Investing Lies We Grew Up With

March 3, 2010

This is the title of a nice article by Brett Arends at Marketwatch.  He points out that a lot of our assumptions, especially regarding risk, are open to question. 

Risk is an interesting topic for a lot of reasons, but principally (I think) because people seem to be obsessed with safety.  People gravitate like crazy to anything they perceive to be “safe.”  (Arnold Kling has an interesting meditation on safe assets here.) 

Risk, though, is like matter–it can neither be created nor destroyed.  It just exists.  When you buy a safe investment, like a U.S. Treasury bill, you are not eliminating your risk; you are just switching out of the risk of losing your money into the risk of losing purchasing power.  The risk hasn’t gone away; you have just substituted one risk for another.  Good investing is just making sure you’re getting a reasonable return for the risk you are taking. 

In general, investors–and people generally–are way too risk averse.  They often get snookered in deals that are supposed to be “low risk” mainly because their risk aversion leads them to lunge at anything pretending to be safe.  Psychologists, however, have documented that individuals make more errors from being too conservative than too aggressive.  Investors tend to make that same mistake.  For example, nothing is more revered than a steady-Eddie mutual fund.  Investors scour magazines and databases to find a fund that (paradoxically) is safe and has a big return.  (News flash: if such a fund existed, you wouldn’t have to look very hard.) 

No one goes looking for high-volatility funds on purpose.  Yet, according to an article, Risk Rewards: Roller-Coaster Funds Are Worth the Ride at TheStreet.com:

Funds that post big returns in good years but also lose scads of money in down years still tend to do better over time than funds that post slow, steady returns without ever losing much.

The tendency for volatile investments to best those with steadier returns is even more pronounced over time. When we compared volatile funds with less volatile funds over a decade, those that tended to see big performance swings emerged the clear winners. They made roughly twice as much money over a decade.

That’s a game changer.  Now, clearly, risk aversion at the cost of long-term returns may be appropriate for some investors.  But if blind risk aversion is killing your long-term returns, you might want to re-think.  After all, eating Alpo is not very pleasant and Maalox is pretty cheap.  Maybe instead of worrying exclusively about volatility, we should give some consideration to returns as well.

Leave a Comment » | From the MM, Investor Behavior, Markets, Thought Process | Permalink
Posted by Mike Moody

Was It Really a Lost Decade?

February 17, 2010

Index Universe has a provocative article by Rob Arnott and John West of Research Affiliates.  Their contention is that 2000-2009 was not really a lost decade.  Perhaps if your only asset was U.S. equities it would seem that way, but they point out that other, more exotic assets actually had respectable returns.

The table below shows total returns for some of the asset classes they examined.

click to enlarge

What are the commonalities of the best performing assets? 1) Lots of them are highly volatile like emerging markets equities and debt, 2) lots of them are international and thus were a play on the weaker dollar, 3) lots of them were alternative assets like commodities, TIPs, and REITs.

In other words, they were all asset classes that would tend to be marginalized in a traditional strategic asset allocation, where the typical pie would primarily consist of domestic stocks and bonds, with only small allocations to very volatile, international, or alternative assets.

In an interesting way, I think this makes a nice case for tactical asset allocation.  While it is true that most investors–just from a risk and volatility perspective–would be unwilling to have a large allocation to emerging markets for an entire decade, they might find that periodic significant exposure to emerging markets during strong trends would be quite acceptable.  And even assets near the bottom of the return table like U.S. Treasury bills would have been very welcome in a portfolio during parts of 2008, for example.  You can cover the waterfront and just own an equal-weighted piece of everything, but I don’t know if that is the most effective way to do things.

What’s really needed is a systematic method for determining which asset classes to own, and when.  Our Systematic Relative Strength process does this pretty effectively, even for asset classes that might be difficult or impossible to grade from a valuation perspective.  (How do you determine whether the Euro is cheaper than energy stocks, or whether emerging market debt is cheaper than silver or agricultural commodities?)  Once a systematic process is in place, the investor can be slightly more comfortable with perhaps a higher exposure to high volatility or alternative assets, knowing that in a tactical approach the exposures would be adjusted if trends change.

Leave a Comment » | From the MM, Markets, Tactical Asset Alloc, Thought Process | Permalink
Posted by Mike Moody

How To Create Good Portfolio Performance

February 3, 2010

Clay Allen of Market Dynamics has a fantastic, fantastic essay on the process needed to generate good performance in a portfolio.  The process is incredibly simple, but most often ignored.

Surprisingly, the key to good performance is the ability to identify those stocks that are detracting from the performance of the portfolio. Most portfolio managers spend most of their time and effort trying to find the next big winner in the stock market but good portfolio performance depends more on finding and eliminating the bad stocks from the portfolio.

Why is the process ignored?  Because most investors do not want to take a loss!  This aspect of investor behavior is so ingrained that academics writing about behavioral finance have given the tendency a name, the disposition effect.  (If you google for it, you will find dozens of articles written about it.)  No doubt the disposition effect costs amateur investors untold millions in aggregate profits every year.

Mr. Allen’s essay is an eloquent restatement of a fundamental principle: cut your losses and let your winners run.   The casting-out process used in our systematic relative strength process does exactly that.  Each asset has a stop based on its relative strength rank.  If it falters in relative performance, it is kicked out of the portfolio and replaced.   Mathematically, this is the correct way to run a portfolio.  The recent White Paper on our relative strength testing process shows that even randomly selected high relative strength stocks will outperform over time, as long as the weak stocks are knocked out of the portfolio on a consistent basis.  Managing the portfolio properly is as important to the ultimate result as the research to find the strong stocks.

Leave a Comment » | From the MM, Investor Behavior, Relative Strength Research, Thought Process | Permalink
Posted by Mike Moody

The Math Behind Manager Selection

January 28, 2010

Hiring and firing money managers is a tricky business.  Institutions do it poorly (see background post here ), and retail investors do it horribly (see article on DALBAR  ).  Why is it so difficult?

