In the previous two articles on surviving market crashes (“Surviving market crashes” and “Surviving market crashes, Part 2: The dot-com bust“) we looked at how Modified Modern Portfolio Theory (MMPT)–an innovative modification to Modern Portfolio Theory (MPT)–provided asset allocation recommendations that resulted in the investor not only avoiding the bulk of losses during market crashes but, in the case of the dot-com bust, actually producing a substantial positive return.

This article is a continuation of the same theme, but here, we’ll be looking at something that wreaked more havoc on portfolio returns than did the dot-com bust—the subprime mortgage meltdown from 2007-2009. We shall show that MMPT-based portfolios again produce superior returns at much lower risk than their classic MPT-based counterparts.

Summary of the MMPT methodology
Since the MMPT methodology was explained in the first two articles, the reader is referred to those for the detailed explanation. Briefly, the approach applies a robust market-timing scheme to Modern Portfolio Theory to determine when one should allocate funds to a particular asset class and when one should take money out of that asset class and place it in the safety of the risk-free asset class (cash, insured money markets, or T-bills).

To illustrate fund performance, nine of the most commonly used asset classes form the basis of the our model investment portfolio: large-cap stocks, small-cap stocks, long-term investment grade corporate bonds, long-term government bonds, intermediate-term government bonds, real estate investment trusts (REITs), international stocks, international bonds, and T-bills (or some other risk-free asset class). Gold and other precious metals are not included for reasons given previously (although we could add it if so desired since we have the gold database).

The devastation of the subprime mortgage crisis
The time frame we’ll be using in this analysis begins on October 11, 2007 when the S&P 500 hit an intraday high of 1576 and ends on March 6, 2009 when the S&P 500 reached its intraday low of 667. This represents a loss of over 57%, more than the 50% loss suffered during the dot-com bust. Figure 1 (which includes dividend income) illustrates just how steep this plunge was.

Figure 1. Total Return Derived from the S&P 500

Along with a steeper loss in the large-cap stocks, the mortgage crisis took a major toll on small-caps as well with similar losses. This was not the case in the dot-com bust where the value of small-cap stocks actually held on despite large price fluctuations.

Figure 2. Total Return Derived from Small Cap Stocks

Table 1 below shows that the annualized returns for all asset classes except for government bonds did poorly during this time period. Comparing these returns with those during the dot-com bust (refer to Table 3 in the previous article), we can see that everything except for government bonds fared much worse during the subprime crisis especially REITs which went from a +7% return to a whopping -54% loss.

Table 1: Annualized total returns of all asset classes during the subprime mortgage financial crisis

Portfolio allocations during the mortgage meltdown
As in the previous articles, we are requiring a 10% compounded return for our model portfolios. Portfolios are rebalanced monthly as new total return asset class performance data is made available. The portfolio allocation tool used for both approaches is the SMC Analyzer.

Table 2 below shows that a classic MPT portfolio would have had roughly 60 – 80% of their assets allocated to equities with the rest divided between REITs and corporate bonds. Table 1 above shows that it is exactly these asset classes that fared the worst during this time period.

Table 2. Classic MPT Historical Allocations During the Subprime Mortgage Financial Crisis for a Target 10% Compounded Annual Return

By comparison, the MMPT equivalent portfolio (Table 3) was already light in the large stock asset class, having benefited from the experience of the dot-com bubble collapse. For the entire period of the subprime meltdown, the MMPT portfolio was heavily weighted (70-100%) towards medium-term government bonds and T-bills with some minor exposure to small-caps and international equities.

Table 3. MMPT Historical Allocations During the Subprime Mortgage Financial Crisis for a Target 10% Compounded Annual Return

As Table 1 shows, it was this exposure to the equity classes that caused the most harm to both portfolios. It’s interesting to note that long-term government bonds fared the best in a so called flight to safety but MMPT did not allocate funds to it instead preferring the historical lower volatility (risk) of the intermediate-term bonds.

