Jim O’Shaughnessy on Active Management

April 23, 2017

If you want to succeed with active management, I would suggest this is a must watch.

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Process over Short-Term Outcome

March 20, 2017

Jim O’Shaughnessey, author of What Works on Wall Street, recently wrote about 7 traits that he believes are required for active investors to win in the long run.  I fully agree with all 7, but I found #2 on his list to be particularly compelling:

2. Successful Active Investors Value Process over Outcome.

“If you can’t describe what you are doing as a process, you don’t know what you’re doing.”

~W. Edwards Deming
The vast majority of investors make investment choices based upon the past performance of a manager or investment strategy. So much so that SEC Rule 156 requires all money managers to include the disclosure that “past performance is not indicative of future results.” It’s ubiquitous–and routinely ignored by both managers and their clients. In keeping with human nature, we just can’t help ourselves when confronted with great or lousy recent performance. “What’s his/her track record?” is probably investors’ most frequently asked question when considering a fund or investment strategy. And, as mentioned above, the vast majority of investors are most concerned with how an investment did over the last one- or three-year period.

Yet successful active investors go further and ask “what’s his or her process in making investment decisions?”  Outcomes are important, but it’s much more important to study and understand the underlying process that led to the outcome, be it good or bad. If you only focus on outcomes, you have no idea if the process that generated it is superior or inferior. This leads to performance chasing and relying far too much on recent outcomes to be of any practical use.  Indeed, shorter-term performance can be positively misleading.

Look at a simple and intuitive strategy of buying the 50 stocks with the best annual sales gains. Consider this not in the abstract, but in the context of what had happened in the previous five years:

Year                            Annual Return            S&P 500 return

Year one                      7.90%                          16.48%

Year two                     32.20%                        12.45%

Year three                   -5.95%                         -10.06%

Year four                     107.37%                     23.98%

Year five                     20.37%                        11.06%


Average Annual

Return                         27.34%                        10.16%

$10,000 invested in the strategy grew to $33,482, dwarfing the same investment in the S&P 500, which grew to $16,220. The three-year return (which is the metric that almost all investors look at when deciding if they want to invest or not) was even more compelling, with the strategy returning an average annual return of 32.90% compared to just 7.39% for the S&P 500.

Also consider that these returns would not appear in a vacuum—if it was a mutual fund it would probably have a five star Morningstar rating, it would likely be featured in business news stories quite favorably and the long-term “proof” of the last five years would say that this intuitive strategy made a great deal of sense and therefore attract a lot of investors.

Here’s the catch—the returns are for the period from 1964 through 1968, when, much like the late 1990s, speculative stocks soared. Investors without access to the historical results for this investment strategy would not have the perspective that the long term outlook reveals, and thus might have been tempted to invest in this strategy right before it went on to crash and burn. As the data from What Works on Wall Street make plain, over the very long term, this is a horrible strategy that returns less than U.S. T-bills over the long-term.

Had an investor had access to long-term returns, he or she would have seen that buying stocks based just on their annual growth of sales was a horrible way to invest—the strategy returned just 3.88 percent per year between 1964 and 2009! $10,000 invested in the 50 stocks from All Stocks with the best annual sales growth grew to just $57,631 at the end of 2009, whereas the same $10,000 invested in U.S. T-Bills compounded at 5.57 percent per year, turning $10,0000 into $120,778. In contrast, if the investor had simply put the money in an index like the S&P 500, the $10,000 would have earned 9.46 percent per year, with the $10,000 growing to $639,144! What the investor would have missed during the phase of exciting performance for this strategy is that valuation matters, and it matters a lot. What investors missed was that these types of stocks usually are very expensive, and very expensive stocks rarely make good on the promise of their sky-high valuations.

Thus, when evaluating an underlying process, it’s important to decide if it makes sense. The best way to do that is to look at how the process has fared over long periods of time. This allows you to better estimate whether the short-term results are due to luck or skill. We like to look at strategies rolling base rates—this creates a “movie” as opposed to a “snapshot” of how strategies perform in a variety of market environments.

This is a philosophy you’ve repeatedly heard from us as well.  Short-term outcomes are important, but process ultimately determines long-term results.  Among the ways that this can be illustrated is by looking as some of our white papers on relative strength investing.  John Lewis’ white paper, Point and Figure Relative Strength Signals detailed the long-term investment results of a relative strength process that took 1,000 U.S. stocks and categorized them into one of four portfolios based on their PnF relative strength signal (BX-buy signal and in a column of X’s; BO–buy signal and in a column of O’s; SX—sell signal and in a column of X’s; or SO—sell signal and in a column of O’s).  Portfolios were equal-weighted and rebalanced on a monthly basis.  Performance of these four portfolios from 12/31/1989 to 12/31/2015 is shown below.  As detailed in the paper, following a disciplined process of investing in stocks with the highest momentum (BX portfolio) generated significant outperformance over this test period.

base rates

Click here for disclosures

Long-term success with active management comes from doing sufficient due diligence to either design a robust investment process yourself (or to employ one designed by someone else) and then to execute, execute, execute.  If the process is sound, long-term outcomes should take care of themselves.

The performance above is based on total return, inclusive of dividends, but does not include transaction costs.  Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  Some performance information presented is the result of back-tested performance.  Back-tested performance is hypothetical and is provided for informational purposes to illustrate the effects of the strategy during a specific period. The hypothetical returns have been developed and tested by DWA, but have not been verified by any third party and are unaudited. Back-testing performance differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight. Model performance data (both backtested and live) does not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money.  Past performance is not indicative of future results. Potential for profits is accompanied by possibility of loss.