This white paper on manager selection from Intech/Janus goes into the mathematics of manager selection.  Very quickly it becomes clear why it is so hard to do well.

Many investors believe that a ten-year performance record for a group of managers is sufficiently long to make it easy to spot the good managers. In fact, it is unlikely that the good managers will stand out.  Posit a good manager whose true average relative return is 200 basis points (bps) annually and true tracking error (standard deviation of relative return) is 800 bps annually. This manager’s information ratio is 0.25. To put this in perspective, an information ratio of 0.25 typically puts a manager near or into the top quartile of managers in popular manager universes.

Posit twenty bad managers with true average relative returns of 0 bps annually, true tracking error of 1000 bps annually, hence an information ratio of 0.00.

There is a dramatic difference between the good manager and the bad managers.

The probability that the good manager beats all twenty bad managers over a ten-year period is only about 9.6%.  This implies that chasing performance leaves the investor with the good manager only about 9.6% of the time and with a bad manager about 90.4% of the time.

In other words, 90% of the time the manager with the top 10-year track record in the group will be a bad manager!  Maybe a longer track record would help?

A practical approach is to ask how long a historical performance record is necessary to be 75% sure that the good manager will beat all the bad managers, i.e., have the highest historical relative return. Assuming the same good manager as before and twenty of the same bad managers as before, a 157 year historical performance record is required to achieve a 75% probability that the good manager will beat all the bad managers.

It turns out that it would help, but since none of the manager databases have 150-year track records, in practice it is useless.  The required disclaimer that past performance is no guarantee of future results turns out to be true.

There is still an important practical problem to be solved here.  Assuming that bad managers outnumber good ones and assuming that we don’t have 150 years to wait around for better odds, how can we increase our probability of identifying one of the good money managers?

The researchers show mathematically how combining an examination of the investment process with historical returns makes the decision much simpler.  If the investor can make a reasonable assumption about a manager’s investment process leading to outperformance, the math is straightforward and can be done using Bayes’ Theorem to combine probabilities. 

…the answer changes based on the investor’s assessment of the a priori credibility of the manager’s investment process.

It turns out that the big swing factor in the answer is the credibility of the underlying investment process.  What are the odds that an investment process using Fibonacci retracements and phases of the moon will generate outperformance over time?  What are the odds that relative strength or deep value will generate outperformance over time?

The research paper concludes with the following words of wisdom:

A careful examination of almost any investor’s investment manager hiring and firing process is likely to reveal that there is a substantial component of performance chasing. Sometimes it is obvious, e.g., when there is a policy of firing a manager if he has negative performance after three years. Other times it is subtle, e.g., when the initial phase of the manager search process strongly weights attractive historical performance. No matter the form that performance chasing takes, it tends to produce future relative returns that are disappointing compared to expectations.

Historical performance alone is not an effective basis for identifying a good manager among a group of bad managers. This does not mean that historical performance is useless. Rather, it means that it must be combined efficiently with other information. The correct use of historical performance relegates it to a secondary role. The primary focus in manager choice should be an analysis of the investment process.  [emphasis added]

This research paper is eye-opening in several respects. 

1) It shows pretty clearly that historical performance alone–despite what our intuition tells us–is not sufficient to select managers.  This probably accounts for a great deal of the poor manager selection, the subsequent disappointment, and rapid manager turnover that goes on. 

2) It is very clear from the math that only credible investment processes are likely to generate long-term outperformance.  Fortunately, lots of substantive academic and practitioner research has been done on factor analysis leading to outperformance.  The only two broadly robust factors discovered so far have been relative strength and value, both in various formulations–and, obviously, they have to be implemented in a disciplined and systematic fashion.  If your investment process is based on something else, there’s a decent chance you’re going to be disappointed. 

3) Significant time is required for the best managers to stand out from the much larger pack of mediocre managers.

This is a demanding process for consultants and clients.  They have to willfully reduce their focus on even 10-year track records, limit their selection to rigorous managers using proven factors for outperformance, and then exercise a great deal of patience to allow enough time for the cream to rise to the top.  The rewards for doing so, however, might be quite large–especially since almost all of your competition will ignore the correct process and and simply chase performance.

3 Comments | From the MM, Investor Behavior, Thought Process | Permalink
Posted by Mike Moody

The Future of Decision-Making

January 15, 2010

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.

    1 Comment | From the MM, Thought Process | Permalink
    Posted by Mike Moody

    Q4 2009 Review

    January 15, 2010

    Our Q4 2009 Review covers the following topics:

    • Putting the 2009 laggard rally in context of past laggard rallies.
    • Using data from James O’Shaughnessy, we look at how relative strength strategies have rebounded from past laggard rallies over the last 84 years.
    • Possible economic implications of a steep yield curve.

    Click here to read the full review.

    Leave a Comment » | From the MM | Permalink
    Posted by Andy Hyer

    Is Buy-and-Hold Dead?

    January 8, 2010

    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:

    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.

    5 Comments | From the MM, Markets, Relative Strength Research, Tactical Asset Alloc, Thought Process | Permalink
    Posted by Mike Moody

    The $ Value of Patience

    January 6, 2010

    The annals of investor behavior make for some pretty scary reading.  Yet this story from the Wall Street Journal may take the cake.  It is an article about the top-performing mutual fund of the decade and it shows with remarkable clarity how badly investors butcher their long-term returns.  The article hits the premise right up front:

    Meet the decade’s best-performing U.S. diversified stock mutual fund: Ken Heebner’s $3.7 billion CGM Focus Fund, which rose more than 18% annually and outpaced its closest rival by more than three percentage points.

    Too bad investors weren’t around to enjoy much of those gains. The typical CGM Focus shareholder lost 11% annually in the 10 years ending Nov. 30, according to investment research firm Morningstar Inc.

    It’s hard to know whether to laugh or cry.  In a brutal decade, Mr. Heebner did a remarkable job, gaining 18% per year for his investors.  The only investment acumen required to reap this 18% return was leaving the fund alone.  Yet in the single best stock fund of the decade investors managed to misbehave and actually lose substantial amounts of money—11% annually.