Comparison of portfolio returns
During the months of the dot-com bust, the MMPT portfolio lost at a rate of only 1.1% compounded annually (green line in Figure 3 below) while the classic MPT portfolio lost money at an annualized rate of -32.9% (magenta line). Further, the MMPT portfolio was much less volatile. It experienced a standard deviation of only 4.8% while the classic MPT investor was being whipsawed to the tune of 17.3%. Sure, suffering a small loss isn’t desirable but it’s certainly a lot more palatable than losing a third of your holdings. Which scenario would you have chosen?

[As an aside, I attended a conference of public fund and hedge fund managers who actually admitted to losing between 25 and 50% of their funds’ assets during this period.]

Figure 3. Comparison of results
(green = MMPT)
(magenta = MPT)

Summary
We have shown that an MMPT investor during the subprime mortgage financial crisis was able to keep almost all of his money while most other investors were getting clobbered. The reason is that a judiciously and properly applied market timing approach injects an element of nimbleness and reactivity to a portfolio while still benefiting from historical experience. This is of tremendous value especially in today’s uncertain markets.

Every day the news concerning the deterioration of the global economy is increasing volatility across most of the traditional asset classes, and it’s for this reason that market timing approaches should be given their proper due. I believe we have shown with these case studies that the MMPT approach combines the best of Modern Portfolio Theory with the desirability of a viable market timing strategy.

For further information on how you can save your nest egg from the devastation of market crashes, please visit our website.

Introduction
With the continued upswing in the price of gold many investors are once again either wondering if they should buy, sell, or hold the metal in their investment portfolios. This article will examine the contributions made by the gold asset class to a investment portfolio allocated according to Modern Portfolio Theory (MPT).

Used by many fund managers and investment professionals, MPT allocates asset classes according to a desired average rate of return while simultaneously minimizing portfolio risk. Risk can be looked at as fluctuations in portfolio returns; in MPT, risk is measured by a statistical term called the standard deviation.

Because of the broad scope, this discussion is broken up into two parts. In the first part, we’ll compare allocations, returns, and risk in portfolios where gold is included to those portfolios where it isn’t. This comparison will tell us to what extent gold is meaningful as an asset class in a diversified portfolio with a long investment horizon.

In part two, we’ll revisit an article written a couple of years ago to see what effect the recent rally in gold has had on long term returns. We’ll examine it using traditional MPT assumptions and compare the results to those obtained by adding the extra dimension of optimized market timing to see how we can actually decrease risk while increasing returns.

The politics of gold
Our MPT portfolio includes the nine traditional asset classes (see tables below). Monthly total return data from all of these asset classes were only readily available after 1928 which is when we’d like to begin our analysis. We could start later, but in statistics, the more data one has, the more reliable the results.

One problem with this approach is that the price of gold was politically controlled for much of the time between 1928 and the present. In 1933, Americans were prohibited from owning non-jewelry gold and in 1961 they were even barred from holding gold in foreign countries.

Further, the price of gold was fixed in 1934 at $35 per ounce. That changed in 1968 when a two-tiered pricing system separated official transactions at the fixed price while providing for a free market at fluctuating prices. In 1975, Americans were once again allowed to officially own gold as an investment class.

MPT analyses require that data from all asset classes be available for the entire time period with the earliest date being designated as the starting point. Some might argue that 1975 should be the start date since the price of gold was artificially fixed before that. On the other hand, the political nature of gold pricing is a significant reality that should be considered in any analysis. As mentioned above, the performance of the other asset classes is better evaluated using as much data as possible. So, starting the analysis in 1928 has the advantage of including a much larger database from which to predict the performance of asset classes going forward from the present time. [An analysis of portfolios with and without gold using 1975 as the starting date can be found in the July 7 and July 8 articles written in 2009.]

The rise of gold
The figure below shows that one dollar invested in gold in January of 1928 would be worth just over $74 today. [A note on pricing: Gold prices used here are the London Price Fix, the most commonly used benchmark since 1919.]