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A Game Plan For Incorporating “The Totality of Information”

November 8, 2016

Jason Zweig recently made a key observation during an interview with Russ Roberts (via The Irrelevant Investor):

I think if there’s one overriding theme to the book, one of the things I’ve tried to get across in The Devil’s Financial Dictionary is the importance of just being humble before the financial markets. I mean people are humble before nature- think about when you stand on the rim of the Grand Canyon, or you walk to the edge of the ocean, or you look up at the stars, people feel this sense of awe and wonder and smallness because we are small when we compare ourselves with the natural world. Well individuals, and for that matter, policy makers, are small when we compare ourselves with the financial markets, but most of us forget that.  And we think, oh we’ll we have better data or we know something the other guy doesn’t, and in fact we should have that same sense of just being a spec of sand on a long beach and just remember that whatever we know is very small compared to the totality of the information that’s out there.

This begs the question, what is your edge as a financial advisor?  If your edge is “knowing something the other guy doesn’t” how realistic is that edge?  So much of what goes on in the investment management business is centered around people believing that they have insight into why a given security is mispriced.  Taking Zweig’s advice to stay humble as it relates to the totality of the information that is out there goes to the essence of  technical analysis.  For technicians, and specifically those adhering to a trend following/relative strength-based approach to investing, our edge has nothing to do with identifying mispriced securities.  The prices are what they are—the simple intersection of supply and demand.  Our edge is having a disciplined method of identifying and participating in the strongest trends in the market.  Thanks to the power of technology, our trend following models see and incorporate all information in the market that is relevant to our buy and sell signals.

If you need some ammo to help make the case for such a trend following approach.  I would suggest reading (or re-reading) some of John Lewis’ white papers on the topic.  Some of my key takeaways from these white papers:

  • Price is sufficient as an input for trend following models.  There is no need to complicate things with other inputs.
  • Trend following works on stocks, ETFs, and asset classes
  • Relative Strength doesn’t work all the time, but it does work a high percentage of the time
  • Discipline is the key.  Rather than focus on constantly tweaking a relative strength model, it is best to do thorough research up front than then focus on execution after that.  Constantly tweaking a trend following model is no different than not having any discipline.
  • There are best practices when it comes to relative strength models.  Those white papers detail best practices.  Some of those best practices including knowing what box size to use on a PnF relative strength chart and where to set your relative strength rank buy and sell threshold for a given objective.

As a subscriber to DWA research, you have the necessary tools at your fingertips to employ such relative strength strategies.  There is no need to recreate the wheel here.  We’ve done the heavy lifting for you.  Team Builder, Matrix Plus, the Models Page, the Technical Attributes, and Fund Score, DALI.  It’s all there.

It is possible to be humble and confident at the same time.   Humility is demonstrated by not looking beyond price.  The confidence comes from embracing a trend following model designed to interpret those prices in a systematic way.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Dialing Down the Noise

September 27, 2016

The Harvard Business Review’s October 2016 Issue includes a deep look at decision making by authors Kahneman, Rosenfield, Ghandhi, and Blaser.  Their conclusion: “noise”, left unchecked, renders decision making highly inconsistent.

At a global financial services firm we worked with, a longtime customer accidentally submitted the same application file to two offices. Though the employees who reviewed the file were supposed to follow the same guidelines—and thus arrive at similar outcomes—the separate offices returned very different quotes. Taken aback, the customer gave the business to a competitor. From the point of view of the firm, employees in the same role should have been interchangeable, but in this case they were not. Unfortunately, this is a common problem.

Professionals in many organizations are assigned arbitrarily to cases: appraisers in credit-rating agencies, physicians in emergency rooms, underwriters of loans and insurance, and others. Organizations expect consistency from these professionals: Identical cases should be treated similarly, if not identically. The problem is that humans are unreliable decision makers; their judgments are strongly influenced by irrelevant factors, such as their current mood, the time since their last meal, and the weather. We call the chance variability of judgments noise. It is an invisible tax on the bottom line of many companies.

Some jobs are noise-free. Clerks at a bank or a post office perform complex tasks, but they must follow strict rules that limit subjective judgment and guarantee, by design, that identical cases will be treated identically. In contrast, medical professionals, loan officers, project managers, judges, and executives all make judgment calls, which are guided by informal experience and general principles rather than by rigid rules. And if they don’t reach precisely the same answer that every other person in their role would, that’s acceptable; this is what we mean when we say that a decision is “a matter of judgment.” A firm whose employees exercise judgment does not expect decisions to be entirely free of noise. But often noise is far above the level that executives would consider tolerable—and they are completely unaware of it.

The prevalence of noise has been demonstrated in several studies. Academic researchers have repeatedly confirmed that professionals often contradict their own prior judgments when given the same data on different occasions. For instance, when software developers were asked on two separate days to estimate the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge. Research has confirmed that in many tasks, experts’ decisions are highly variable: valuing stocks, appraising real estate, sentencing criminals, evaluating job performance, auditing financial statements, and more. The unavoidable conclusion is that professionals often make decisions that deviate significantly from those of their peers, from their own prior decisions, and from rules that they themselves claim to follow.

My emphasis added.  Among the author’s proposed solutions to the “noise” problem was the was following:

The most radical solution to the noise problem is to replace human judgment with formal rules—known as algorithms—that use the data about a case to produce a prediction or a decision. People have competed against algorithms in several hundred contests of accuracy over the past 60 years, in tasks ranging from predicting the life expectancy of cancer patients to predicting the success of graduate students. Algorithms were more accurate than human professionals in about half the studies, and approximately tied with the humans in the others. The ties should also count as victories for the algorithms, which are more cost-effective.

This will sound very similar to advice that Dorsey Wright has been giving for many years: Embrace models!  Try as we might to be consistent, without the framework of a systematic investment model, our own subjective decision making will be all over the place.  Then, how can we tell if our investment success or failure is the result of skill or just good or bad luck?  Of course, you can’t simply blindly adhere to just any systematic investment model.  The decision rules upon which the model has been built must stack the odds in your favor.  Extensive testing, as is detailed here, has give us the necessary input to build systematic relative strength strategies that “dial down the noise” and allow us to focus on execution of a well-designed investment process.