    Even Morningstar is not sure what to do with Mr. Heebner:

    The fund, a highly concentrated portfolio typically holding fewer than 25 large-company stocks, offers “a really potent investment style, but it’s really hard for investors to use well,” says Christopher Davis, senior fund analyst at Morningstar. 

     I beg to differ.  It’s really hard to use well??  What does that even mean?  If it is, it’s only in the sense that a pet rock is really hard to care for.       

    Investor note: actively managed or adaptive products need to be left alone!   The whole idea of an active or adaptive product is that the manager will handle things for you, instead of you having to do it yourself.

    Unfortunately, there is an implicit belief among investors—and their advisors—that they can do a better job than the professionals running the funds, but every single study shows that belief to be false.  There is not one study of which I am aware that shows retail investors (or retail investors assisted by advisors) outperforming professional investors.  So where does that widespread belief come from?  

    From the biggest bogeyman in behavioral finance: overconfidence.  Confidence is a wonderful trait in human beings.  It gets us to attempt new things and to grow.  From an evolutionary point of view, it is probably quite adaptive.  In the financial arena, it’s a killer.  Like high blood pressure, it’s a silent killer too, because no one ever believes they are overconfident. 

    At a Harvard conference on behavioral finance, I heard Nobel Prize winner Daniel Kahneman talk about the best way to combat overconfidence.  He suggested intentionally taking what he called an “outside view.”   Instead of placing yourself—with all of your incredible and unique talents and abilities—in the midst of the situation, he proposed using an outside individual, like your neighbor, for instance.  Instead of asking, “What are the odds that I can quit my day job and open a top-performing hedge fund or play in the NBA?” ask instead, “What are the odds that my neighbor (the plumber, or the realtor, or the unemployed MBA) can quit his day job and open a top performing hedge fund or play in the NBA?”  When you put things in an outside context like that, they always seem a lot less likely according to Kahneman.  We all think of ourselves as special; in reality, we’re pretty much like everyone else.

    Why, then, are investors so quick to bail out on everyone else?  Overconfidence again.  Our generally mistaken belief that we are special makes everyone else not quite as special as us.  Overconfidence and belief in our own specialness makes us frame things completely differently:  when we have a bad quarter, it was probably bad luck on a couple of stock picks; if Bill Miller (to choose a recent example) has a bad quarter, it’s probably because he’s lost his marbles and his investment process is irretriveably broken.  We’d better bail out, fast.  (A lot of people came to that conclusion over the past couple of years.  In 2009, Legg Mason Value Trust was +40.6%, more than 14% ahead of its category peers.) 

    Think about an adaptive Dorsey, Wright Research model like DALI.  As conditions change, it attempts to adapt by changing its holdings.  Does it make sense to jump in and out of DALI depending on what happened last quarter or last year?  Of course not.  You either buy into the tactical approach or you don’t.  Once you decide to buy into—presumably because you agree with the general premise—a managed mutual fund, a managed account, or an active index, for goodness sakes, leave it alone. 

    In financial markets, overconfidence is the enemy of patience.  Overconfidence is expensive; patience with managed products can be quite rewarding.  In the example of the CGM Focus Fund, Mr. Heebner grew $10,000 into $61,444 over the course of the last ten years.  Investors in the fund, compounding at -11% annually, turned $10,000 into $3,118.  The difference of $58,326 is the dollar value of patience in black and white.

    1 Comment | From the MM, Investor Behavior, Thought Process | Permalink
    Posted by Mike Moody

    The Yield Curve Yells for Attention

    December 17, 2009

    With all of the fuss about the costs of healthcare reform, the TARP, the pros and cons of more economic stimulus, and Bernanke’s reappointment, economists seem to have taken their eye off the yield curve.  I haven’t read much commentary about it at all.  That’s unfortunate because the steep yield curve has a pretty dramatic message right now.

    FTAlphaville has a nice article on the yield curve today that includes the graphic below:

    This particular yield curve is constructed with the U.S. Treasury 2-year/10-year spread, and the spread is now at an all-time high.

    It turns out that the yield curve is one of the best, if not the best, predictors of economic activity, recession, and inflation down the road.  The forecasting record of the yield curve, for example, is much better than the record for various panels of economists.  (For the New York Federal Reserve Bank’s FAQ on the forecasting properties of the yield curve, click here.)  Pimco has a primer on the yield curve on its website which states:

    A sharply upward sloping, or steep yield curve, has often preceded an economic upturn. The assumption behind a steep yield curve is interest rates will begin to rise significantly in the future. Investors demand more yield as maturity extends if they expect rapid economic growth because of the associated risks of higher inflation and higher interest rates, which can both hurt bond returns. When inflation is rising, the Federal Reserve will often raise interest rates to fight inflation.

    At the conclusion of the Fed meeting yesterday, they voted to continue the policy of keeping short-term rates low.  As a result, we may see the yield curve continue to steepen.  Most economists (and stock market investors) are counting on a sluggish recovery—but that’s not the message the yield curve is sending out.  If the yield curve is correct, we could see much higher economic growth and much more inflation than is built into the consensus forecast.

    A pundit once wrote that economists were invented to make witch doctors look good.  Fortunately, I am not an economist and I have no idea what will happen to the economy going forward.  However, from an investment perspective, I am quite aware of the dangers of building an asset allocation based on a wildly incorrect economic forecast.  U.S. investors, by plowing enormous amounts of money into bonds and bond funds this year, are implicitly endorsing the consensus forecast of slower growth.  The yield curve is saying that could be a big mistake.

    As unlikely as it seems, what happens if we have powerful economic growth and rising inflation over the next couple of years?  For a baby boomer nearing retirement with a  portfolio loaded with fixed income it might be pretty painful.  It may be that commodities or inflation-indexed securities—or another asset class entirely—will work out better.  A more tactical approach to asset allocation removes the need to guess about what will happen and allows the investor to react to conditions as they change.

