How Not to be a Terrible Investor

February 27, 2014

Morgan Housel at Motley Fool has a wonderful article on how investors can learn from failure.  He sets the tone with a few different quotes and anecdotes that point out that a lot of being a success is just avoiding really dumb mistakes.

At a conference years ago, a young teen asked Charlie Munger how to succeed in life. “Don’t do cocaine, don’t race trains to the track, and avoid all AIDS situations,” Munger said. Which is to say: Success is less about making great decisions and more about avoiding really bad ones.

People focus on role models; it is more effective to find antimodels—people you don’t want to resemble when you grow up.    Nassim Taleb

I’ve added the emphasis, but Mr. Housel makes a good point.  Learning from failure is equally important as learning from success.  In fact, he argues it may be more important.

If it were up to me, I would replace every book called How to Invest Like Warren Buffett with a one called How to Not Invest Like Lehman Brothers, Long-Term Capital Management, and Jesse Livermore. There are so many lessons to learn from these failed investors about situations most of us will face, like how quickly debt can ruin you. I’m a fan of learning from Buffett, but the truth is most of us can’t devote as much time to investing as he can. The biggest risk you face as an investor isn’t that you’ll fail to be Warren Buffett; it’s that you’ll end up as Lehman Brothers.

But there’s no rule that says you have to learn by failing yourself. It is far better to learn vicariously from other people’s mistakes than suffer through them on your own.

That’s his thesis in a nutshell.  He offers three tidbits from his study of investing failures.  I’ve quoted him in full here because I think his context is important (and the writing is really good).

1. The overwhelming majority of financial problems are caused by debt, impatience, and insecurity. People want to fit in and impress other people, and they want it right now. So they borrow money to live a lifestyle they can’t afford. Then they hit the inevitable speed bump, and they find themselves over their heads and out of control. That simple story sums up most financial problems in the world. Stop trying to impress people who don’t care about you anyways, spend less than you earn, and invest the rest for the long run. You’ll beat 99% of people financially.

2. Complexity kills. You can make a lot of money in finance, so the industry attracted some really brilliant people. Those brilliant people naturally tried to make finance more like their native fields of physics, math, and engineering, so finance has grown exponentially more complex in the last two decades. For most, that’s been a disservice. I think the evidence is overwhelming that simple investments like index funds and common stocks will demolish complicated ones like derivatives and leveraged ETFs. There are two big stories in the news this morning: One is about how the University of California system is losing more than $100 million on a complicated interest rate swap trade. The other is about how Warren Buffett quintupled his money buying a farm in Nebraska. Simple investments usually win.

3. So does panic. In his book Deep Survival, Laurence Gonzalez chronicles how some people managed to survive plane crashes, getting stranded on boats, and being stuck in blizzards while their peers perished. The common denominator is simple: The survivors didn’t panic. It’s the same in investing. I’ve seen people make a lifetime of good financial decisions only to blow it all during a market panic like we saw in 2008. Any financial decision you make with an elevated heart rate is probably going to be one you’ll regret. Napoleon’s definition of a military genius was “the man who can do the average thing when all those around him are going crazy.” It’s the same in investing.

I think these are really good points.  It’s true that uncontrolled leverage accompanies most real blowups.  Having patience in the investing process is indeed necessary; we’ve written about that a lot here too.  The panic, impatience, and insecurity he references are really all behavioral issues—and it just points out that having your head on straight is incredibly important to investment success.  How successful you are in your profession or how much higher math you know is immaterial.  As Adam Smith (George Goodman) wrote, “If you don’t know who you are, the stock market is an expensive place to find out.” 

Mr. Housel’s point on complexity could be a book in itself.  Successful investing just entails owning productive assets—the equity and debt of successful enterprises—acquired at a reasonable price.  Whether you own the equity directly, like Warren Buffett and his farm, or in security form is immaterial.  An enterprise can be a company—or even a country—but it’s got to be successful.

Complexity doesn’t help with this evaluation.  In fact, complexity often obscures the whole point of the exercise.

This is actually one place where I think relative strength can be very helpful in the investment process.  Relative strength is incredibly simple and relative strength is a pretty good signaling mechanism for what is successful.  Importantly, it’s also adaptive: when something is no longer successful, relative strength can signal that too.  Sears was once the king of retailing.  Upstart princes like K-Mart in its day, and Wal-Mart and Costco later, put an end to its dominance.  Once, homes were lit with candles and heated with fuel oil.  Now, electricity is much more common—but tomorrow it may be something different.  No asset is forever, not even Warren Buffett’s farmland.  When the soil is depleted, that farm will become a lead anchor too.  Systematic application of relative strength, whether it’s being used within an asset class or across asset classes, can be a very useful tool to assess long-term success of an enterprise.

Most investing problems boil down to behavioral issues.  Impatience and panic are a couple of the most costly.  Avoiding complexity is a different dimension that Mr. Housel brings up, and one that I think should be included in the discussion.  There are plenty of millionaires that have been created through owning businesses, securities, or real estate.  I can’t think of many interest rate swap millionaires (unless you count the people selling them).  Staying calm and keeping things simple might be the way to go.  And if the positive prescription doesn’t do it for you, the best way to be a good investor may be to avoid being a terrible investor!

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It’s All At The Upper End

February 3, 2014

Almost all of the performance from a relative strength or momentum model comes from the upper end of the ranks.  We run different models all the time to test different theories or to see how existing decision rules work on different groups of securities.  Sometimes we are surprised by the results, sometimes we aren’t.  But the more we run these tests, the more some clear patterns emerge.

One of these patterns we see constantly is all of the outperformance in a strategy coming from the very top of the ranks.  People are often surprised at how quickly any performance advantage disappears as you move down the ranking scale.  That is one of the things that makes implementing a relative strength strategy so difficult.  You have to be absolutely relentless in pushing the portfolio toward the strength because there is often zero outperformance in aggregate from the stuff that isn’t at the top of the ranks.  If you are the type of person that would rather “wait for a bounce” or “wait until I’m back to breakeven,” then you might as well just equal-weight the universe and call it a day.

Below is a chart from a sector rotation model I was looking at earlier this week.  This model uses the S&P 500 GICS sub-sectors and the ranks were done using a point & figure matrix (ie, running each sub-sector against every other sub-sector) and the portfolio was rebalanced monthly.  You can see the top quintile (ranks 80-100) performs quite well.  After that, good luck.  The “Univ” line is a monthly equal-weighted portfolio of all the GICS sub-sectors.  The next quintile (ranks 60-80) barely beats the universe return and probably adds no value after you are done with trading costs, taxes, etc…  Keep in mind that these sectors are still well within the top half of the ranks and they still add minimal value.  The other three quintiles are underperformers.  They are all clustered together well below the universe return.

 (Click on image to enlarge)

The overall performance numbers aren’t as good, but you get the exact same pattern of results if you use a 12-Month Trailing Return to rank the sub-sectors instead of a point & figure matrix:

 (click on image to enlarge)

Same deal if you use a 6-Month Trailing Return:

(click on image to enlarge)

This is a constant theme we see.  The very best sectors, stocks, markets, and so on drive almost all of the outperformance.  If you miss a few of the best ones it is very difficult to outperform.  If you are unwilling to constantly cut the losers and buy the winners because of some emotional hangup, it is extremely difficult to outperform.  The basket of securities in a momentum strategy that delivers the outperformance is often smaller than you think, so it is crucial to keep the portfolio focused on the top-ranked securities.