Focus on the process and the results will take care of themselves.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Something for the Pitch Book

August 16, 2016

Brian Pornoy, Director of Investor Education at Virtus, recently included the following thought-provoking chart in his commentary titled What Does the Stock Market Owe You?:

BP blog 6_23

The chart encompasses daily snapshots of the total return (i.e., dividends included) of the S&P 500®, a broad index of U.S. stocks, from 1928 until today. Reading the picture from left to right, what you’re looking at are “rolling” time periods of increasing duration. A rolling time period thinly slices our windows on market returns over whatever period we choose to define them. So, for example, a rolling three-year period could be the market returns from November 1, 1953 to October 31, 1956. The next three-year period would be November 2, 1953 to November 1, 1956. And so on. I looked at the rolling returns over periods ranging from one to ten years in length. All in, it encompasses tens of thousands of observations.

Pornoy’s conclusion were as follows:

  • Notice that the average return over these different periods is remarkably consistent. It’s about 10%. Not surprisingly, many people reflexively believe that “the market” returns about 10% per year. They’re not whistling Dixie. Based on history, that’s about right.
  • Yet that mode of thinking—asking “what’s the average?”—reflects the brain’s bias toward locking onto specific point estimates. We prefer to fixate on a precise number and reject, often subconsciously, thinking in statistical, probabilistic terms. In other words, we don’t naturally play the odds. Sure, to say instead that the market returns “about 8-12%” per year is a baby step in the right direction. Unfortunately the world is much messier than that. The following observations, therefore, force us out of our comfort zone, as they force us to think in terms of dynamic ranges and probabilities.
  • For each of the rolling periods, I show the maximum and minimum returns: the biggest gains and the biggest losses. Thus, over thousands of rolling one-year periods going back to 1928, the largest one-year gain was 171% and the largest one-year loss was -71%. This range is massive. (Note that the most extreme results occurred during the 1930s.)
  • What this tells us is clear: In the short term (please forget days and months, even a year counts as short term), stock market returns are extremely volatile; they are basically random. The fact that the rolling one-year “average” is around 11% tells you nearly nothing about what the market can and will deliver you. Over the past century, we’ve seen one-year periods when some investors nearly tripled their money, while others lost more than two-thirds of it.

Those investors/pundits who are predisposed to be bullish can use data such as this to argue for aggressive allocations.  After all, what’s not to like about those average and max returns!  Those investors/pundits who are predisposed to be bearish can use the same data to argue for conservative allocations.  The latter group will simply focus on the worst outcomes over those rolling time periods.

It also occurs to me that an advisor who has embraced Dorsey Wright into their practice could use that chart to demonstrate to a client or prospect the value that they can bring to the table.  We all know what the market has done in the past.  From that history, we can clearly observe the massive degree of variability.  Armed with that knowledge, I’m not sure how many investors will continue to be fully comfortable with a strategic approach to asset allocation that offers little flexibility.

Something to consider adding to the pitch book.

Past performance is no guarantee of future returns.  Dorsey Wright is a research provider to Virtus.

HT: Abnormal Returns

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Curiosity Conversations

May 9, 2016

Brian Grazer is the producer of A Beautiful Mind, Apollo 13, Splash, Arrested Development, 24, 8 Mile Empire, and J. Edgar, among others.  His films and TV shows have been nominated for forty-three Academy Awards and 149 Emmys.  In 2007, he was named one of Time‘s 100 Most Influential People in the World.

He also has a habit of constantly seeking out “curiosity conversations” and has interviewed the likes of Andy Warhol, Barack Obama, Princess Diana, Michael Jackson, Norman Mailer and many more.  From his book, A Curious Mind:

I started having what I called curiosity conversations.  At first, they were just inside the business.  For a long time, I had a rule for myself:  I had to meet one new person in the entertainment business every day.  But pretty quickly I realized that I could actually reach out and talk to anyone, in any business that I was curious about.  It’s not just showbiz people who are willing to talk about themselves and their work–everyone is.

For thirty-five years, I’ve been tracking down people about whom I was curious and asking if I could sit down with them for an hour.  I’ve had as few as a dozen curiosity conversations in a year, but sometimes I’ve done them as often as once a week.  My goal was always at least one every two weeks.  Once I started doing the curiosity conversations as a practice, my only rule for myself was that the people had to be from outside the world of movies and TV…

…I have meetings and phone calls and conversations all day long.  For me, every one of those is in fact a curiosity conversation.  I don’t just use curiosity to get to meet famous people, or to find good scripts.  I use curiosity to make sure movies get made—on budget, on time, and with the most powerful storytelling possible.  I’ve discovered that even when you’re in charge, you are often much more effective asking questions than giving orders…

…I use curiosity as a management tool.  I use it to help me be outgoing.  I use curiosity to power my self-confidence.  I use it to avoid getting into a rut, and I use it to manage my own worries…

You’re born curious, and no matter how much battering your curiosity has taken, it’s standing by, ready to be awakened

…Curiosity itself is essential to survival.  But the power of human development comes from being able to share what we learn, and to accumulate it.  And that’s what stories are: shared knowledge

…I want the opportunity to be different.  Where do I get the confidence to be different?  A lot of it comes from curiosity….

…That’s what curiosity has done for me, and what I think it can do for almost anyone.  It can give you the courage to be adventurous and ambitious.

My emphasis added.  I could go on, but hopefully the excepts that I have shared give you a flavor of the role that curiosity conversations have played in defining and shaping Brian Grazer’s life.  I found his book to be fascinating.