    4 Comments | From the MM, Markets, Tactical Asset Alloc, Thought Process | Permalink
    Posted by Mike Moody

    Capturing Trends

    December 16, 2009

    Intuitively, investors feel like the more nimble they are, the better they will do.  They put tremendous pressure of themselves to capture every wiggle in the market.  Yet, much of the time, going faster is counterproductive.

    In this blog post, “Understanding How Markets Move,” noted psychologist and trader Brett Steenbarger uses the simple example of a moving average system applied to the S&P 500.  The more you speed up the moving average, the worse it does.  That seems counter-intuitive, but you have to keep in mind that trends are what make money and trends  are often slow.  The faster you go, the more noise you capture, and thus, the worse you do.

    We find exactly the same process at work when using relative strength.  Reacting to short-term relative strength does not perform well over time.  The best-performing models follow intermediate to long-term relative strength—and just tough out the periods that are rocky.  Many clients have trouble sitting still when going through a rocky period, but as Steenbarger points out in his post, you have to deal with the asset you’re trading.  Stocks have their own time frames for trends and an impatient investor isn’t going to speed it up.  If you want to trade financial assets, you have to work with them on their own terms.

    Leave a Comment » | From the MM, Investor Behavior, Markets, Thought Process | Permalink
    Posted by Mike Moody

    Ken French Should Check His Website

    December 3, 2009

    A new paper from Eugene Fama and Ken French is circulating, suggesting that active mutual fund managers don’t add value.  Articles, like the one here at MarketWatch, have been appearing and the typical editorial slant is that you should just buy an index fund.

    I have a bone to pick with this article and its conclusions, but certain things are not in dispute.  Fama and French, in their article Luck versus Skill in the Cross Section of Mutual Fund Returns, look at the performance of domestic equity funds from 1984 to 2006.  (You can find a summary of the paper here.)  They discover that the funds, in aggregate, are worse than the market by 80 basis points per year–basically the amount of the fees and expenses.  (After backing out fees and expenses, the funds are 10 basis points per year above the market.)  After that, Fama and French run 10,000 simulations with alpha set to zero to see if the distribution of returns from actual fund managers is any different from the distribution of returns from the random simulations.  They conclude it is not very different and suggest that any fund manager that outperforms is simply lucky.

    Let me start my critique by pointing out that, based on their sample and their experimental design, their conclusions are probably correct.  Existing mutual funds in aggregate pretty much own the market portfolio and underperform by the amount of fees and expenses.  There clearly are some above-average mutual fund managers, but as Fama and French point out, it’s difficult to tell statistically from just performance data if they are good or simply lucky.  Within a big sample of funds like they had, after all, a few are bound to have good performance just because the sample is so large.

    This is quite a quandary for the individual investor, so let’s think about the realistic scenarios and their outcomes–in other words, let’s take actual investor behavior into account.

    Scenario 1.  Buy a mutual fund after its good performance is advertised somewhere and bail out when it has a bad year.  Continue this behavior throughout your investment lifetime.  According to Dalbar’s QAIB and other data, this is what actually happens most of the time.  Not a good outcome–underperformance by a large margin, often 500 basis points or more annually.

    Scenario 2.  Buy a decent mutual fund and make the radical decision to leave it alone, come hell or high water.  Do not be tempted by the blandishments of currently hot funds or panicked by underperformance in your fund when it inevitably happens.  Close eyes and hold on for dear life.  Continue your ostrich-with-its-head-in-the-sand routine throughout your investment lifetime.  Your outcome, as Fama and French point out, will probably be market returns less the 80 basis point per year in fees.  Your returns will probably be 400 basis points annually or more better than Scenario 1.

    Scenario 3. Throw active management overboard entirely.  Buy an S&P 500 index fund or a total market index fund and proceed as in Scenario 2.  Your outcome might be 60-70 basis points per year better from reduced costs than the investor in Scenario 2.  (Your cost is that you don’t get to brag at cocktail parties on the occasions when your actively managed fund has a good year.)  On the other hand, you are no less likely to succumb to Scenario 1 than an actively managed mutual fund investor.  Unfortunately, index mutual funds tend to show the same pattern of lagging returns due to investor behavior as actively managed funds.

    Scenario 4. Visit Ken French’s own website.  Look for factors that are tested and that have outperformed consistently over time.  Hint: relative strength.  (Academics tend to call it ”momentum,” I suspect because it would be very deflating to have to admit that anything related to technical analysis actually works.)  Find a manager that exposes a portfolio to the relative strength factor in a disciplined fashion over time.  Buy it and pretend you are Rip Van Winkle.  Continue this dolt-like behavior for your entire investment lifetime.  Your outcome, according to Ken French’s own website, is likely to be market outperformance on the magnitude of 500 basis points per year or more.  (You can link to an article showing a performance chart back to 1927 here, and the article also includes the link to Ken French’s database at Dartmouth University.)

    I prefer Scenario 4, but maybe that’s just me.  Since it is well-known even to Eugene Fama and Ken French that momentum has outperformed over time, what is their study really saying?  It’s saying that essentially no one in the mutual fund industry is employing this approach.  That’s more a problem with the mutual fund industry than it is with anything else.  (Mutual fund firms are businesses and they have their reasons for running the business the way they do.)  One option, I guess, is to throw up your hands and buy an index fund, but maybe it would make more sense to seek out the rare firms that are employing a disciplined relative strength approach and shoot for Scenario 4. 

    Their experimental design makes no sense to me.  Although I am 6′5″, I can no longer dunk a basketball.  I imagine that if I ran a sample of 10,000 random Americans and measured how close they could get to the rim, very few of them could dunk a basketball either.  If I created a distribution of jumping ability, would I conclude that, because I had a large sample size, the 300 people would could dunk were just lucky?  Since I know that dunking a basketball consistently is possible–just as Fama and French know that consistent outperformance is possible–does that really make any sense?  If I want to increase my odds of finding a portfolio of people who could dunk, wouldn’t it make more sense to expose my portfolio to dunking-related factors–like, say, only recruiting people who were 18 to 25 years old and 6′8″ or taller?  In the same fashion, if I am looking for portfolio outperformance, doesn’t it make a lot more sense to expose my portfolio to factors related to outperformance, like relative strength or deep value, rather than to conclude that managers who add value are just lucky?  No investigation of possible sub-groups that were consistently following relative strength or deep value strategies was done, so it is impossible to tell.  Fama and French are right, I think, in their assertion that plenty of luck is involved in year-to-year performance, but their overall conclusion is questionable.