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Investor Behavior

January 6, 2014

Marshall Jaffe, in a recent article for ThinkAdvisor, made an outstanding observation:

In a world where almost nothing can be predicted with any accuracy, investor behavior is one of the rare exceptions. You can take it to the bank that investors will continue to be driven by impatience, social conformity, conventional wisdom, fear, greed and a confusion of volatility with risk. By standing apart and being driven solely by the facts, the value investor can take advantage of the opportunities caused by those behaviors—and be in the optimal position to create and preserve wealth.

His article was focused on value investing, but I think it is equally applicable to relative strength investing.  In fact, maybe even more so, as value investors often differ about what they consider a good value, while relative strength is just a mathematical calculation with little room for interpretation.

Mr. Jaffe’s main point—that investors are driven by all sorts of irrational and incorrect cognitive forces—is quite valid.  Dozens of studies point it out and there is a shocking lack of studies (i.e., none!) that show the average investor to be a patient, independent thinker devoid of fear and greed.

What’s the best way to take advantage of this observation about investor behavior?  I think salvation may lie in using a systematic investment process.  If you start with an investment methodology likely to outperform over time, like relative strength or value, and construct a rules-based systematic process to follow for entry and exit, you’ve got a decent chance to avoid some of the cognitive errors that will assail everyone else.

Of course, you will construct your rules during a period of calm and contemplation—but that’s never when rules are difficult to apply!  The real test is sticking to your rules during the periods of fear and greed that occur routinely in financial markets.  Devising the rules may be relatively simple, but following them in trying circumstances never is!  As with most things, the harder it is to do, the bigger the potential payoff usually is.

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More on Systematic Process

October 18, 2013

We use a systematic process for investment because we think that’s the best way to go.  Our systematic process also happens to be adaptive because we think adaptation to the current market environment is also an important consideration.  (If you don’t adapt you die.)  Our decision to use a systematic process is grounded in evidence that, over time, systematic processes tend to win out over inconsistent human decision making.  (See here, for example.)

The latest instance of this was an interesting article on Quartz about the coming wave of full-service coffee machines that may have the potential to replace baristas.  Consider, for example, what this particular quotation says about the power of a systematic process:

In 2012, Julian Baggini, a British philosophy writer and coffee aficionado, wondered why dozens of Europe’s Michelin-starred restaurants were serving guests coffee that came out of vacuum-sealed plastic capsules manufactured by Nespresso. So he conducted a taste test on a small group of experts. A barista using the best, freshly-roasted beans went head to head with a Nespresso capsule coffee brewing machine. It’s the tale of John Henry all over again, only now it was a question of skill and grace rather than brute strength.

As the chefs at countless restaurants could have predicted, the Nespresso beat the barista.

Suffice it to say that most manufacturing nowadays is done by machine because it is usually faster, less expensive, and more accurate than a human.  Perhaps you will miss terribly your nose-ringed, pink-haired, tatooed barista, but then again, maybe not so much.

Systematic investing has its problems—sometimes the adaptation seems too slow or too fast.  Sometimes your process is just out of favor.  But like a manufacturing process, a systematic investment process holds the promise of consistency and potential improvement as technology and new techniques are incorporated over time.  While it may seem less romantic than the lone stock picker, systematic investment could well be the wave of the future.

HT to Abnormal Returns

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Rats, Humans, and Probability

October 9, 2013

Investors—or people generally—find it difficult to think in terms of probability.  A quote from a recent ThinkAdvisor article on probability is instructive:

In multiple studies (most prominently those by Edwards and Estes, as reported by Philip Tetlock in his book Expert Political Judgment), subjects were asked to predict which side of a “T-maze” held food for a rat. The maze was rigged such that the food was randomly placed (no pattern), but 60% of the time on one side and 40% on the other. The rat quickly “gets it” and waits at the “60% side” every time and is thus correct 60% of the time. Human observers keep looking for patterns and choose sides in rough proportion to recent results. As a consequence, the humans were right only 52% of the time—they (we!) are much dumber than rats. We routinely misinterpret probabilistic strategies that accept the inevitability of randomness and error.

Even rats get probability better than people!  It is for this reason that a systematic investing process can be so valuable.  Away from the pressure and hubbub of the markets, strategies can be researched and probabilities investigated and calculated.  Decisions can be made on the basis of probability because a systematic process incorporates the notion that there is a certain amount of randomness that cannot be overcome with clever decision-making.

Ironically, because humans have sophisticated pattern recognition skills built in, we see patterns in probability where there are none.  A systematic investment process can reduce or eliminate the “overinterpretation” inherent in our own cleverness.  When we can base our decisions only on the actual probabilities embedded in the data, those decisions will be much better over a large number of trials.

Good investing is never easy, but a systematic investing process can eliminate at least one barrier to good performance.

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Dumb Talk About Smart Beta?

October 7, 2013

John Rekenthaler at Morningstar, who usually has some pretty smart stuff to say, took on the topic of smart beta in a recent article.  Specifically, he examined a variety of smart beta factors with an eye to determining which ones were real and might persist.  He also thought some factors might be fool’s gold.

Here’s what he had to say about value:

The value premium has long been known and continues to persist.

And here’s what he had to say about relative strength (momentum):

I have trouble seeing how momentum can succeed now that its existence is well documented.

The italics are mine.  I didn’t take logic in college, but it seems disingenuous to argue that one factor will continue to work after it is well-known, while becoming well-known will cause the other factor to fail!  (If you are biased in favor of value, just say so, but don’t use the same argument to reach two opposite conclusions.)

There are a variety of explanations about why momentum works, but just because academics can’t agree on which one is correct doesn’t mean it won’t continue to work.  It is certainly possible that any anomaly could be arbitraged away, but Robert Levy’s relative strength work has been known since the 1960s and our 2005 paper in Technical Analysis of Stocks & Commodities showed it continued to work just fine just the way he published it.  Academics under the spell of efficient markets trashed his work at the time too, but 40 years of subsequent returns shows the professors got it wrong.

However, I do have a background in psychology and I can hazard a guess as to why both the value and momentum factors will continue to persistthey are both uncomfortable to implement.  It is very uncomfortable to buy deep value.  There is a terrific fear that you are buying a value trap and that the impairment that created the value will continue or get worse.  It also goes against human nature to buy momentum stocks after they have already outperformed significantly.  There is a great fear that the stock will top and collapse right after you add it to your portfolio.  Investors and clients are quite resistant to buying stocks after they have already doubled, for example, because there is a possibility of looking really dumb.

Here’s the reason I think both factors are psychological in origin: it is absurdly easy to screen for either value or momentum.  Any idiot can implement either strategy with any free screener on the web.  Pick your value metric or your momentum lookback period and away you go.  In fact, this is pretty much exactly what James O’Shaughnessy did in What Works on Wall Street.  Both factors worked well—and continue to work despite plenty of publicity.  So the barrier is not that there is some secret formula, it’s just that investors are unwilling to implement either strategy in a systematic way–because of the psychological discomfort.

If I were to make an argument—the behavioral finance version—about which smart beta factor could potentially be arbitraged away over time, I would have to guess low volatility.  If you ask clients whether they would prefer to buy stocks that a) had already dropped 50%, b) had already gone up 50%, or c) had low volatility, I think most of them would go with “c!”  (Although I think it’s also possible that aversion to leverage will keep this factor going.)