And I couldn’t help but think of its implications to our business.  What is the difference between those who succeed in financial services and those who struggle along or fail?  Surely, connections, storytelling ability, confidence, persistence, and wisdom are among the defining characteristics of those who succeed.  And what better way to develop those attributes that to constantly seek out opportunities to learn from people of all walks of life.  Perhaps, you are saying to yourself, “Yea, but if I reach out to someone and ask for an hour of their time to learn from them, they will surely perceive a hidden agenda of simply trying to turn them into a client.”  There is no doubt that this will be a major obstacle.  Brian Grazer’s book was filled with all the opposition he received from people about sitting down with him for an interview.  But, he persisted.  Furthermore, as Grazer points out, people generally like talking about themselves and many people will be flattered by your request.

Financial services is nothing if not a people business.  A more organized and concerted effort to engage in these types of conversations could very well enrich our lives in many ways beyond the monetary.

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Probable vs. Possible

January 13, 2016

Good advice from Jim O’Shaughnessy:

Investors should make decisions using the long-term base rates a strategy exhibits—in other words, they should concentrate on what is probable rather than what is possible. If you organized your life around things that might possibly happen to you, you’d probably never leave your house, and when you did, it would only be to buy a lottery ticket. Consider, on a drive to the supermarket, it is highly probable that you will get there, buy your groceries and get back home to unpack them without incident. But what’s possible? Almost anything—it’s possible a plane flying overhead could lose an engine falling directly on your car and instantly killing you. It’s possible another car runs a red light and kills you on impact. It’s possible that It’s possible that you get carjacked and your assailant kills you in the process. You get the point—anything is possible buy highly improbable. It’s only when you think in terms of probability that you will get in your car and go, yet few investors do so when making investment decisions. Our brains create cause and effect narratives after something has occurred that seem to make sense, however improbable the event. Witness anyone who invested in the stocks with the highest sales gains after a great short-term run.

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Embracing Automation

December 17, 2015

The Harvard Business Review has a nice write-up of a McKinsey study looking at the benefits to organizations that automate as many daily tasks as possible:

To pinpoint the opportunities, we have looked at about 2,000 activities that are performed in various occupations across the U.S. economy. We find that, from a technical standpoint, work that occupies 45% of employee time could be automated by adapting currently available or demonstrated technology. However, less than 5% of jobs could be fully automated—that is, every activity could be handled by a machine.We estimate, that for 60% of existing US jobs, 30% or more of current work activities can be automated by with available or announced technologies. In other words, for the majority of US jobs, a day and a half’s worth of activities in each work week can be automated…

…The over-arching implication from our research into automating tasks is that roles will be redesigned and organizations will have to become very good at understanding where machines can do a better job, where humans have the edge, and how to reinvent processes to make the most of both types of talent. The largest benefits of information technology accrue to organizations that analyze their processes carefully to determine how smart machines can enhance and transform them—rather than organizations that simply automate old activities.

Embracing automation has been a hallmark of our work here at Dorsey Wright over the years.  Reading the above referenced study took me on a trip down memory lane as I thought about how automation has changed our business for the better.  When I asked John Lewis, our Senior Portfolio Manager and the person most responsible for automating many of our investment strategies, for feedback on this he gave me the following thoughts:

Automating the investment process allows us to examine a huge universe of securities without needing to have a huge team of analysts.  We can run many strategies and we can follow many different markets (domestic, small cap, emerging, developed, etc….) without having to staff up and get analysts up the curve.  This also helps keep costs down, which is very important as fee compression has been happening since commissions were deregulated in the 1970’s.

Automation has also allowed us to be more disciplined and not let emotion get in the way of the investment process.  That has been one of the largest benefits over time.  We used to do all of the analysis manually, but it wasn’t any better.  You begin to realize that computer rankings are relentless – they never have a bad day or take a vacation.  Day after day you get the exact same unemotional ranking no matter what is happening in the market.

Automating repetitive tasks has also freed up our time to focus on more important things.  You can’t automate everything.  We have a lot of functions that require the time of an experienced person that can’t be automated.  A few examples of this include: new product development, trying to make existing strategies better, and client service.  By automating everyday tasks (like loading data into databases and running ranks and models) we can devote more resources to areas that need experienced people.

We never feel like we are “behind” in the investment process.  When the markets do crazy things, our ranks still run overnight and all of our models are updated and ready for us in the morning.  We are never trying to figure out why something is happening or what we should be doing.  That is a huge deal for that small sample of time when everyone is wondering what they should do.

Nobody likes doing repetitive, clerical tasks day after day so automating them makes for a better quality of life in the workplace.  People are just happier not having to copy column C to column D in Excel every day.  When you have happier people they are more productive.

We’re not far from the time of the year where people get introspective and start making goals for the new year.  It might be a healthy activity to review your business and evaluate what percentage of your time is spent on repetitive tasks.  Automating those tasks may well be the key to taking your business to the next level in the years ahead.

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(Unwarranted?) Pessimism Explained

December 16, 2015

Interesting perspective from Morgan Housel on “Why Your Parents Are Dissappointed In You”:

Baby boomers are disappointed in their children. The younger generation whines too much, feels entitled to success, and lacks the responsibility of their parents, we hear. This is not anecdotal. A Pew Social Trends survey reports, “about two-thirds or more of the public believes that, compared with the younger generation, older Americans have better moral values, have a better work ethic and are more respectful of others.”

Of course, Baby boomers’ parents held their kids in equal contempt. Tom Brokaw’s book The Greatest Generation tells a story of baby boomers’ parents disappointed in their childrens’ lack of values and work ethic. “The morals have changed tremendously,” lamented one. Another’s “only regret is that the lessons of his generation” weren’t passed onto his kids. “The idea of personal responsibility is such a defining characteristic of the World War II generation,’ Brokaw wrote, “that when the rules changed later, these men and women were appalled.”

Decades before, the greatest generation was criticized by their elders, too. Woodrow Wilson, who grew up on horseback, said widespread use of the car promoted “the arrogance of wealth.” The younger generation was criticized for abandoning church, dressing provocatively, and leaving the rigors of farm labor for the ease of factory machines. Modern times stole their grit, as Fortune magazine wrote in 1936:

The present-day college generation is fatalistic. It will not stick its neck out. It keeps its pants buttoned, its chin up, and its mouth shut. If we take the mean average to be the truth, it is a cautious, subdued, unadventurous generation.

