If someone out there can pick winning stocks, then they should be able to pick the losers too, right?

In Forbes Magazine, John Rogers reviews a stock picking test that he implemented.  Essentially, he asked that 71 staff members from his Chicago-based firm, pick 10 stocks that would underperform the 2Q 2009 market.  Of the 71, only 19 succeeded.  So, of 71 people, only 27% were able to purposefully perform worse than the market.  That leaves 73% of the staff members from the sample actually doing as well as the market while under the directive that they should lose to the market.  What does that say about their ability to meet your long-term objectives?

In late 2008 and early 2009, many investors speculated that active managers would fare well due to the high volatility.  Essentially, because stocks of the same asset class were showing increased volatility and lower correlations to each other, the thought was that active managers would really be able to shine.  As we’ve seen, and as Roger’s article shows, even when they try to lose, they can’t do so in any significant manner.

Play your chances as you like, but it seems to us that a professional that claims to be able to pick the right stocks should also be able to remove the wrong stocks.  But that might also imply that the same money managers could perform consistently.  We invite investors to step up and achieve market returns, year-after-year, through a disciplined, and globally diversified multiple asset-class portfolio.

In wine there is truth. Over the weekend, The Wall Street Journal helped bring this truth to light.

People have different tastes, and expectations and environment can drastically alter what they taste. It’s all very psychological. A bottle of Bordeaux that costs $150, and has won numerous awards is believed to be spectacular before the bottle is opened. No matter your personal preferences, this Bordeaux has got be good. Put it in a box with a push-button dispenser instead of a crystal decanter, and I think we can all agree that our expectations will change. It begs the question of the accuracy of ratings and the personal biases that are applied when a neutral perspective would really be more valued.

The various studies speak to so many of the human biases that we apply to all things. One study of wine competitions illustrates “that the probability that a wine [that] won a gold medal in one competition would win nothing in others was high.” To follow up on this study, defining parameters were set and it was found that “the distribution of medals…mirrors what might be expected should a gold medal be awarded by chance alone.” Even with a purportedly systematic rating system, randomness persisted.

Asset class performance is random, and so fund performance is also random. We cannot truly compare stocks to grapes, or indexes to grape vines, though certainly some metaphors can be poetically woven. What we can do is take a step back and recognize our own behavior patterns and the effect they have on our decisions.

Leonard Mlodinow is the author of the WSJ article, for his bio and other information, please click here.

A few of our clients have called in the last couple of weeks asking why their portfolio had taken a loss in October.  All of the clients cited various media sources that were spewing about how great the markets have been.  They asked, with all of this wonderful news, why did my portfolio take a loss in October?  The answer is rather straightforward, and that is because global equity markets were largely down, and fixed income was relatively flat; but we see deeper issues that we can’t help but address.

Media feeds off of fear mongering and elation.  At least, that’s our take on their cheap magazines and 24-hour “Action News” stream.  I think most of us accept this fact or at least have come to understand how that machine works.  Nonetheless, not all investors are tracking markets very closely.  Rather, they tend to catch the emotional “wind” of the media.

From an investment perspective, one month’s returns is too short of a time span to measure any meaningful metrics.  Reviewing performance over such a short period magnifies the experienced volatility. Below is a graph that shows the returns of the S&P 500, measured on a monthly basis, for the last 30 years. The point of showing this is to illustrate the volatility experienced month over month, for 30-years.

Over the short-term, we expect to see more volatility, and over the long-term we expect for things to be much smoother.  To be a good and disciplined investor, we need have the right expectations and the right understanding of how markets work.  The long-term approach is important for so many reasons.  In today’s blog, a long-term horizon makes for a smoother and more successful investment experience.

*Returns data from Center for Research on Security Prices (CRSP).

One of my favorite graphics is this “periodic table” - periodic in the sense that it shows asset class performance on an interval basis.  Not only is it colorful, but it tells a big story.  The moral of the story is simple - diversify across global markets and invest for the long-term.

After investors understand what they’re looking at, they usually do two things.  First, they look to see the top performers and attempt to confirm that their past beliefs were correct.  Second, they usually look to establish a pattern over the years.  It’s human nature to look for patterns.  Something about the appeal of symmetry causes us to seek a familiar trend, even amid complete randomness.  Volatility and unpredictability becomes a bit more visible in this graph, and going back over longer time periods would show even more random behavior.

(To read these charts, look at the asset classes on the left, then follow the color code throughout the returns data - this will show how each asset class stacks up.)

If Randomness persists, what is the solution?  In this next table, we sort the asset classes from the highest to lowest growth of wealth, over the same 9-year period.  If one had held only Emerging Markets for the last 9-years, one would have had the highest annualized return, the second highest risk profile, and the second worst growth of wealth total return.

There are many ways to analyze this data, but what we hope to illustrate is that even a simple portfolio construction of equally weighted asset classes beat 7 out of 11 asset classes over this time period.  When faced with moderate returns and moderate risk, investors often snub the idea since people tend to be competitive and want to be the best (which often means taking more risk).  In the long-term, a well-constructed portfolio that delivers consistent “middle of the road” returns, along with calculated risk, would almost certainly outperform individual asset classes.  The reason is that such a strategy enables investors to capture all of the return premiums provided by the market.  Putting such a portfolio together is not difficult; it is our human element that is difficult to control.  Discipline may be boring and may feel restrictive, but it is at the core of what helps us achieve our personal benchmarks.

We have data on longer time periods, please contact us if you’d like more information.

Disclosures:
This matrix shows annual total returns per asset class, starting 11/2000 and ending 10/31/2009. Returns data is provided by the Center for Research on Security Prices (CRSP). While data is retrieved from sources believed to be reliable, McLean Asset Management Corporation can not guarantee the accuracy of this data.  One cannot invest directly in an index. No investor earned the exact returns displayed. Past Performance is not an indicator of future performance.

Indexes used:
US Large Market = S&P 500
US Large Value = Russell 1000 Value
US Small Market = Russell 2000
US Small Value = Russell 2000 Value
US Micro Cap = Russell Microcap
US REIT = DJ Wilshire REIT Index
Emerging Markets = MSCI EAFE Emerging Markets
Intl Large Cap Market = MSCI EAFE Index
Intl Large Cap Value = MSCI EAFE Value Index
Intl Small Cap Market = MSCI EAFE Small Cap Index

Survivorship bias: The tendency to exclude the performance data of funds that have not survived.  Results become skewed because only the funds successful enough to survive have been included.