Value and momentum also happen to work very well together.  Value is a mean reversion factor, while momentum is a trend continuation factor.  As AQR has shown, the excess returns of these two factors (unsurprisingly, once you understand how they are philosophical opposites) are uncorrelated.  Combining them may have the potential to smooth out an equity return stream a little bit.  Regardless, two good return factors are better than one!

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Investment Process versus Investment Performance

September 6, 2013

Marshall Jaffe wrote an excellent article on investment process versus investment performance in the most recent edition of ThinkAdvisor.  I think it is notable for a couple of reasons.  First, it’s pithy and well-written.  But more importantly, he’s very blunt about the problems of focusing only on investment performance for both clients and the industry.  And make no mistake—that’s how the investment industry works in real life, even though it is a demonstrably poor way to do things.  Consider this excerpt:

We see the disclaimer way too often. “Past performance is no guarantee of future results.” It is massively over-used—plastered on countless investment reports, statements and research. It’s not simply meaningless; it’s as if it’s not even there. And that creates a huge problem, because the message itself is really true: Past performance has no predictive value.

Since we are looking for something that does have predictive value—all the research, experience and hard facts say: Look elsewhere.

This is not a controversial finding. There are no fringe groups of investors or scholars penning op-ed pieces in the Wall Street Journal shooting holes in the logic of this reality. Each year there is more data, and each year that data reconfirms that past performance is completely unreliable as an investment tool. Given all that, you would think it would be next to impossible to find any serious investors still using past performance as a guideline. Indeed, that would be a logical conclusion.

But logical conclusions are often wrong when it comes to understanding human behavior. Not only does past performance remain an important issue in the minds of investors, for the vast majority it is the primary issue. In a study I referred to in my August column, 80% of the hiring decisions of large and sophisticated institutional pension plans were the direct result of outstanding past performance, especially recent performance.

The truth hurts!  The bulk of the article discusses why investors focus on performance to their detriment and gives lots of examples of top performers that focus only on process.  There is a reason that top performers focus on process—because results are the byproduct of the process, not an end in themselves.

The reason Nick Saban, our best athletes, leading scientists, creative educators, and successful investors focus on process is because it anchors them in reality and helps them make sensible choices—especially in challenging times. Without that anchor any investor observing the investment world today would be intimidated by its complexity, uncomfortable with its volatility and (after the meltdown in 2008) visibly fearful of its fragility. Of course we all want good returns—but those who use a healthy process realize that performance is not a goal; performance is a result.

Near the end of the article, I think Mr. Jaffe strikes right to the core of the investment problem for both individual investors and institutions.  He frames the right question.  Without the right question, you’re never going to get the right answer!

In an obsessive but fruitless drive for performance too many fund managers compromise the single most important weapon in their arsenal: their investment process.

Now we can see the flaw in the argument that an investor’s basic choice is active or passive. An investor indeed has two choices: whether to be goal oriented or process oriented. In reframing the investment challenge that way, the answer is self-evident and the only decision is whether to favor a mechanical process or a human one.

Reframing the question as “What is your investment process?” sidelines everything else.  (I added the bold.)  In truth, process is what matters most.  Every shred of research points out the primacy of investment process, but it is still hard to get investors to look away from performance, even temporarily.

We focus on relative strength as a return factor—and we use a systematic process to extract whatever return is available—but it really doesn’t matter what return factor you use.  Value investors, growth investors, or firms trying to harvest more exotic return factors must still have the same focus on investment process to be successful.

If you are an advisor, you should be able to clearly explain your investment process to a client.  If you are an investor, you should be asking your advisor to explain their process to you.  If there’s no consistent process, you might want to read Mr. Jaffe’s article again.

HT to Abnormal Returns

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Adventures in Fictive Learning

September 3, 2013

What the heck is fictive learning?  Well, I’m glad you asked.  Fictive learning refers to our ability to imagine “what if” situations.  We learn not only from our actual actions, but from our perceptions of what would have happened if we had done something differently.  It turns out that fictive learning has a lot to do with investor behavior too.  Here are a few excerpts about relevant experiments discussed in an article in Wired magazine.

To better understand the source of our compulsive speculation, Read Montague, a neuroscientist now at Virginia Tech, has begun investigating the formation of bubbles from the perspective of the brain. He argues that the urge to speculate is rooted in our mental software. In particular, bubbles seem to depend on a unique human talent called “fictive learning,” which is the ability to  learn from hypothetical scenarios and counterfactual questions. In other words, people don’t just learn from mistakes they’ve actually made, they’re able to learn from mistakes they might have made, if only they’d done something different.

Investors, after all, are constantly engaging in fictive learning, as they compare their actual returns against the returns that might have been, if only they’d sold their shares before the crash or bought Google stock when the company first went public. And so, in 2007, Montague began simulating stock bubbles in a brain scanner, as he attempted to decipher the neuroscience of irrational speculation. His experiment went like this: Each subject was given $100 and some basic information about the “current” state of the stock market. After choosing how much money to invest, the players watched nervously as their investments either rose or fell in value. The game continued for 20 rounds, and the subjects got to keep their earnings. One interesting twist was that instead of using random simulations of the stock market, Montague relied on distillations of data from famous historical markets. Montague had people “play” the Dow of 1929, the Nasdaq of 1998 and the S&P 500 of 1987, so the neural responses of investors reflected real-life bubbles and crashes.

Montague, et. al. immediately discovered a strong neural signal that drove many of the investment decisions. The signal was fictive learning. Take, for example, this situation. A player has decided to wager 10 percent of her total portfolio in the market, which is a rather small bet. Then, she watches as the market rises dramatically in value. At this point, the investor experiences a surge of regret, which is a side-effect of fictive learning. (We are thinking about how much richer we would be if only we’d invested more in the market.) This negative feeling is preceded by a swell of activity in the ventral caudate, a small area in the center of the cortex.  Instead of enjoying our earnings, we are fixated on the profits we missed, which leads us to do something different the next time around.

When markets were booming, as in the Nasdaq bubble of the late 1990s, people perpetually increased their investments. In fact, many of Montague’s subjects eventually put all of their money into the rising market. They had become convinced that the bubble wasn’t a bubble. This boom would be different.

And then, just like that, the bubble burst. The Dow sinks, the Nasdaq collapses, the Nikkei implodes. At this point investors race to dump any assets that are declining in value, as their brain realizes that it made some very expensive mistakes. Our investing decisions are still being driven by regret, but now that feeling is telling us to sell. That’s when we get a financial panic.

Montague has also begun exploring the power of social comparison, or what he calls the “country club  effect,” on the formation of financial bubbles. “This is what happens when you’re sitting around with your friends at the country club, and they’re all talking about how much money they’re making in the market,” Montague told me. “That casual conversation is going to change the way you think about investing.” In a series of ongoing experiments, Montague has studied what happens when people compete against each other in an investment game. While the subjects are making decisions about the stock market, Montague monitors their brain activity in two different fMRI machines. The first thing Montague discovered is that making more money than someone else is extremely pleasurable.  When subjects “win” the investment game, Montague observes a large increase in activity in the striatum, a brain area typically associated with the processing of pleasurable rewards. (Montague refers to this as “cocaine brain,” as the striatum is also associated with the euphoric high of illicit drugs.) Unfortunately, this same urge to outperform others can also lead people to take reckless risks.