    In short, I think a questionable experimental design and possible sub-groups buried in the aggregate data (see this post for more information on tricks with aggregate data) make their conclusions rather suspect.

    6 Comments | From the MM, Investor Behavior, Markets, Relative Strength Research, Thought Process | Permalink
    Posted by Mike Moody

    The Flaw of Averages

    December 2, 2009

    A fantastic article in the Wall Street Journal today discusses the problems with aggregated data, something we have discussed previously on this blog.

    The article discusses Simpson’s Paradox, a statistical phenomenon that can make averages misleading due to the differing sizes of subgroups.  It uses the example of unemployment.  The current unemployment rate is 10.2%, not as bad as at the peak of the 1982 recession when it was 10.8%.  However, according to Princeton University economics professor Henry Farber, compared with a similarly educated worker in 1982, “the worker today has higher unemployment at every education level.”  It turns out that the average unemployment rate is only lower now because today’s workers are, on average, more educated.

    College graduates, who have the lowest unemployment rate, are now more than a third of the work force, compared with roughly 25% in 1983, says the Labor Department. Meanwhile, the share of high-school dropouts has shrunk to roughly 10% of the work force, from nearly 20% in 1983.

    It could easily be argued that this recession is worse than 1982, since college graduates (4.9% versus 3.6%) as well as high school dropouts (14.9% versus 13.6%) are having more trouble finding jobs. 

    Aggregate data is tricky and can often obscure the real truth behind the numbers.  People find statistics persuasive and many groups cite statistics to “prove” their position.  This article points out that it is entirely possible that the statistics they cite prove exactly the opposite position. 

    In the investment industry, junk statistics can sometimes crop up in backtesting.  It’s important to know how the backtesting was conducted, whether the data set has survivor bias, how many parameters are fit to the data, and what kind of testing for robustness was done.  All too often, product behavior going forward does not match what was expected from the backtest.  Part of the appeal of our Systematic Relative Strength family of products, I think, is that the statistical testing is well done.  In fact, we are planning to put out a white paper on our proprietary testing methods and how they differ from what is typically seen in the not-too-distant future.  If you are interested in being on the distribution list for this white paper and you are not already on our distribution list, please sign up here.

    Leave a Comment » | From the MM, Relative Strength Research, Thought Process | Permalink
    Posted by Mike Moody

    What’s Really In Your Bond Fund?

    December 2, 2009

    A new study points out that many bonds funds are less safe than their average bond ratings seem.  Lower-rated bonds default at increasing rates, not at rates that increase in a constant fashion.  For example, an imaginary portfolio of 50% AAA bonds and 50% A bonds will carry an average rating of AA—but the default risk will be higher than if the portfolio were 100% AA.

    One article about the study remarks:

    Craig McCann, principal at Securities Litigation and Consulting Group and one of the study’s authors, cites their analysis of 285 taxable intermediate bond funds from Morningstar’s database, in which they excluded all of the duplicative share classes: 47 were graded AAA; 193 were AA; 38 were A; and seven were B. Of those funds, they found that only 18, or about 6%, warranted the grade they were given; 153 of the funds — more than half — should have been a letter grade lower; and, 112, or 40%, should have been two letter grades lower.

    You can read the whole white paper here.

    The point is that averages can hide a lot of things.  Statistics are extremely useful, but you still need to dig in and understand what you are looking at.  Due diligence for any investment product must be done carefully so that you know what to expect.

    Leave a Comment » | From the MM | Permalink
    Posted by Mike Moody

    Marriage of Deep Value and Momentum

    August 20, 2009

    Sam Mamudi, MarketWatch, recently profiled deep-value-pioneer Mutual Shares Corp.  Click here to read the article.  As explained by Peter Langerman, CEO of Mutual Shares Corp, deep value strategies are looking for companies in pre-bancruptcy or that are distressed.  As Langerman puts it, “It’s about buying a dollar value for 50 cents.”

    Deep value managers, like Mutual Shares Corp, have found an exploitable market inefficiency.  This happens to be a very different market inefficiency than we are focused on at Dorsey Wright, but they are very good at what they do.  A momentum or relative strength strategy is rarely involved in buying companies in pre-bancruptcy because our methodology leads us to securities that have been the best relative performers over an intermediate time horizon.  Both deep value and momentum have a well-documented history of being able to beat the market over time.

    However, each strategy has its vulnerabilities.  Deep value traps result when distressed securities are bought only to see them become more distressed.  Momentum underperforms during every major change in leadership.  However, mix the two strategies together and the benefits of diversification become apparent.  Creating two strategies so opposite in spirit and opposite in construction, and therefore so negatively correlated with each other, and still having them both produce positive average returns  is an area where financial advisors can add meaningful value to their client.

    The chart below is the efficient frontier of Dorsy Wright’s Systematic Relative Strength Global Macro strategy and Mutual Shares Corp’s flagship deep-value strategy, TESIX.  As you can see, over this time period having 100% of the portfolio in the Global Macro strategy produced the best returns.  However, it is possible to lower the annual standard deviation by having a mix of the two.  For reference, the S&P 500 generated annualized returns of -1.80% and standard deviation of 17.32% over this same time.

    (Click to Enlarge)

    Yes, in this case mixing the two results in lower overall returns, but that is fine with many people.  After all, sticking to a winning discipline for decades is the key to investment success.  Many investors get twitchy after very short periods of underperformance.  Mixing uncorrelated strategies is a way to address this problem.  It is not going to result in outperforming every quarter, but it is likely to result in a smoother ride over time.  Such an approach may be enough to keep investors from succumbing to the behavioral biases that will cause them to constantly chase the hottest manager.