As you can see, much of the activity has occurred since 1968 when the two-tiered market came into being. The first significant runup in the price preceded the lifting of the legal ownership ban and the second spike accompanied a period of high inflation in the US. Recently, gold has seen a resurgence as confidence in other financial assets has waned.

Current portfolio returns with and without gold
What does this all mean for your portfolio? Should you own gold, and if so, how much? The answers to these questions depend on your risk/return objectives.

Table 1 below shows the current MPT risk/return allocations among the nine traditional asset classes (excluding gold). The average annual compounded rate of returns currently achievable by the MPT model are listed in the first column. The second column shows the risk (standard deviation) which naturally increases along with the required return. Subsequent columns show the probability of portfolio loss in any one year and the Sharpe Ratio which is a measure of return versus risk with higher numbers being more desirable. This table and the next one were derived from the SMC Analyzer portfolio optimization software.

[To use this table, decide which return in Column 1 is right for you, according to your own appetite for risk and your investment horizon. Then, form your portfolio according to the allocations shown in that row. Note that all of these asset classes having corresponding mutual funds and/or etfs that mimic the underlying asset class; for example, the SPY–the S&P 500 etf– could be used as a proxy for Large Company Stocks.]

Now let’s see what effect adding gold as an asset class does to the above allocations. The right-most column in Table 2 below shows the gold allocations as calculated by MPT.

As you can see, MPT allocates gold into only the most conservative portfolios, and then only at modest percentages. Furthermore, the addition of gold provides virtually no risk reduction in those portfolios. This somewhat counterintuitive result supports the conclusion of economist Thomas Sowell who in his book, Basic Economics: A Citizens Guide to the Economy, noted that over time stocks and bonds have produced greater returns than gold.

A comparison of historical returns
The above chart shows that a portfolio constructed for a 6% return has the largest allocation of gold. Using this return objective, let’s compare how a portfolio with gold would have compared with a comparable one without gold. Running the MPT analysis from 1928 to the present, we see from the figure below that while both portfolios actually exceeded their targeted return, the no gold portfolio did so with lower risk (6.8% vs 7.3%).

Conclusion
There is no doubt that over the past few years gold has indeed been a spectacular investment but the data show that growth is sporadic. MPT portfolios are not about producing extraordinary returns in the short term; rather, they are designed to achieve a defined objective return at minimum risk over the long haul.

If you’re a die-hard gold bug who believes that the yellow metal has more room to run, then by all means go out and get yourself some. The moral of the story: Just don’t overdo it.

In a follow up article we’ll look at the effects of gold on a portfolio that uses an optimized market timing approach to MPT to see if we can achieve greater returns at lower risk.

Note: Pat Glenn contributed research and analysis to this article.

The Sharpe Ratio, Part III: Comparison of current portfolio allocations

This is the final installment of our discussion of the Sharpe Ratio. Today we’ll see some representative Sharpe Ratios of portfolios allocated according to Modern Portfolio Theory (MPT) using traditional asset classes, that is, stocks and bonds (as opposed to futures or derivative instruments). We’ll also see how adding the element of market timing to classic Modern Portfolio Theory greatly increases the Sharpe Ratio by dramatically reducing overall portfolio risk.

Current Optimally Allocated Portfolios and the Sharpe Ratio
Let’s look at some current (through September 2009) classic MPT allocated portfolios and their Sharpe Ratios. The table below lists the best allocations among a set of nine diversified asset classes. Target returns shown are for the range of possible achievable returns derived from monthly data since 1928. The data reflects compounded annualized returns.