More recently, a team of Italian neuroscientists led by Nicola Canessa and Matteo Motterlini have shown that regret is also contagious, so that “observing the regretful outcomes of another’s choices reactivates the regret network.” (In other words, we internalize the errors of others. Or, as Motterlini wrote in an e-mail, “We simply live their emotions like these were our own.”) Furthermore, this empathy impacts our own decisions: The “risk-aptitude” of investors is significantly shaped by how well the risky decisions of a stranger turned out. If you bet the farm on some tech IPO and did well, then I might, too.

If you are an investment advisor, all of this is sounding pretty familiar.  We’ve all seen clients make decisions based on social comparison, regret, or trying to avoid regret.  Sometimes they are simply paralyzed, trapped between wanting to do as well as their brother-in-law and wanting to avoid the regret of losing money if their investment doesn’t work out.

The broader point is that a lot of what drives trends in the market is rooted in human behavior, not valuations and fundamentals.  Human nature is unlikely to change, especially a feature like fictive learning which is actually incredibly helpful in many other contexts.  As a result, markets will continue to trend and reverse, to form bubbles and to have those bubbles implode periodically.

While social science may be helpful in understanding why the market behaves as it does, we still have to figure out a way to navigate it.  As long as markets trend, relative strength trend following should work.  (That’s the method we follow.)  As long as bubbles form and implode, other methods like buying deep value should help mitigate the risk of permanent loss.  Most important, the discipline to execute a systematic investment plan and not get sucked into all of the cognitive biases will be necessary to prosper with whatever investment method you choose.

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Is Active Investing Hopeless?

August 19, 2013

Every time I read an article about how active investing is hopeless, I shake my head.  Most of the problem is investor behavior, not active investing.  The data on this has been around for a while, but is ignored by indexing fans.  Consider for example, this article in Wealth Management that discusses a 2011 study conducted by Morningstar and the Investment Company Institute.  What they found doesn’t exactly square out with most of what you read.  Here are some excerpts:

But studies by Morningstar and the Investment Company Institute (ICI) suggest that fund shareholders may not be so dumb after all. According to the latest data, investors gravitate to low-cost funds with strong track records. “People make reasonably intelligent choices when they pick active funds,” says John Rekenthaler, Morningstar’s vice president of research.

The academic approach produces a distorted picture, says Rekenthaler. “It doesn’t matter what percentage of funds trail the index,” says Rekenthaler. “What matters most is how the big funds do. That’s where most of the money is.”

In order to get a realistic picture of fund results, Rekenthaler calculated asset-weighted returns—the average return of each invested dollar. Under his system, large funds carry more weight than small ones. He also calculated average returns, which give equal weight to each fund. Altogether Morningstar looked at how 16 stock-fund categories performed during the ten years ending in 2010. In each category, the asset-weighted return was higher than the result that was achieved when each fund carried the same weight.

Consider the small-growth category. On an equal-weighted basis, active funds returned 2.89 percent annually and trailed the benchmark, which returned 3.78 percent. But the asset-weighted figure for small-growth funds exceeded the benchmark by 0.20 percentage points. Categories where active funds won by wide margins included world stock, small blend, and health. Active funds trailed in large blend and mid growth. The asset-weighted result topped the benchmark in half the categories. In most of the eight categories where the active funds lagged, they trailed by small margins. “There is still an argument for indexing, but the argument is not as strong when you look at this from an asset-weighted basis,” says Rekenthaler.

The numbers indicate that when they are choosing from among the many funds on the market, investors tend to pick the right ones.

Apparently investors aren’t so dumb when it comes to deciding which funds to buy.  Most of the actively invested money in the mutual fund industry is in pretty good hands.  Academic studies, which weight all funds equally regardless of assets, don’t give a very clear picture of what investors are actually doing.

Where, then, is the big problem with active investing?  There isn’t one—the culprit is investor behavior.  As the article points out:

But investors display remarkably bad timing for their purchases and sales. Studies by research firm Dalbar have shown that over the past two decades, fund investors have typically bought at market peaks and sold at troughs.

Active investing is alive and well.  (I added the bold.)  In fact, the recent trend toward factor investing, which is just a very systematic method for making active bets, reinforces the value of the approach.

The Morningstar/ICI research just underscores that much of the value of an advisor may lie in helping the client control their emotional impulse to sell when they are fearful and to buy when they feel confident.  I think this is often overlooked.  If your client has a decent active fund, you can probably help them more by combatting their destructive timing than you can by switching them to an index fund.  After all, owning an index fund does not make the investor immune to emotions after a 20% drop in the stock market!

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From the Archives: If You Miss the 10 Best Days

June 7, 2013

We’ve all seen numerous studies that purport to show how passive investing is the way to go because you don’t want to be out of the market for the 10 best days.  No one ever mentions that the “best days” most often occur during the declines!

It turns out that the majority of the best days and the worst days occur near one another, during the declines.  Why?  Because the market is more volatile during declines.  It is true that the market goes down 2-3x as fast as it goes up.  (World Beta has a nice post on this topic of volatility clustering, which is where this handy-dandy table comes from.)

 If You Miss the 10 Best Days

from World Beta

You can see how volatility increases and the number of days with daily moves greater than 2.5% really spikes when the market is in a downward trend.  It would seem to be a very straightforward proposition to improve your returns simply by avoiding the market when it is in a downtrend.

However, not every strategy can be improved by going to cash.  Think about the math: if your investing methodology makes enough extra money on the good days to offset the bad days, or if it can make money during a significant number of the declines, you might be better off just gritting your teeth during the declines and banking the higher returns.  Although the table above suggests it should help, a simple strategy of exiting the market (i.e., going to cash) when it is below its 200-day moving average may not always live up to its theoretical billing.

 If You Miss the 10 Best Days

 If You Miss the 10 Best Days

click to enlarge

Consider the graphs above.  (The first graph uses linear scaling; the second uses logarithmic scaling for the exact same data.)  This test uses Ken French’s database to get a long time horizon and shows the returns of two portfolios constructed with market cap above the NYSE median and in the top 1/3 for relative strength.  In other words, the two portfolios are composed of mid- and large-cap stocks with good relative strength.  The only difference between the two portfolios is that one (red line) goes to cash when it is below its 200-day moving average.  One portfolio (blue line) stays fully invested.  The fully invested portfolio turns $100 into $49,577, while the cash-raising portfolio yields only $26,550.

If you would rather forego the extra money in return for less volatility, go right ahead and make that choice.  But first stack up 93 boxes of  Diamond matches so that you can burn 23,027 $1 bills, one at a time, to represent the difference–and then make your decision.

 If You Miss the 10 Best Days

The drawdowns are less with the 200-day moving average, but it’s not like they are tame–equities will be an inherently volatile asset class as long as human emotions are involved.  There are still a couple of drawdowns that are greater than 20%.  If an investor is willing to sit through that, they might as well go for the gusto.

As surprising as it may seem, the annualized return over a long period of time is significantly higher if you just stay in the market and bite the bullet during train wrecks–and even two severe bear markets in the last decade have not allowed the 200-day moving average timer to catch up.

At the bottom of every bear market, of course, it certainly feels like it would have been a good idea (in hindsight) to have used the 200-day moving average to get out.  In the long run, though, going to cash with a high-performing, high relative strength strategy might be counterproductive.  When we looked at 10-year rolling returns, the fully invested high relative strength model has maintained an edge in returns for the last 30 years running.

