The Limits of Diversification

November 23, 2009

Jason Zweig’s usually wonderful Intelligent Investor column in the Wall Street Journal had a real winner this week. He wrote about a finance professor that did some studies on diversification. The idea that diversification is essentially a free lunch is ingrained in modern finance. Investors are taught that proper portfolio construction involves owning enough companies, generally 20-30, to reduce stock-specific risk.

Mr. Zweig describes the base case for diversification when looking at aggregated data:

Don Chance, a finance professor in the business school at Louisiana State University, asked 202 students to select one stock they wanted to own, then to add a second, a third and so on until they each held a portfolio of 30 stocks.

Prof. Chance wanted to prove to his students that diversification works. On average, for the group as a whole, diversifying from one stock to 20 cut the riskiness of portfolios by roughly 40%, just as the research predicted. “It was like a magic trick,” Prof. Chance says. “The classes produced the exact same graph that’s in their textbook.”

Aggregated data, though, is tricky stuff. It’s one thing to see a pattern across a large number of cases, but what happens when you drill down into the individual portfolios that were constructed? This type of micro-analysis is often not done in finance, which can create a less nuanced view of reality. Indeed, when Dr. Chance looked at the data closely, there were some surprises.

But then Prof. Chance went back and analyzed the results student by student, and found that diversification failed remarkably often. As they broadened their holdings from a single stock to a basket of 30, many of the students raised their risk instead of lowering it. One in nine times, they ended up with 30-stock portfolios that were riskier than the single company they had started with. For 23%, the final 30-stock basket fluctuated more than it had with only five stocks.

The lesson: For any given investor, the averages mightn’t apply. “We send this message out that you don’t need that many stocks to diversify,” Prof. Chance says, “but that’s just not true.” What accounts for these results? Leave it to a professor called Chance to show that even a random process produces seemingly unlikely outliers. Thirteen percent of the time, a 20-stock portfolio generated by computer will be riskier than a one-stock portfolio.

The averages might not apply because of interaction effects within the portfolio, and because outliers are perhaps more common that we would like to believe. Even a portfolio built by throwing darts might not have the diversification that an investor is seeking.

Diversification is just one form of risk management, and it is clearly not a complete solution. Our Systematic Relative Strength accounts, for example, also break the investment universe into baskets and require portfolios to be built from a number of different baskets. Even so, portfolio volatility can change over time as new securities come into the portfolios and underperformers are eliminated. At times, risk is rewarded and portfolios might be loaded with risky securities; in fearful investment climates, portfolios might instead have a large helping of cement-like securities. Along with diversification, having a systematic investment process that adapts to the environment can be helpful in managing risk.


Eat Your Heart Out, Roubini

November 23, 2009

A sign making its way into offices around the world. Very appropriate.

From NYT


52-Week RS Model

November 23, 2009

Relative strength strategies are compelling for a number of reasons. First, intuitively it make sense that buying and holding winners and selling losers should be an effective way to navigate the markets. Second, relative strength has been shown time and time again by practitioners and academic studies to be a viable method of beating the market over time. Surely, one of the most effective ways to help investors to commit to a relative strength strategy for the long-term is to share with them some of the body of research on relative strength investing.

Among the relative strength factors that we test is a nonproprietary 52-week return model. This model ranks a universe of securities by their trailing 52-week performance and then divides them into percentile ranks. The investment universe for this model is the S&P 900, which consists of U.S. mid and large cap stocks. The testing period is the nearly 14-year period from 12/31/1995 - 9/30/2009. For this test, we defined a target number of holdings for the portfolio, a buy threshold, and a sell threshold. The buy threshold was the minimum percentile score a stock would need to make it eligible for inclusion in the portfolio. If we set this parameter at 90, for example, only stocks in the top declile (or those with a percentile rank above 90) were eligible for inclusion in the portfolio. The sell threshold was the level at which a stock was automatically sold out of the portfolio and replaced with a stronger stock. We used a buy threshold to define a basket of eligible stocks and then picked one stock at random from the basket. Each security was reviewed weekly and not sold unless its rank fell below the predefined sell threshold. We used this methodology to run 100 simulations for the model with the given parameters. These Monte Carlo simulations also demonstrate the robustness of relative strength because they show the returns are not clustered in a small number of stocks.

Results of this test are shown below:

(Click to Enlarge)

The green dot is the return of the S&P 500 in that given year. The red bar is the average return of the 100 simulations of the relative strength model. The range of returns of each of the trials is also shown.

The percentage of trials that resulted in outperformance in any given year is shown in the table below.

The table below shows the results of all of the simulations over the entire test period.

For comparison, the cumulative return of the S&P 500 over this time period was 71.62% while the average relative strength simulation over the same time was 211.16%. Even the single worst trial for the relative strength model generated superior returns over the that period of time.

Conclusions

  • The Monte Carlo methodology is evidence of robustness of the process, since all 100 trials led to outperformance over the entire test period.
  • Year-to-year there is large dispersion in the performance of this relative strength model compared to the S&P 500.
  • With the exception of 2007, the last couple years have not been a good environment for relative strength.
  • If you believe, as we do, that winning investment styles move in and out of favor over time then you may wish take advantage of the opportunity to add to a long-term winning strategy while it is out of favor.

Debt Wave

November 23, 2009

Financing costs for the U.S. Treasury are projected to go from $202 billion now to $700 billion or more in the next ten years. As this New York Times article points out, the additional $500 billion is more than the combined federal budgets this year for education, energy, homeland security and the wars in Iraq and Afghanistan.

One complicating factor is that the rapid increase in debt service payments is coming at exactly the same time the Treasury is trying to finance giant increases in Medicare and Social Security as a result of the baby-boomers reaching retirement age.

One consequence of this debt buildup is that credit default swap volume against sovereign debt default for industrialized countries has doubled over the past year, while the swap volume to insure against default in emerging markets has dropped. Investors are increasingly betting that the debt wave will be unsustainable and may result in defaults in large economies.

The growth in debt service costs is already running at a much higher rate than growth in GDP, which means it is no longer possible to grow our way out of the problem. We are headed toward an inevitable showdown between Congress’ propensity to spend and the bond market’s reluctance to finance all of that spending. The investment environment could change radically and it will be important to have a flexible investment policy that can adapt.


Weekly RS Recap

November 23, 2009

The table below shows the performance of a universe of mid and large cap U.S. equities, broken down by relative strength decile and quartile and then compared to the universe return. Those at the top of the ranks are those stocks which have the best intermediate-term relative strength. Relative strength strategies buy securities that have strong intermediate-term relative strength and hold them as long as they remain strong.

Last week’s performance (11/16/09 - 11/20/09) is as follows:

The better performance last week was found in those stocks with the worst longer-term relative strength. The week before saw just the opposite. It seems that relative strength strategies experienced their worst underperformance in the several months following the March 9th lows. However, over the last couple of months it seems that relative strength leaders and laggards have been trading shots with no one side proving decisive. Time will tell if this is just a transitional period into a more favorable environment for relative strength strategies.