This goes on and on, a ritual dating back as far as anyone looks. It’s a time-honored tradition to be disappointed in the younger generation.


Here’s one explanation: Things get better over time. As you see younger generations bypassing problems you yourself dealt with, you become resentful. People can appear lazy when they don’t have to suffer as much as you did. This comes through as disappointment in younger generations who don’t seem to care about the same threats and worries their elders did. 

My emphasis added.  This does remind me of a quote by Thomas Macaulay: “Why when we see nothing but improvement behind us, do we see nothing but deterioration before us.”

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Favorite Warren Buffett Quotes

August 19, 2015

Some of my favorites:

It has been helpful to me to have tens of thousands (of students) turned out of business schools taught that it didn’t do any good to think.

(Warren Buffett, Grant, 1991)

To invest successfully, you need not understand beta, efficient markets, modern portfolio theory, option pricing or emerging markets.  You may, in fact, be better off knowing nothing of these.

(Warren Buffett, 1996 Letter to the Shareholders of Berkshire Hathaway)

Success in investing doesn’t correlate wtih IQ once you’re above the level of 125.  Once you have ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing.

(Warren Buffett, BusinessWeek 1999)


Photo credit: Biography.com

Source: Excess Returns, Vanhaverbeke

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Be Water My Friend

July 17, 2015

Trend Following wisdom from Bruce Lee:

HT: Michael Covel

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Quote of the Week

June 24, 2015

From Tadas Viskanta of Abnormal Returns:

One of the mistakes novice investors make is that they think they need to stay on top of all of the news that gets generated. They plow through the Wall Street Journal everyday, spend hours with a copy of Barron’s on the weekend and keep financial television all day. The problem is that there is little correlation between keeping up the financial media and actual performance.

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Champions Don’t Do Extraordinary Things

June 23, 2015

Great insight from Ben Carlson at A Wealth of Common Sense:

Trying to knock it out of the park at all times can lead to poor habits in your investment process. I just finished the book The Power of Habit by Charles Duhigg, who explains why this is the case. The reason habits, both good and bad, exist is because the brain is constantly looking for ways to save energy. Habits allow our mind to rest more often because our actions become almost second nature. What gets people in trouble is that we usually default to poor habits.

My favorite example in the book tells the tale of former NFL coach Tony Dungy. When he was an assistant coach, Dungy was constantly passed over for head coaching jobs. In part this had to do with his philosophy, which was too simple for many organizations:

Part of the problem was Dungy’s coaching philosophy. In his job interviews, he would patiently explain his belief that the key to winning was changing players’ habits. He wanted to get players to stop making so many decisions during a game, he said. He wanted them to react automatically, habitually. If he could instill the right habits, his team would win. Period.

“Champions don’t do extraordinary things,” Dungy would explain. “They do ordinary things, but they do them without thinking, too fast for the other team to react. They follow the habit they’ve learned.”

Dungy was finally hired by the Tampa Bay Buccaneers and it only took him a few years to turn around what was once the laughing stock of the league. The players bought into his philosophy, but it seemed to breakdown in big games:

“We would practice, and everything would come together and then we’d get to a big game and it was like the training disappeared,” Dungy told me. “Afterward, my players would say, ‘Well it was a critical play and I went back to what I knew,’ or ‘I felt like I had to step it up.’ What they were really saying was they trusted our system most of the time, but when everything was on the line, that belief broke down.”

Dungy was fired by the Bucs after a few consecutive losses in the conference championship game (they won it all with Jon Gruden the very next year), but eventually went on to win a Super Bowl with the Indianapolis Colts, who finally trusted his philosophy in the big games.

I had to smile at the part describing how Dungy was passed over for many head coaching jobs because his philosophy “was too simple for many organizations.”  Part of my every day is explaining the concept of momentum investing to potential clients (either individuals, financial advisors, or managed accounts departments) and it is not uncommon for me to hear a similar response, “that’s it, it’s 100% based on relative strength?”  Our investment process is essentially bringing Tony Dungy’s philosophy to portfolio management.  We have built our investment strategies around a proven factor–relative strength—and we have systematized our models so that we don’t have to overthink things.  Yet, many seem to feel more comfortable with something that sounds incredibly complex.

I’ve seen money run non-systematically and I’ve seen money run systematically.  In my view, here are the key benefits to systematizing the investment process:

  • In order to systematize a strategy, extensive research is required to understand what rules should be implemented.  Such testing makes it clear what works and what doesn’t over time.  Quiet confidence is a natural results of completing this research before the first dollar is invested.
  • Stress goes way down.  Simply systematizing an investment strategy does not remove periods of underperformance (unfortunately!).  However, it does make us think much more about process than short-term outcome.  The role of luck becomes greatly minimized and we are much better prepared to weather the inevitable rough patches without making hasty changes to our model.
  • Better results for our clients.  I firmly believe that our client’s lives will be better off because we employ a systematic process.  I believe they will have more money than they would otherwise have and I believe that they are more likely to become comfortable with our investment process and and stay with the strategy for longer periods of time.

So why don’t more people systematize their investment strategies?  Lack of computer programming ability, lack of access to data to do proper testing, lack of self-discipline to refrain from constantly tweaking a good model, and perhaps most of all, searching for the perfect rather than acceptance of the good.  However, if you can overcome those obstacles I believe you will put yourself in a position to be in very select company over time.

A relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Average is Over

June 5, 2015

Tyler Cowen’s book Average Is Over provided me some important insight into the ever-changing nature of our global economy—and particular insight into why some financial advisors are seeing their businesses thrive and others are struggling to stay afloat.  Growing income inequality seems to be a hard trend that economists and politicians have debated ad nauseam in recent decades.  Consider Cowen’s take on why this growing disparity is taking place and where we go from here:

This imbalance in technological growth will have some surprising implications.  For instance, workers more and more will come to be classified into two categories.  The key questions will be:  Are you good at working with intelligent machines or not?  Are your skills a complement to the skills of the computer, or is the computer doing better without you?  Worst of all, are you competing against the computer?