 If You Miss the 10 Best Days

click to enlarge

Surprising, isn’t it?  Counterintuitive results like this are one of the reasons that we find testing so critical.  It’s  easy to fall in line with the accepted wisdom, but when it is actually put to the test, the accepted wisdom is often wrong.  (We often find that even when shown the test data, many people refuse, on principle, to believe it!  It is not in their worldview to accept that one of their cherished beliefs could be false.)  Every managed portfolio in our Systematic RS lineup has been subjected to heavy testing, both for returns and–and more importantly–for robustness.  We have a high degree of confidence that these portfolios will do well in the long run.

—-this article originally appeared 3/5/2010.  We find that many investors continue to refuse, on principle, to believe the data!  If you have a robust investment method, the idea that you can improve your returns by getting out of the market during downturns appears to be false.  (Although it could certainly look true for small specific samples.  And, to be clear, 100% invested in a volatile strategy is not the appropriate allocation for most investors.)  Volatility can generally be reduced somewhat, but returns suffer.  One of our most controversial posts ever—but the data is tough to dispute.

In more recent data, the effect can be seen in this comparison of an S&P 500 ETF and an ETN that switches between the S&P 500 and Treasury bills based on a 200-day moving average system.  The volatility has been muted a little bit, but so have the returns.

(click on image to enlarge)

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Cognitive Biases

June 5, 2013

Motley Fool had an excellent article by Morgan Housel on a couple of the most common cognitive biases that cause problems for investors, cognitive dissonance and confirmation bias.  The information is not new, but what makes this article so fun is Housel’s writing style and good analogies.  A couple of excerpts should suffice to illuminate the problem with cognitive biases.

Study successful investors, and you’ll notice a common denominator: They are masters of psychology. They can’t control the market, but they have complete control over the gray matter between their ears.

And lucky them. Most of us, on the other hand, are mental catastrophes. As investor Barry Ritholtz once put it:

You’re a monkey. It all comes down to that. You are a slightly clever, pants-wearing primate. If you forget that you’re nothing more than a monkey who has been fashioned by eons on the plains, being chased by tigers, you shouldn’t invest. You have to be aware of how your own psychology affects what you do.

Take one of the most powerful theories in behavior psychology: cognitive dissonance. It’s the term psychologists use for the uncomfortable feeling you get when having two conflicting thoughts at the same time. “Smoking is bad for me. I’m going to go smoke.” That’s cognitive dissonance.

We hate cognitive dissonance, and jump through hoops to reduce it. The easiest way to reduce it is to engage in mental gymnastics that justifies behavior we know is wrong. “I had a stressful day and I deserve a cigarette.” Now you can smoke guilt-free. Problem solved.

Classic.  And this:

Cognitive dissonance is especially toxic in the emotional cesspool that is managing money. Raise your hand if this is you:

  • You criticize Wall Street for being a casino while checking your portfolio twice a day.
  • You sold your stocks in 2009 because the Fed was printing money. When stocks doubled in value soon after, you blamed it on the Fed printing money.
  • You put $1,000 on a hyped penny stock your brother convinced you is the next Facebook. After losing everything, you tell yourself you were just investing for the entertainment.
  • You call the government irresponsible for running a deficit while simultaneously saddling yourself with an unaffordable mortgage.
  • You buy a stock only because you think it’s cheap. When you realize you were wrong, you decide to hold it because you like the company’s customer service.

Almost all of us do something similar with our money. We have to believe our decisions make sense. So when faced with a situation that doesn’t make sense, we fool ourselves into believing something else.

And this about confirmation bias:

Worse, another bias — confirmation bias — causes us to bond with people whose self-delusions look like our own. Those who missed the rally of the last four years are more likely to listen to analysts who forecast another crash. Investors who feel burned by the Fed visit websites that share the same view. Bears listen to fellow bears; bulls listen to fellow bulls.

Before long, you’ve got a trifecta of failure: You make a bad decision, rationalize it by fighting cognitive dissonance, and reinforce it with confirmation bias. No wonder the average investor does so poorly.

It’s worth reading the whole article, but the gist of it is that we are all susceptible to these cognitive biases.  It’s possible to mitigate the problem with some kind of systematic investment process, but you still have to be careful that you’re not fooling yourself.  Investing well is not easy and mastering one’s own psyche may be the most difficult part of all.

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Relative Strength: A Solid Investment Method

January 28, 2013

We are fond of relative strength.  It’s a solid investment method that have proven itself over a long period of time.  Sure, it has its challenges and there are certainly periods of time during which it underperforms, but all-in-all it works and it’s been good to us.  It’s always nice, though, when I run across another credible source that sings its praises.  Consider the following excerpt from an article on the Optimal Momentum blog:

Momentum, on the other hand, has always made sense. It is based on the phrase “cut your losses; let your profits run on,” coined by the famed economist David Ricardo in the 1700s. Ricardo became wealthy following his own advice.  [Editor’s note: We wrote about this in David Ricardo’s Golden Rules.] Many others, such as Livermore, Gartley, Wycoff, Darvas, and Driehaus, have done likewise over the following years. Behavioral finance has given solid reasons why momentum works. The case for momentum is now so strong that two of the fathers of modern finance, Fama and French, call momentum “the premier market anomaly” that is “above suspicion.”

Momentum, on the other hand, is pretty simple. Every approach, including momentum, must determine what assets to use and when to rebalance a portfolio. The single parameter unique to momentum is the look back period for determining an asset’s relative strength. In a 1937, using data from 1920 through 1935, Cowles and Jones found stocks that performed best over the past twelve months continued to perform best afterwards. In 1967, Bob Levy came to the same conclusion using a six-month look back window applied to stocks from 1960 through 1965. In 1993, using data from 1962 through 1989 and rigorous testing methods, Jegadeesh and Titman (J&T) reaffirmed the validity of momentum. They found the same six and twelve months look back periods to be best. Momentum is not only simple, but it has been remarkably consistent over the past seventy-five years.

Momentum, on the other hand, is one of the most robust approaches in terms of its applicability and reliability. Following the 1993 seminal study by J&T, there have been nearly 400 published momentum papers, making it one of the most heavily researched finance topics over the past twenty years. Extensive academic research has shown that price momentum works in virtually all markets and time periods, from Victorian ages up to the present.

Of course, momentum is just the academic term for relative strength.  For more on the history of relative strength—and how it became known as momentum in academia—see CSI Pasadena: Relative Strength Identity Theft.  The bigger point is that relative strength has a lot of backing from both academics and practitioners.  There are more complicated investment methods, but not many that are better than relative strength.

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From the Archives: Why We Like Price

January 25, 2013

Relative strength calculations rely on a single input: price.  We like price because it is a known quantity, not an assumption.  In this deconstruction of the Price-to-Earnings Growth (PEG) ratio, the author, Tom Brakke, discusses all of the uncertainties when calculating even a simple ratio like PEG.  And amidst all of the uncertainties he mentions is this:

In looking at that calculation, only one of the three variables has any precision:  We can observe the market price (P) at virtually any time and be assured that we have an accurate number.  The E is a different matter entirely.  Which earnings?  Forward, trailing, smoothed, operating, adjusted, owner?  Why?  How deep into accounting and the theory of finance do you want to go?

For most investors, not very far.  We like our heuristics clean and easy, not hairy.  So, in combining the first two variables we get the P/E ratio, the “multiple” upon which most valuation work rests, despite the questionable assumptions that may be baked in at any time.  The addition of the third element, growth (G), gives us not the epiphany we seek, but even more confusion.

