Ebay.  That and a failed IPO are all that remain of Wilt Chamberlain Restaurants, Inc.

Contributor(s)

Dr. Bryan Taylor

Dr. Bryan Taylor President & Chief Economist, Global Financial Data Dr. Bryan Taylor serves as President and Chief Economist for Global Financial Data. He received his B.A. from Rhodes College, his M.A. from the University of South Carolina in International Relations, and...