    Certainly, this type of approach is not without its risks.  It is not enough to identify uncorrelated strategies.  The goal is to identify uncorrelated strategies that are also both able to generate above average returns over time.

    Shortly, we will offer the ability to use our website to create efficient frontiers on your own.

    To receive information about our Global Macro strategy, including important performance disclosures, please send an e-mail to andyh@dorseywright.com.

    Click here for disclosures from Dorsey Wright Money Management.

    Leave a Comment » | From the MM, Investor Behavior | Permalink
    Posted by Andy Hyer

    Less Can Be More

    August 18, 2009

    The WSJ reports that the average stock fund has 172 holdings.  What is the point of having that many holdings?  Diversification?  The table below reveals that there is very little incremental reduction in annual standard deviation once you get past about 20 holdings.

    (Click to Enlarge)

    Source:  M. Statman, “How Many Stocks Make a Diversified Portfolio?” Journal of Financial and Quantitative Analysis 22 (September 1987), pp. 353-64.

    The real reason mutual funds own so many stocks was revealed in an academic study conducted by researchers from Yale.   The real goal of most mutual fund managers is to reduce tracking error (volatility of portfolio return around a benchmark index.)  Many fund managers have realized the challenges associated with deviating from the benchmark and have chosen to increase the number of holdings so that they will never be too much worse than the benchmark.  Of course, they will never be too much better either.  With the impact of fees, such a closet-indexing approach is very unlikely to add any value over time.  However, that doesn’t keep the manager from telling a great story and attracting investors based on their perceived investment prowess.  The active-share study completed by K. J. Martijn Cremers and Antii Petajisto examined the proportion of stock holdings in a mutual fund’s composition that was different from the composition found in its benchmark.  The greater the difference between the asset composition of the fund and its benchmark, the greater the active share.  According to active-share study, there was a positive correlation between a fund’s active-share value and the fund’s performance against its benchmark.  For example, a mutual fund with an active-share percentage of 75% indicates that 75% of its assets differed from its index, while the remaining 25% mirrored the index.

    The study found that funds with a higher active-share value would tend to be more consistent in generating high returns against their benchmark indexes, which implies that more actively managed funds have more skilled managers.  However, higher active share necessarily means higher tracking error.  Since the 1980s there has been a steady rise in closet-indexing.

    Investors need to understand the real reason that most mutual funds have so many holdings.  After all, an active manager can only add value relative to the index by deviating from it.  If an investor’s goal is to beat the benchmark over time, buying a mutual fund with over 100 holdings (a closet indexer)  is not likely the way to go.  To beat the benchmark over time an investor needs to invest in strategies that have fewer holdings and, of course, a winning investment strategy.  On the other hand, if the investor’s goal is to match the benchmark over time then it is more cost effective to buy an index fund from Vanguard for 9 basis points.

    We make no secret about the fact that our relative strength strategies have high active-share (most above 90%).  Most of our strategies have 20-25 holdings.  While others serve the closet-indexing market, we have chosen to serve the active-investors market.

    Leave a Comment » | From the MM, Markets | Permalink
    Posted by Andy Hyer

    DWAFX Ranks Highly at 3-Year Mark

    August 11, 2009

    The Arrow DWA Balanced Fund (DWAFX) is one of the top mutual funds in its class.  It has finished in the 9th percentile in the Morningstar Moderate Allocation Category (better than 91 percent of the 924 funds in this category) over the last three years.

    (Click to Enlarge)

    DWAFX is the first Dorsey Wright managed mutual fund and we are very pleased with its success.  Furthermore, we are very appreciative of the warm reception that it has received in the marketplace – thank you!  We developed this strategy because of the following philosophy:

    • The standard 60/40 policy portfolio is too narrow.  Broader diversification, with special emphasis on alternative investments, is helpful to returns and risk management.
    • Endowment managers, like David Swenson at Yale, have been generating superior results for years by creating allocations with significant exposure to alternative asset classes.  Now that ETFs provide access to most all of the asset classes that have been used in the endowment models, such a broadly diversified approach has been made available to the public through DWAFX.
    • The tactical asset allocation approach, driven by our systematic relative strength process, allows us to be extremely adaptive. This unique process seeks to do an excellent job of protecting on the downside as well as capitalizing on bull market moves in a wide variety of asset classes.

    Click here to access the fact sheet for the Arrow DWA Balanced Fund.

     

    Leave a Comment » | From the MM | Permalink
    Posted by Andy Hyer

    RS In Depth

    July 24, 2009

    Most investors misunderstand—or maybe “over-simplify” is the right term– Relative Strength (RS).  I think a big part of the problem is the inherent desire to make RS sound more simple and basic than it really is.  Simple processes are easier for a novice to understand and accept as a viable investment strategy.  From the standpoint of the expert talking to a novice this is preferable because a novice doesn’t have the time or inclination to learn all of the complexities of the strategy.  In other words, a novice doesn’t need to know how to build a watch; they just need to know how to tell time.

    The reality is that RS is much more complicated than people actually think.  When we talk to advisors they are often under the impression that a strong stock is a strong stock and there is only one way to measure strength.  It just so happens that the best measure of momentum, according to whoever you are talking to, is the method that person has been using lately!  RS is much more complicated than that.  There are numerous ways to measure the strength of a stock.

    It is no different than a value strategy.  If you ask a room full of value managers what defines a “value stock,” you’re going to get a room full of answers.  Some people define value in terms of dividend yield, while others might use a price-to-cash flow model.  If you look at the portfolios of these two individuals, they are going to have different holdings even though they are both buying “cheap” stocks.  More importantly, the portfolios are going to perform differently during different parts of the market cycle.  The market rewards different factors to different degrees at different times.  Both factors might outperform over a long time horizon, but over any short time period the performance might be dramatically different.  There is nothing wrong with this!  If the dividend yield manager underperforms the cash flow manager during a given time period, it might not have anything to do with the skill of the two managers–it might simply be a matter of the market rewarding different value factors at different times.