The emphasis is mine.  This isn’t a knock on fundamental analysis.  It can be valuable, but there is an inherent squishiness to it.  The only precision is found in price.  And price is dynamic: it adapts in real time as expectations of the asset change.  (Fundamental data is often available only on a quarterly schedule.)  As a result, systematic models built using relative strength adapt quite nicely as conditions change.

—-this article originally appeared 2/10/2010.  We still like using prices as an input, especially now that there are so many cross-currents.  Every pundit has a different take on what will happen down the road, but prices in a free market will eventually sort it all out.

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From the Archives: Irrational Loss Aversion

January 22, 2013

It’s well known in behavioral finance that investors experience a loss 2-3x more intensely than a gain of the same magnitude.  This loss aversion leads investors to avoid even rational bets, according to a Reuters story on a recent study by a Cal Tech scientist.

Laboratory and field evidence suggests that people often avoid risks with losses even when they might earn a substantially larger gain, a behavioral preference termed ‘loss aversion’,” they wrote.

For instance, people will avoid gambles in which they are equally likely to either lose $10 or win $15, even though the expected value of the gamble is positive ($2.50).

The study indicates that people show fear at even the prospect of a loss.  Markets are designed to generate fear, not to mention all of the bearish commentators on CNBC.  Fear leads to poor decisions, like selling near the bottom of a correction.  Unless you are planning to electrically lesion your amygdala, the fear is going to be there–so what’s the best way to deal with it?

The course we have chosen is to make our investment models systematic.  That means the decisions are rules-based, not subject to whatever fear the portfolio managers may be experiencing at any given time.  Once in a blue moon, excessive caution pays off, but studies suggest that more errors are made being excessively cautious than overly aggressive.  A rules-based method treats risk in a even-handed, mathematical way.  In other words, take risks that historically are likely to pay off, and keep taking them regardless of your emotional state.  Given enough time, the math is likely to swing things in your favor.

—-this article originally appeared 2/10/2010.  In the two years since this was written, investors have continued to pay a high price for their fear as the market has continued to advance.  There are always scary things around the corner, but a rules-based process can often help you navigate through them.  Investors seem to have a hard time learning that scary things don’t necessarily cause markets to perform poorly.  In fact, the opposite is often true.

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Winners and Losers

December 12, 2012

If you’ve ever wondered why clients remember their winners so well—and are so quick to sell them–while forgetting the losers and how badly they have done, some academics have done you a favor.  You can read this article for the full explanation.  Or you can look at this handy graphic from CXO Advisory that explains how clients use different reference points for winners and losers.  In short, the winners are compared with their highest-ever price, while losers are compared with their break-even purchase price.

Source: CXO Advisory

It explains a lot, doesn’t it?  It explains why clients make bizarre self-estimates of their investment performance.  And it explains why clients are perpetually disappointed with their advisors—because they are comparing their winners with the highest price achieved.  Any downtick makes it a loser in their eyes.  The losers are ignored, in hopes they will get back to even.

The antidote to this cognitive bias, of course, is to use a systematic investment process that ruthlessly evaluates every position against a common standard.  If you are a value investor, presumably you are estimating future expected returns as your holding criterion.  For relative strength investors like ourselves, we’re constantly evaluating the relative strength ranking of each security in the investment universe.  Strong securities are retained, and securities that weaken are swapped out for stronger ones.  Only a systematic process is going to keep you from looking at reference points differently for winners and losers.

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When Will the Fed Raise Interest Rates?

October 12, 2012

The topic of interest rates is of concern to investors for a couple of reasons.  Savers are interested in finding out when interest rates might rise and they might earn more than 0% on their accumulated capital, and bond investors would like some kind of early warning if there is trouble ahead.  I’ve seen lots of opinions on this, and they’ve mostly all been wrong.  My personal answer to the question about when interest rates would rise would have been something like “2009,” which explains why 1) I am not a prominent interest rate forecaster and why 2) everyone should use a systematic investment process (as we do)!

To attempt to answer the interest rate question, Eddy Elfenbein of Crossing Wall Street stepped in in a way I particularly admire—with actual data, not just opinions.  He showed two competing interest rate models developed by Greg Mankiw at Harvard and Paul Krugman at Princeton.  Although the coefficients are slightly different, it turns out that their models are pretty similar.  I’ve shown the two graphs below.

Mankiw Interest Rate Model


Krugman Interest Rate Model

Source: Crossing Wall Street  (click on images to enlarge)

Up until the recent financial crisis, the forecast fit the data rather well for both models.  That is to be expected, since the model is derived from the data and each modeler is searching for the best fit equation.  Both models show that, given the past behavior of interest rates in relation to the variables they use (core inflation and unemployment), current interest rates should be negative!  The Fed seems to be coping with this situation by holding rates at zero and using quantitative easing to simulate negative rates.

What will make these models suggest that interest rates should start to move higher?  If core inflation increases and the unemployment rate begins to decline, both of these models would call for higher rates.  For Krugman’s model, for example, core inflation would have to rise to 2.5% (from the current 1.8% level; I used PCE excluding food and energy), while unemployment would need to decline to 7.5% from 7.8%.  (Or it could be a different combination that was mathematically equivalent.)  For Mankiw’s model to call for higher rates, only a slight increase in inflation or a drop in unemployment would be needed.

If the economy continues to plug along with slow growth, low inflation, and relatively high unemployment, both of these models would continue to suggest that negative rates are needed to revive the economy.

So much for theory.  In reality, many considerations go into setting the Fed Funds rate.  Watching the behavior of inflation and unemployment probably enters into it, but I’m guessing the Fed is examining other data as well.  From the outside, perhaps the best thing we can do is monitor the relative strength of bonds versus other asset classes to get a handle on the expectation for interest rates.

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Fun With Backtesting

September 4, 2012

This just in from Institutional Investor: many backtests fail in real life.  They write:

Makers of indexes often fill in the blanks with historic data on the components to produce hypothetical index performance. But a recent Vanguard study found that a large percentage of these hypothetical, back-filled indexes that had outperformed the U.S. stock market didn’t keep up after they went live as the index returns subsequently fell. What may be happening, says senior Vanguard ETF strategist Joel Dickson, is that indexes are being developed by “rearview mirror investing,” that is, through selection bias of what worked well in the past. The result can mean a nasty surprise for investors.


Pretty much anyone can do data mining with the computing power available on a desktop computer.  And index providers will continue to do data mining as long as investors ram money into products with lousy backtests.

Back-filled index funds attract on average twice the cash flow in the initial launch phase than funds with new indexes that don’t have such data, indicating that the availability of a track record makes the fund more attractive — even if it probably won’t last.

Good backtesting can be very useful and can give investors a good idea of what to expect in the future.  But how can an investor tell if the backtest is any good or not?

One thing to examine is how robust the index methodology is.  For example, when we built our Systematic Relative Strength products, we subjected them to Monte Carlo testing for robustness.  That made it apparent that the systematic investment method itself was sound, even though the range of outcomes on a quarterly or annual basis can be significant.

With the proliferation of indexes for ETFs, it’s becoming important to be able to evaluate how robust the backtesting was.  Probably partly because of a robust backtesting process, our Technical Leaders Index has outperformed the market since inception.  I’m sure many other indexes are thoughtfully constructed—but I’m just as sure that there are some that are not.

Do your homework before you put client money at risk.