…If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery.  If your skills do not complement the computer, you may want to address that mismatch.  Ever more people are starting to fall on one side of the divide or the other.  That’s why average is over.

Surely, we in the financial services industry can attest that this is true.  Those advisors who have embraced technology have likely seen their businesses rapidly expand over the past decade.  They find themselves to be significantly more productive, able to manage much more money with seemingly less effort, and better able to stay connected to their growing number of clients in meaningful ways.  Those advisors who have not embraced technology, still trying to do business the way they did it in years past, are being left behind.

Subscribers of DWA research are keenly aware that there has been a wee bit (ok an enormous amount) of innovation over the years in our research database.  The core PnF principles from decades past are still there (supply and demand still determine price just like they always have).  However, this method that once involved hand charting stocks on a piece of graph paper has been computerized.  Now, our research is focused on rules-based asset allocation models, guided ETF models, and relative strength matrices that allow advisors to customize and systematize their own investment strategies at the click of a mouse.

We have also seen our assets under advisement take giant leaps forward…yet the firm still has about the same number of employees we had a decade ago.  How is this possible?  We are managing money with a reliance on systematized relative strength models which allow for tremendous efficiency and, we believe, better investment results that non-systematized approaches to investing.  Are there still money management firms that employ vast armies of analysts feeding data to investment committees who regularly meet for long meetings to debate investment strategy?  Yes, but those are among the people that Cowen is talking about when he asks, “are you competing against the computer?”

The future is very bright for those advisors who stay on the right side of technology.  For those that don’t, they are going to find it harder and harder to stay average.

A relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Making Fewer Decisions

May 14, 2015

RPSeawright makes a very compelling argument for reliance upon systematic models:

At the institutional level, making fewer decisions can mean building an investment process that, in effect, makes the decisions for us. If we carefully and collaboratively build, monitor and continue to evaluate a process that gets us to the decision we need without having to make (potentially a lot of) active preliminary decisions at every step we can improve outcomes, often by a great deal.

So if you want to make better decisions, start by working out how you can make fewer of them.

HT: Abnormal Returns

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Waiting for the Dust to Settle

April 24, 2015

The Irrelevant Investor kills it with this post:

The Worst Investment Strategy Ever


Do you make bad decisions when your portfolio goes down? What if there was a way to automate the decision so that your emotions wouldn’t get in the way. Good news, I found a way!

Here is the strategy, every time stocks drop five percent, you sell and wait for “clarity.” Why would you voluntarily ride out volatility, right? And here is the best part, you don’t get back in until things have stabilized. Repurchase stocks when they are one percent higher than when you sold, just to make sure that the dust has settled. Better be safe then sorry right? Here is what that strategy has looked like since the inception of the S&P 500.


Alright so you didn’t beat the buy and hold investors but you did compound your money at 2.8% with less than a ten percent annualized standard deviation. This is just slightly worse than what the average investor has historically earned, but after adjusting for risk this looks like a great alternative.

End sarcasm

If you want to suppress volatility it’s likely you’ll suppress your returns as well, it’s just that simple. Here is an idea- if you are uncomfortable with equities, pick a different asset class. Notably, five year treasury notes have compounded at 6.6% a year since 1957 with an annualized standard deviation of just five percent. Unless your looking for an equity strategy with bond-like returns, you might want to rethink jumping in and out every time the market takes a dip.

Comfortable doesn’t work in the financial markets if you want to earn equity-like returns over time.  My simple solution (for typical 55ish-65ish+  year old): Divide your portfolio into three buckets.  Income Bucket, Balanced Bucket, and Growth Bucket.  For your Growth Bucket, don’t try to manage the volatility (that is, in large part, what the other buckets are for).  Don’t do something similar to the strategy described above of selling when you feel uncomfortable and buying when “the dust settles.”  Rather, accept that your Growth Bucket is going to have some volatility to it, some drawdowns, some uncomfortable years.  By all means, spend the necessary time (or seek the appropriate financial advice) to put together a well-thought-out allocation for that Growth Bucket, but once that part is done, don’t look at the Growth Bucket in isolation.  Look at it in the context of your overall asset allocation.  Simple advice, but I believe it would lead to much better outcomes than are typically achieved in the financial markets by investors.

Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  Nothing contained herein should be construed as an offer to sell or the solicitation of an offer to buy any security.  This post does not attempt to examine all the facts and circumstances which may be relevant to any product or security mentioned herein.  We are not soliciting any action based on this post.  It is for the general information of readers of this blog.  This post does not constitute a personal recommendation or take into account the particular investment objectives, financial situations, or needs of individual clients.  Before acting on any analysis, advice or recommendation in this post, investors should consider whether the security or strategy in question is suitable for their particular circumstances and, if necessary, seek professional advice.  

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The Fallacy That Bull Markets are Easy

March 12, 2015

Ben Carlson is spot on:

It’s easy to look back on it now and say how much of a lay-up it was to invest at the depths of the market crash in early 2009, but there weren’t too many people saying things were all clear at the time. Investors were scared and constantly waiting for the next shoe to drop. Ever since the recovery started people have been doubting it’s legitimacy. Pundits have been scaring people away with predictions of double dip recessions, hyperinflation and the collapse of the U.S. dollar.

Don’t let anyone tell you investing in this bull market has been easy. It hasn’t, but really, it never is.

A good portion of the money that we manage here at Dorsey Wright is in one of our Tactical Asset Allocation strategies.  One of the key features of these strategies is the systematic way that these models rank a broad range of asset classes and invest in or overweight the strongest asset classes.  We let relative strength dictate whether we are going to be invested in “defensive” asset classes or “offensive” asset classes.  Without a systematic investment process, it’s just as easy to mess up (often by sitting on the sidelines) in a bull market as it is in a bear market.