    Just as there are numerous value factors, relative strength can be calculated in many different ways.  Even something as straightforward as point & figure relative strength has numerous calculation methodologies.  You can use a 3.25% box size, 6.50% box size, the old Chartcraft standard boxes, a matrix (using any box size you want), or anything else you can dream up.  You can also favor an RS column change, and RS buy signal, or some combination of the two.  Dorsey Wright doesn’t even advocate one superior calculation method, as the research database makes all of these different calculation tools available to you.  The market will reward these different RS factors at different times.  Sometimes short term strength is rewarded more than long term strength; sometimes it’s the other way around.  It is no different than the value stock example discussed above. If one person uses a 12-month price return model to measure RS and another uses a 3-month price return model, their portfolios are going to hold different securities, have different turnover, and performance might be completely different during any given time period.  Both managers are using relative strength and buying strong stocks–they are just defining RS slightly differently.

    For example, take a look at how several different RS calculation methodologies performed during the first 6 months of 2009.  As part of our research and ongoing process of continuous improvement, we track lots of RS factors in the Money Management office (besides our own proprietary measurement).  Here, we are just using some simple price-return-over-time models to illustrate our point.

    table1

    The data in the table above shows that the way you define relative strength leads to very different return profiles over short time periods. All of these models outperform the broad market over long periods of time (we do have the data on that!) so they are all acceptable ways to determine strength.  However, over half a year the market has rewarded each factor very differently.

    In addition to the variation in returns between RS factors there will also be variation in returns within an RS factor model.  Unless two managers buy the entire basket of high RS securities, their portfolios are going to look different.  This may seem obvious, but it is something many people don’t consider when evaluating an RS strategy.  While the chosen factor might be robust enough to deliver market-beating returns over time, any sub-set of the high RS basket might perform better or worse than any other sub-set over a given time period.  There can be massive variation between portfolios, even ones using the exact same RS factor.  Here, we use 100 different random trials to give you some sense of the possible variation.  The following table illustrates this point.

    table2

    Using exactly the same factor, your returns could range from -21% to +8%, depending on which stocks in the group you ended up with.

    We use a unique and robust testing protocol here in the Money Management office.  We try to make everything as “real world” as possible.  In a paper our portfolio staff wrote way back in 2005, we designed a random trade process that allowed us to randomly select high RS securities for a concentrated portfolio.  The theory is this: if you can select stocks at random from a predefined sub-set of a universe and still outperform a benchmark over time you have a remarkably robust process.  That random trade generation process was used to test the model in the table above.  You can see that with the same parameters and same investment universe there can be wildly different results over a short time period.  Over a longer testing period (1995 through mid-2009) all 100 random trials of the simple 6-month model outperformed the broad market.  But over a short period of time, it is quite possible to get stuck with a low-probability, lousy outcome.  (You can even get quarters where about half the trials outperform and half underperform.)

    There is a tendency for investors, when they get stuck with a lousy outcome, to believe the process is broken.  It isn’t.  Most people want to believe they are in control of every situation so having to think in terms of probabilities often makes them uneasy.  However, it’s the reality of investing: not just in an RS strategy, but in every other strategy as well.

    As you can see, there is variation in relative strength strategies just like there is variation in returns with every other investment strategy.  There isn’t one right or wrong way to calculate RS, although we do know that some ways are better than others!  (We happen to be fans of our proprietary method.)  Every calculation methodology has strengths and weaknesses.  Both the strengths and weaknesses will be exposed at some point during the market cycle.  There is no way to avoid this phenomenon.  There is no magic unicorn that’s going to appear and tell you the best way to identify a strong stock this week—and it will change next week anyway.  You’re better off to stop looking for the unicorn and spend your time testing for a robust method that will work over a long period of time, and then understanding everything you can about the tradeoffs you are making.  Our bias is to use exhaustive testing and data analysis to investigate and make decisions about the tradeoffs we need to live with.  Perhaps not everyone has the resources and programming ability to make that feasible, but you can spend time thinking about how your factor is constructed and where it might be vulnerable.  There are very few guarantees in finance, but I can make this one:  if you truly understand everything about the statistical parameters of your process, the next time someone tries to tell you your model is broken, you might have a knowing smile instead of a concerned frown.

    1 Comment | From the MM, Thought Process | Permalink
    Posted by John Lewis

    Peer Pressure

    May 28, 2009

    In the 1950’s, a psychologist named Solomon Asch made a startling discovery about the power of peer pressure.  He asked experimental subjects to determine the length of one line relative to three other lines.  When there was no peer pressure, subjects found it to be a simple test and had 100% correct answers.  When there was peer pressure–in the form of confederates of the experimenter who all loudly gave the wrong answer–more than 75% of the subjects also gave an incorrect answer in order not to be out of step with the crowd.  It was clear from the control group results that none of the subjects were really  confused about the length of the lines, but when faced with group members who all apparently saw the same thing, lots of subjects buckled under.

    Every time I see a press release from the CFA Society (full disclosure: I am an affiliate member) that details how many people take the CFA exam every year, I think about Solomon Asch.  This year, for example, 128,600 candidates from 154 countries have registered for the June exams.   (You can see the press release here.)   That is a lot of CFAs in training!  There is no doubt that their particular form of fundamental analysis is the dominant method of securities analysis in the world today.  More than that, their influence has spread to infect thinking about diversification, asset allocation, portfolio theory and beyond.  They’ve developed a large curriculum of readings to reinforce their position and these days it is hardly ever questioned.

    Technical analysis is a much older form of securities analysis, one that relies not on theories about how markets operate but on market-generated data such as price and volume.  It often continues to be useful when markets don’t operate according to the rules, i.e., most of the time.  Technical analysts tend to be a pragmatic lot.  They go with what is working, and when it stops working, they get off and go on to the next thing. 

    The CFA program, in contrast,  promotes deep thinking about complex systems, fundamentals, and causation.  It doesn’t surprise me that it is hard to be right about things that have so many variables.  And I always wonder if they might be missing something by all approaching the problem with the same mindset.  Is there pressure to conform to the accepted theories?  Do all the lines look the same length to them?