See for more information.  Past performance is no guarantee of future returns.  A list of all holding for the previous 12 months is available upon request.

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From the Archives: Two Approaches to Motivating Clients

June 28, 2012

Ken Haman, a managing director at the Advisor Institute at AllianceBernstein responds to the following question posed by an advisor and published in Investment News:

Q: I’ve had some frustrating conversations with clients recently—trying to get them back in the market. Very few are taking my advice, even though they seem to know that staying on the sidelines is a mistake. What’s going on, and how can I get them “unstuck”?

A: Problems like this have to do with how people make decisions. Behavioral finance uses the term “inappropriate extrapolation”–and insights about it can help you understand your clients and respond to them more effectively.

To make any decision, human beings create a mental picture of the future. That’s what “expectations” are–the ability to take information from the past and present, and project it into the future. Unlike most animals, human beings can project far into the future; as a result, we are able to “plan ahead.” Unfortunately, we usually don’t create these future images terribly well. Instead of making a thoughtful assessment of what’s likely to happen in the future, we typically picture the future as just a continuation of the recent past.

Essentially, you want to learn how to install a positive picture of the future that the client feels is likely to happen in reality. Start by explaining the mechanisms of the market and illustrating visually how those mechanisms work. Many investors have only the vaguest understanding of the cause-effect dynamics in the markets. Instead of making thoughtful, well-informed decisions, they react to their perception of patterns and trends. Market “mechanisms” are those cause-effect relationships that equip financial professionals to invest rationally instead of speculating randomly.

By looking at how market mechanisms operated in both the recent and more distant past, you teach your clients how to think more strategically about the markets. This allows them to build a more vivid mental picture of market behaviors in the future. Make sure you explain market mechanisms visually as well as verbally: use charts and graphs that show market behaviors over time. Whenever possible, connect your investment recommendations to a clear explanation of the mechanism that is involved.

Second, provide an adequate level of detail about the mechanisms you explain. There’s a commonly held myth that clients aren’t interested in hearing about the markets. So, many financial advisors gloss over important information and rush to their proposal without creating a case the client understands. But clients are interested in understanding the mechanisms that drive their investment results–as long as your explanation is clearly illustrated and easy to understand.

Finally, you have to deliver your message with personal conviction–that you fully believe the future will look the way you anticipate. Your clients need to borrow your conviction and clarity about the future. That’s how they’ll build their sense of confidence in the decisions you’re asking them to make. Take a stand on what you believe about the future, and add the courage of your own convictions to the clarity of your explanation.

There is also an alternative approach of just being frank with the client and telling them that you don’t know exactly what the future holds, nor does anyone else.  However, you adhere to a systematic relative strength process that gives you great flexibility to allocate to a wide range of asset classes depending on how the future unfolds.  At times, the approach can be allocated very conservatively and at times it can be allocated quite aggressively.  My experience has been that clients appreciate the honesty and are willing to embrace a trend-following approach that deals very effectively with not being able to see into the future.

—-this article originally appeared 1/12/2010.  More than two years later, many clients are still on the sidelines.    Many of them definitely do engage in inappropriate extrapolation!  An advisor’s first duty is to be honest, but you’ve got to do it in a way that is motivating and not paralyzing.

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PDP in the News

June 26, 2012

Bloomberg has an article today entitled “ETFs Passive No More.”  It’s an article about the rise of intelligent indexation.  Here’s their thesis:

Exchange-traded funds are posing a new threat to the $7.8 trillion market for active mutual funds by challenging the notion ETFs are only good for tracking benchmarks.

Here’s their blurb about PDP:

The PowerShares DWA fund, which invests in U.S.-listed companies, uses an index that selects them based on “relative strength,” a proprietary screening methodology developed by Richmond, Virginia-based Dorsey, Wright & Associates Inc. The fund has advanced at an annual rate of 2 percent since its inception in March 2007, compared with the 1.2 percent gain for the Standard & Poor’s 500 Index over the same period, and the 3.8 percent increase in the Russell 3000 Growth Index.

Their offerings may further erode the market share of active mutual funds, sold by traditional money managers such as Fidelity Investments, Capital Group Cos. and Franklin Resources Inc. The companies tout the ability of their managers to beat benchmarks mostly through individual security selection.

“Historically, active managers held a unique appeal to prospective investors,” said Steven Bloom, who helped develop the first ETF in the 1980s and is now an assistant professor of economics at the U.S. Military Academy at West Point, New York.“Now, ETFs are infringing on that territory by holding out the prospect of alpha.”

The article points out that by using a rules-based investment process within an ETF, you can shoot for alpha, while getting the tax benefits of the ETF structure.  Rules-based ETFs are going to continue to blur the line with active mutual funds over time.  It’s also going to be interesting to see how many of the rules-based processes are robust and how many have been optimized.  Curve-fitted performance will tend to degrade over time, while a truly adaptive model should be more consistent.

We think the trend toward intelligent indexes will continue and we’re excited to be one of the pioneers.

See for more information about PDP.  Past performance is no guarantee of future returns.  A list of all holdings for the trailing 12 months is available upon request.

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From the Archives: Is Buy-and-Hold Dead?

June 20, 2012

The  Journal of Indexes has the entire current issue devoted to articles on this topic, along with the best magazine cover ever.  (Since it is, after all, the Journal of Indexes, you can probably guess how they came out on the active versus passive debate!)

One article by Craig Israelson, a finance professor at Brigham Young University, stood out.  He discussed what he called “actively passive” portfolios, where a number of passive indexes are managed in an active way.  (Both of the mutual funds that we sub-advise and our Global Macro separate account are essentially done this way, as we are using ETFs as the investment vehicles.)  With a mix of seven asset classes, he looks at a variety of scenarios for being actively passive: perfectly good timing, perfectly poor timing, average timing, random timing, momentum, mean reversion, buying laggards, and annual rebalancing with various portfolio blends.  I’ve clipped one of the tables from the paper below so that you can see the various outcomes:

 Is Buy and Hold Dead?

Click to enlarge

Although there is only a slight mention of it in the article, the momentum portfolio (you would know it as relative strength) swamps everything but perfect market timing, with a terminal value more than 3X the next best strategy.  Obviously, when it is well-executed, a relative strength strategy can add a lot of return.  (The rebalancing also seemed to help a little bit over time and reduced the volatility.)

Maybe for Joe Retail Investor, who can’t control his emotions and/or his impulsive trading, asset allocation and rebalancing is the way to go, but if you have any kind of reasonable systematic process and you are after returns, the data show pretty clearly that relative strength should be the preferred strategy.

—-this article originally appeared 1/8/2010.  Relative strength rocks.

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Value and Bias

June 19, 2012

Aswath Damodaran wrote the book on valuation, literally.  He is a valuation guru, or as close to it as you are going to find.  He readily admits that valuation is a biased process.  From Business Insider:

Something that Aswath Damodaran reiterated frequently during his lecture is that valuation is not some sort of magical, objective science that will let you know what others don’t. It provides an anchor for your thinking and investment behavior.