HT: Abnormal Returns

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.

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Can a Quant Model Be Trusted?

March 9, 2015

Can a quant model really be trusted?

For all kinds of reasons, investment managers tend to find quantitative models to be interesting, but insufficient.  They want to have access to the outputs of quantitative rankings, but then to only use that output as one consideration among many when making the ultimate decision about what to buy or sell.  Not surprisingly, human emotion ends up getting the biggest say.

Maybe, there are some problems with that approach.  Consider the following study (Dresdner Kleinwort Macro Research):

The first study I want to discuss is a classic in the field.  It centers on the diagnosis of whether someone is neurotic or psychotic.  A patient suffering psychosis has lost touch with the external world; whereas someone suffering neurosis is in touch with the external world but suffering from internal emotional distress, which may be immobilizing.  The treatments for the two conditions are very different, so the diagnosis is not one to be taken lightly.

The standard test to distinguish the two is the Minnesota Multiphasic Personality Inventory (MMPI).  This consists of around 600 statements with which the patient must express either agreement or disagreement.  The statements range from “At times I think I am no good at all” to “I like mechanics magazines”.  Fairly obviously, those feeling depressed are much more likely to agree with the first statement than those in an upbeat mood.  More bizarrely, those suffering paranoia are more likely to enjoy mechanics magazines than the rest of us!

In 1968, Lewis Goldberg obtained access to more than 1000 patients’ MMPI test responses and final diagnoses as neurotic or psychotic.  he developed a simple statistical formula, based on 10 MMPI scores, to predict the final diagnosis.  His model was roughly 70% accurate when applied out of sample.

Goldberg then gave MMPI scores to experienced and inexperienced clinical psychologists and asked them to diagnose the patient.  As the chart below shows, the simple quant rule significantly outperformed even the best of the psychologists.

hit rate

Even when the results of the rules’ predictions were made available to the psychologists, they still underperformed the model.  This is a very important point: much as we all like to think we can add something to the quant model output, the truth is that very often quant models represent a ceiling in performance (from which we detract) rather than a floor (to which we can add).

The Dresdner Kleinworth research finds the same conclusions in the realms of baseball, wine, university admissions, and criminal recidivism.  Could it also apply to investment managers?

That is a pretty hard pill to swallow for investment managers, who as a group, don’t lack for self-confidence.  Yet, for those investment managers who truly understand the quantitative model, who built the quantitative model, who have stress-tested the model, and who are using an adaptive factor (like relative strength), strict reliance upon the model may very well represent “the ceiling in performance.”

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Quote of the Week

March 3, 2015

When you talk, you are only repeating what you already know; But when you listen, you may learn something new.  —Dalai Lama

Very applicable to the markets as well.  In fact, I would argue that listening (and reflecting market action) is all relative strength is really doing.

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What You Should Focus On

February 27, 2015

One of my favorite sketches from New York Times columnist, Carl Richards, is the following:


There are a lot of things that matter and there are a lot of things that we can control, but there is a much more limited number of things that both matter and are things that we can control—and that is where our focus should be.

This principle certainly applies to the construction of our investment strategies here at Dorsey Wright. I’ll take one index as an example. Every quarter, we select 100 stocks from a universe of approximately 1,000 U.S. mid and large cap stocks to make up the Technical Leaders Index (used for the PowerShares DWA Momentum ETF—PDP).

What We Can Control

  • We can control the quality of the research that we performed to identify the best PnF relative strength characteristics that will be used to select and weight the stocks for the index
  • We can maintain the integrity of the index construction process from one quarter to the next

What We Can’t Control

  • How those stocks in the TL Index perform after they have been selected
  • World events that might affect the financial markets from one quarter to the next

For Q1 2015, we once again followed the same process that has been followed for the last nearly 8 years in constructing the index for PDP and the following chart shows the current holdings of the index. The chart also shows how these stocks have performed so far this quarter.


(click to enlarge)

Most of the holdings have done quite well this quarter. Some haven’t. The ones that continue to meet our criteria will stay in the index in Q2 and those which have deteriorated will get cycled out and replaced with stronger names. As is our mantra here at Dorsey Wright, we will continue to focus on the right process and the results will take care of themselves over time.

From the perspective of an advisor who is using rules-based strategies, like PDP, I believe there is a great deal of confidence that comes from knowing that the strategy is based upon a methodology that has stood the test of time. Clients recognize and gravitate to advisors with that type of confidence.

Keeping a focus on the right things (i.e. the things we can control and the things that matter) make all the difference in this industry—an industry which is characterized by a flood of distractions at every turn.

Dorsey Wright is the index provider for PDP.  See www.powershares.com for more information.  The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  The performance above is based on pure price returns, not inclusive of dividends or all transaction costs.

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Unbending Flexibility

February 12, 2015

I am a man of fixed and unbending principles, the first of which is to be flexible at all times. —Everett Dirksen

That statement encapsulates the paradox of relative strength investing—flexibility and discipline.  In the financial markets, the word flexibility conjures up images of whimsical investment decisions with seemingly different rationale used for every trade.  How different that is to the way that we invest at Dorsey Wright.  We build models that typically have a great deal of flexibility, yet the rationale for every trades is the same—relative strength rank.  We sell a current holding because its relative strength rank has fallen sufficiently and we buy a replacement position because of its favorable relative strength rank.  In other words, we buy strong positions and we hold them for as long as they remain strong.

For example, one of the ETF models available through Dorsey Wright research is the DWA PowerShares Sector 4 Model (Power 4).  This model is designed to gain exposure to the strongest relative strength sectors in the US through the use of the nine Sector Momentum ETFs: PYZ, PEZ, PSL, PXI, PFI, PTH, PRN, PTF, and PUI.  When equities are not in favor, the portfolio can raise varying amounts of cash, up to 100%.  Dorsey Wright is also the index provider for the 9 PowerShares Sector Momentum ETFs.