    In the financial markets, when everyone is looking at a market and thinking the same way about it,  usually it does the opposite of what is expected.  I suspect there is a lot of value in approaching asset allocation from a different point of view.

    1 Comment | From the MM, Tactical Asset Alloc, Thought Process | Permalink
    Posted by Mike Moody

    Correlation Does Not Imply Causality

    May 21, 2009

    This is an interesting chart that I dredged out of an article by Mebane Faber reporting on a global asset allocation summit.  At least during 2008, when the dollar fell, gold went up.  (The dollar chart is inverted.)  Or is it that when gold went up, the dollar fell?  This type of chart can only ever show correlation—and correlation does not imply causality.  Who knows what causes what?  The problem is that many strategic asset allocation models are built with correlations.  The models often assume that the correlations are stable, but experience has shown that they are not.  (This is no less a problem for Modern Portfolio Theory.)  Models that are built with correlations fail each time there is a regime change where the correlations shift or adjust to a “new” normal.  Things that are impossible in finance textbooks happen all the time in real life.

    Our Systematic Relative Strength models are not built this way.  We look at the relative strength ranking of each item on its own merits.  If gold is highly ranked in the Global Macro universe, for example, it would likely be in the portfolio.  If the dollar is also highly ranked, it would happily coexist with gold until the rankings dictated that one of them was removed from the portfolio.  Relative strength doesn’t make any assumptions about the asset correlations, and we think that is one of its strengths.

    Correlations

    Correlations

    Click here for disclosures from Dorsey Wright Money Management.

    Leave a Comment » | From the MM, Tactical Asset Alloc, Thought Process | Permalink
    Posted by Mike Moody

    What’s It Like to Work at Dorsey Wright Money Management?

    May 21, 2009

    My name is JP Lee, and I’m the youngest member of the team here at DWA Money Management. 

    John, Andy and Mike have dominated the serious posts so far. I won’t try to compete with them in the data analysis department; instead, I’ll bring a little humanity to the equation. Some soul, if you will. So here goes. 

    One of the first questions that you ask somebody when you get to know them is, “What do you do for a living?” Depending on who’s asking, I say a number of things. 

    I’m an assistant at a money management firm. 

    You know what stocks are, right? I work with stocks. 

    I work in Pasadena….in an office building. 

    I stuff envelopes for a living. 

    Once in a blue moon I’ll meet someone who follows the market or is an investor (I’m 25 years old, I don’t hang out in the Chairman’s Club). It’s always funny to me when people give me their opinions on this stock or that stock, or a brief rundown on what the economy “really” needs to get going again. Rants about corporate greed, the housing market and the next Great Depression, lifted straight out of Newsweek and Time. It’s fun to smile and nod along with market mavens. 

    At the end of the day, I wear a lot of hats. I answer the phone, send out new client welcome packets, reconcile monthly account statements, and help organize quarterly statements and billing. Every once in a while I might liquidate a new account’s holdings and get the cash ready to invest in the DWA strategy. There’s a lot going on in the office, and there’s only 5 people here. It’s fun and exciting. 

    As the only member of Generation Y in the office, I consider myself lucky to be here. Just today I saw a headline about college graduates in 2009 that showed that LESS THAN 20% of those who graduate this year will be employed. That’s a scary number. 

    Stuffing envelopes never sounded so fun. I love this job!

    3 Comments | From the MM, Off-Topic | Permalink
    Posted by JP Lee

    Emotional Asset Allocation

    May 8, 2009

    The steep losses experienced by investors over the last year and a half have led to many changes in investor behavior.  The personal savings rate is ticking up, some measure of frugality has returned to the consumer, and the asset allocation framework in place for many investors is being seriously questioned.  Among the changes has been a dramatic reduction in appetite for risk among investors.  In fact, 56% of Baby Boomers have now concluded that the stock market is too risky for people their age (San Francisco Business Times, 2/13/09).

    This change in appetite for risk is manifested in the floods of money pouring into bond funds, as can be seen in the table below (Investment News, 5/4/09):

    Morningstar Category

    1Q’09 Net Flows
    1.  Intermediate-term bond $24,076
    2.  Precious metals 14,991
    3.  High-yield bond 7,673
    4.  Natural resources 6,765
    5.  Short-term bond 6,210
    6.  Municipal national short 5,214
    7.  Long-term bond 4,647
    8.  Inflation-protected bond 4,551
    9.  Municipal national intermediate 3,423
    10. Intermediate government 3,395

    As investors throw their hands up in despair, torn between putting the bulk of their assets in bonds or embarking on an experiment with day-trading financial stocks, I suggest presenting them with the following data.  Right now, you may be thinking that you are about to read some fascinating new piece of data.  Fascinating is probably not the adjective for life expectancy-data, but perhaps nothing is more important to consider when deciding what changes investors should make right now.  The following data is taken from the Centers for Disease Control and Prevention, updated through 2005.

     

    U.S. Life expectancy at birth

    Men

    75.2 years

    Women

    80.4

     

    U.S. Life expectancy at age 65

    Men

    82.2 years

    Women

    85.0

     

    U.S Life expectancy at age 75

    Men

    85.8 years

    Women

    87.8

     

    Emotions are running extremely high right now, which means that investors are very susceptible to making poor investment decisions.  Any radical changes in framework for asset allocation should be done with the long-term in mind, especially now.  Keep in mind that life expectancy means that one-half of the sample will live shorter than the expectancy, and one-half of the sample will live longer.  After all, it is very likely that many of your clients will live well in to their eighties or nineties.  With that in mind, a diversified bond portfolio doesn’t make a whole lot of sense; nor does it make much sense to embark on some unproven trading strategy.

    When empirical evidence is used, relative strength and tactical asset allocation appear in a very favorable light.

    Leave a Comment » | From the MM, Tactical Asset Alloc | Permalink
    Posted by Andy Hyer