Here are the three biggest myths of valuation from Professor Damodaran’s presentation:

  • A valuation is an objective search for true value
  • A good valuation provides a precise estimate of value
  • The more quantitative a model, the better the valuation

Here’s the anecdote Professor Damodaran told to illustrate the first point:

“I have valued Microsoft  every year since 1986, the year of their IPO. 26 years in a row. Every year through 2011 when I valued Microsoft I found it to be overvalued. You name the  price, I found it overvalued. $2, $4, $8, “don’t buy, don’t buy, don’t buy.”  Strange right? One of the great success stories of US equity markets over the last 50 years, and I wouldn’t have touched it one step of the way. Now I can  give you access to every one of those models… You can dig through these models looking for clues as to why I found Microsoft to be overvalued, but you’d be  looking in the wrong place. If you really want to know why I  found Microsoft to be overvalued all of these years, all you need to do is walk up my office and look around. What you’re going to see is a bunch of computers with fruits on the back.”

Although there are multiple ways in which relative strength can be calculated, all investors using the same method are going to get the same result.  There is no subjectivity in terms of assumptions and inputs.  That kind of objectivity can really help eliminate emotions and biases from the investment process.  To take Damodaran’s example, if Microsoft qualifies as one of the great success stories of the past 50 years, it would have had high relative strength somewhere along the line, by definition.  End of story.

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From the Archives: The Future of Decision-Making

June 13, 2012

Man versus machine, art versus science, intuition versus logic—all of these are ways of expressing what we often think of as contradictory approaches to problem solving.  Should we be guided more by data and precedent, or is it more important to allow for the human element?  Is it critical to be able to step aside and say, with the benefit of our judgment, “maybe this time really is different?”

The Harvard Business Review recently took on this topic and a few of their points were quite provocative.

A huge body of research has clarified much about how intuition works, and how it doesn’t. Here’s some of what we’ve learned:

  • It takes a long time to build good intuition. Chess players, for example, need 10 years of dedicated study and competition to assemble a sufficient mental repertoire of board patterns.
  • Intuition only works well in specific environments, ones that provide a person with good cues and rapid feedback . Cues are accurate indications about what’s going to happen next. They exist in poker and firefighting, but not in, say, stock markets. Despite what chartists think, it’s impossible to build good intuition about future market moves because no publicly available information provides good cues about later stock movements. [Needless to say, I don’t agree with his assessment of stock charts!] Feedback from the environment is information about what worked and what didn’t. It exists in neonatal ICUs because babies stay there for a while. It’s hard, though, to build medical intuition about conditions that change after the patient has left the care environment, since there’s no feedback loop.
  • We apply intuition inconsistently. Even experts are inconsistent. One study determined what criteria clinical psychologists used to diagnose their patients, and then created simple models based on these criteria. Then, the researchers presented the doctors with new patients to diagnose and also diagnosed those new patients with their models. The models did a better job diagnosing the new cases than did the humans whose knowledge was used to build them. The best explanation for this is that people applied what they knew inconsistently — their intuition varied. Models, though, don’t have intuition.
  • We can’t know or tell where our ideas come from. There’s no way for even an experienced person to know if a spontaneous idea is the result of legitimate expert intuition or of a pernicious bias. In other words, we have lousy intuition about our intuition.
  • It’s easy to make bad judgments quickly. We have many biases that lead us astray when making assessments. Here’s just one example. If I ask a group of people “Is the average price of German cars more or less than $100,000?” and then ask them to estimate the average price of German cars, they’ll “anchor” around BMWs and other high-end makes when estimating. If I ask a parallel group the same two questions but say “more or less than $30,000″ instead, they’ll anchor around VWs and give a much lower estimate. How much lower? About $35,000 on average, or half the difference in the two anchor prices. How information is presented affects what we think.

We’ve written before about how long it takes to become world-class.  Most studies show that it takes about ten years to become an expert if you apply yourself diligently.  Obviously, the “intuition” of an expert is much better than the intuition of a neophyte.  If you think about that for a minute, it’s pretty clear that intuition is really just judgment in disguise.  The expert is better than the novice simply because they have a bigger knowledge base and more experience.

Really, the art versus science debate is over and the machines have won it going away.  Nowhere is this more apparent than in chess.  Chess is an incredibly complex mental activity.  Humans study with top trainers for a decade to achieve excellence.  There is no question that training and practice can cause a player to improve hugely, but it is still no contest.  As processing power and programming experience has become more widespread, a $50 CD-ROM off-the-shelf piece of software can defeat the best players in the world in a match without much problem.  Most of the world’s top grandmasters now use chess software to train with and to check their ideas.  (In fact, so do average players since the software is so cheap and ubiquitous.)

How did we get to this state of affairs?  Well, the software now incorporates the experience and judgment of many top players.  Their combined knowledge is much more than any one person can absorb in a lifetime.  In addition, the processing speed of a standard desktop computer is now so fast that no human can keep it with it.  It doesn’t get tired, upset, nervous, or bored.  Basically, you have the best of both worlds—lifetimes of human talent and experience applied with relentless discipline.

A 2000 paper on clinical versus mechanical prediction by  Grove, Zald, Lebow, Snitz, & Nelson  had the following abstract:

>The process of making judgments and decisions requires a method for combining data. To compare the accuracy of clinical and mechanical (formal, statistical) data-combination techniques, we performed a meta-analysis on studies of human health and behavior. On average, mechanical-prediction techniques were about 10% more accurate than clinical predictions. Depending on the specific analysis, mechanical prediction substantially outperformed clinical prediction in 33%–47% of studies examined. Although clinical predictions were often as accurate as mechanical predictions, in only a few studies (6%–16%) were they substantially more accurate. Superiority for mechanical-prediction techniques was consistent, regardless of the judgment task, type of judges, judges’ amounts of experience, or the types of data being combined. Clinical predictions performed relatively less well when predictors included clinical interview data. These data indicate that mechanical predictions of human behaviors are equal or superior to clinical prediction methods for a wide range of circumstances.

That’s a 33-47% win rate for the scientists and a 6-16% win rate for the artists, and that was ten years ago.  That’s not really very surprising.  Science is what has allowed us to develop large-scale agriculture, industrialize, and build a modern society.  Science and technology are not without their problems, but if the artists have stayed in charge we might still be living in caves, although no doubt we would have some pretty awesome cave paintings.

This is the thought process behind our Systematic Relative Strength accounts.  We were able to codify our own best judgment, include lifetimes of other experience from investors we interviewed or relative strength studies that we examined, and have it all run in a disciplined fashion.  We chose relative strength because it was the best-performing factor and also because, since it is relative, it is adaptive.  There is always cooperation between man and machine in our process, but moving more toward data-driven decisions is indeed the future of decision making.

—-this article originally appeared 1/15/2010.  Our thought process hasn’t changed—we still believe that a systematic, adaptive investment process is the way to go.

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One-Trick Pony

June 12, 2012

Crosshairs Trader has an excerpt from Jack Schwager’s Hedge Fund Market Wizards.  The interview with Steve Clark makes an interesting point.

Schwager:  You have seen a lot of traders.  What are the characteristics of traders who succeed?

Clark: They all work hard. Nearly all the successful traders I know are one-trick ponies.  They do one thing, and they do it very well.

I find it interesting that most successful traders specialize in one method or return factor.  The takeaway is that it is almost impossible to be an expert at everything.

Although I wouldn’t rule out expanding our expertise at some point, relative strength has always been our focus.  It’s an incredible bonus that relative strength is so adaptive that it can be used in many different systematic processes.

(By the way, you’re missing a treat if you haven’t read any of Jack Schwager’s books.  I haven’t read the most recent one yet, but his first Market Wizards book is one of my all-time favorites.)

One-Trick Pony

Source: dgdesignnetwork  (click on image to enlarge)

HT to Abnormal Returns

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