Power 4 Portfolio Rules

  • Evaluated monthly
  • An inventory is established to represent each of the nine macro sectors.  The inventory consists of multiple representatives for each macro sector.
  • A matrix is created to compare members of the inventory to one another.
  • The sectors and cash are ranked from strongest to weakest based upon their tally rank within the matrix
  • The top 4 sectors are equally weighted
  • At the end of each month, if a sector falls out of the top 4, it is sold and replaced with the highest ranking sector not already in the portfolio.
  • If cash is the #4 slot, it receives a weighting of 25%.  For each slot it moves up, an additional 25% is allocated to cash.  If cash is the #1 ranked asset class, it will receive a 100% weighting.
  • Portfolio changes are transacted in a “replacement” method, and rebalanced only when a position drifts materially from it target allocation.

The start date for this model is 2/19/2014.  We also tested the strategy back to 2002.  Click here for a fact sheet.

Below you will see the historical allocations of the model:

power 4 allocations

Clearly, this is a flexible model.  Yet, the rationale for each of the trades was the same: relative strength rank.

Any virtue, taken to an extreme, can become a vice.  To be flexible is good, if it results in better investment results than a static allocation.  However, if flexibility is taken to an extreme, it can lead to overtrading and poor investment results.  Likewise, discipline is good, but if it results in an inability to adapt to different market environments, it can become a vice.

In the models we build at Dorsey Wright, we make great efforts to find a healthy way to have both flexibility and discipline.

The relative strength strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value.  Nothing contained herein should be construed as an offer to sell or the solicitation of an offer to buy any security.  This post does not attempt to examine all the facts and circumstances which may be relevant to any product or security mentioned herein.  We are not soliciting any action based on this post.  It is for the general information of readers of this blog.  This post does not constitute a personal recommendation or take into account the particular investment objectives, financial situations, or needs of individual clients.  Before acting on any analysis, advice or recommendation in this post, investors should consider whether the security or strategy in question is suitable for their particular circumstances and, if necessary, seek professional advice.  Dorsey Wright & Associates is the index provider for the suite of PowerShares DWA Momentum ETFs.  Some of the performance information is the result of back-tested performance.  Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the relative strength strategy during a specific period.  Back-tested performance results have certain limitations.  Back-testing performance differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with teh benefit of hindsight.  model performance data does not represent the impact of material economic and market factors might have on an invesment advisor’s decision making process if the advisor were actually managing client money.

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Point and Figure RS Signal Implementation

September 2, 2014

Over the course of the summer we published three different whitepapers looking at point and figure relative strength signals on a universe of domestic equities.  In the first two papers, we demonstrated the power of using PnF RS signals and columns to find high momentum stocks, and then we looked at the optimal box size for calculating relative strength.  If you were on vacation and happened to miss one of the first two papers they can be found here and here.

The third paper examines the performance profiles you can reasonably expect by following a process designed around point and figure relative strength.  You can download a pdf version of the paper here.  Most momentum research focuses on performance based on purchasing large baskets of stocks, which is impractical for non-institutional investors.  Once we know that the entire basket of securities outperforms over time the next logical question is, “What happens if I just invest in a subset of the most highly ranked momentum securities?”  To answer this question, we created portfolios of randomly drawn securities and ran the process through time.  Each portfolio held 50 stocks at all times, which we believe is a realistic number for retail investors.  Each month we sold any security in the portfolio that was not one of the top relative strength ranks.  For every security that was sold, we purchased a new security at random from the high relative strength group that wasn’t already held in the portfolio.  We ran this process 100 times to create 100 different portfolio return streams that were all different.  The one thing all 100 portfolios had in common was they were always 100% invested in 50 stocks from the high relative strength group.  But the exact 50 stocks could be totally different from portfolio to portfolio.

The graph below taken from the paper shows the range of outcomes from our trials.  From year to year you never know if your portfolio is going to outperform, but over the length of the entire test period all 100 trials outperformed the broad market benchmark.

 (Click To Enlarge)

We believe this speaks to the robust nature of the momentum factor, and also demonstrates the breadth of the returns available in the highest ranked names.  It wasn’t just a small handful of names that drove the returns.  As long as you stick to the process of selling the underperforming securities and replacing them with stocks having better momentum ranks there is a high probability of outperformance over time.  Over short time horizons the outperformance can appear random, and two people following the same process can wind up with very different returns.  But over long time horizons the process works very well.

Past performance is not indicative of future results.  Potential for profits is accompanied by possibility of loss.  A momentum strategy is NOT a guarantee.  There may be times where all investments and strategies are unfavorable and depreciate in value. 

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Quote of the Week

August 6, 2014

Via Michael Covel, an excerpt from James O’Shaughnessy’s book What Works on Wall Street:

Models beat the human forecasters because they reliably and consistently apply the same criteria time after time. In almost every instance, it is the total reliability of application of the model that accounts for its superior performance. Models never vary. They are always consistent. They are never moody, never fight with their spouse, are never hung over from a night on the town, and never get bored. They don’t favor vivid, interesting stories over reams of statistical data. They never take anything personally. They don’t have egos. They’re not out to prove anything. If they were people, they’d be the death of any party.

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A Wise Old Owl

July 29, 2014

The essence of relative strength:

A wise old owl lived in an oak.  The more he saw the less he spoke.  The less he spoke the more he heard.  Why can’t we all be like that wise old bird?

HT: Patrick O’Shaughnessy

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Reality Check for Forecasting

July 28, 2014

I’d say this is a pretty compelling argument for trend following.  As shown below, the average strategist forecast for the S&P 500 is routinely way off.


Source: WSJ

Rather than even attempt to forecast the unknowable, trend followers simply stay with the trend, until it is time to move on.  See here, here, and here.

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