This table was computed by the SMC Analyzer software available for user subscription on the StockMarketCookBook.com web site. As you can see, the Sharpe Ratios are highest for the lower risk portfolios  predominantly made up of Treasury Bills.  This is rather misleading because as T-Bills have historically averaged those returns, they’re not producing close to the same today. This is one reason to be cautious when evaluating the Sharpe Ratio as the contributing investment return term is backward looking, i.e. what the investment has produced in the past, while the riskless investment return is forward looking, i.e. what U.S. Treasury Bills will pay over the next few months. With this caveat in mind, one can still fairly compare the Sharpe Ratios in the above table since they are all calculated in the same way and over the same time frame.

Note that higher portfolio returns come at the cost of much higher risk (as defined by the standard deviation). By definition, an increase in risk lowers the value of the Sharpe Ratio revealing the declining compensation received for assuming the risks entailed with higher targeted returns.

Market timing improves Sharpe ratio values
Let’s now look at the table for MPT allocated portfolios using the conservative Long/T-Bills strategy enhancement to MPT. In this strategy, the percentage allocated to a particular asset class (equity asset classes only) is normally held Long when that asset class is in a bullish trend. When the trend reverses, the percentage of the portfolio allocated to that particular asset class is transferred into the safety of T-bills. (Trend reversals are determined by an oscillator that is optimized according to a robust and proprietary optimization method.)

The following table was produced using the SMC Analyzer software. In the table, Long/Long is equivalent to the classic MPT approach and is always applied to the bond asset classes.

The remarkable thing to notice is that it is possible to achieve a long-term rate of return of 10% with a Sharpe Ratio greater than 1.0. Compare that with the meager 0.44 Sharpe Ratio obtained with the classic MPT strategy!

Summary
The Sharpe Ratio can be a useful tool for financial decision making when properly understood and properly applied. As mentioned in Part I, the Sharpe Ratio is not a particularly reliable measure of non-traditional hedge fund comparison because of the non-Gaussian nature of the underlying instruments. What the investor has hopefully learned here is that when properly understood and applied, the Sharpe Ratio can be a legitimate tool for fund comparison but it shouldn’t be the only tool.

This is the technical portion of this article trilogy on the Sharpe Ratio so be sure you’re armed with high-water pants and a pocket-pen protector. A slide rule is a bonus.

A little background
Modern Portfolio Theory (MPT) is a Nobel Prize winning financial concept that utilizes mathematical optimization algorithms to determine the best allocation of investments in a portfolio. For a selected desired overall rate of return, MPT will tell you how to allocate your capital among a diversified set of investment asset classes such that the variation in the periodic portfolio total returns as measured by the standard deviation (aka risk) is minimized.

Follow any discussion on MPT and you’ll run into the following graph on the Efficient Frontier:

This technical plot shows the current relationship between desired return and standard deviation. What it shows is that to get a higher return, you’d need to assume more risk (given by a higher standard deviation). The curve known as the “efficient frontier” represents those optimally efficient portfolios that provide the least amount of risk at a given level of return. The shaded area underneath the curve represents the possible space of less efficient allocations of portfolio assets. To obtain a return in the area above the efficient frontier is theoretically impossible with the given asset classes.*

Note that the efficient frontier is a curved convex line. This is a result of the lack of correlation between the various asset classes in the portfolio and how that results in an optimum portfolio characterized by an overall standard deviation less than it would be for one of completely correlated assets. For uncorrelated assets when some are up in value others are down in value and the overall portfolio therefore exhibits reduced variation. For a portfolio of totally correlated assets a combined linearly weighted standard deviation would result and the efficient frontier curve would be straight rather than curved. It is the existence of optimum allocations that pulls the line to the left for a lower standard deviation at each point that creates that convex shaped curve. The end points of the curve do not benefit from this left pulling because they are dominated by single asset class portfolios, at the high end with 100% the riskiest asset class and at the low end by mostly the least risky asset class. The convexity of the efficient frontier curve therefore graphically demonstrates the beneficial effects of diversification.

How the Sharpe Ratio fits into MPT
Let’s look at the line of Best Capital Allocation on the graph above. This line represents the best way to incorporate a riskless cash component into an already optimum portfolio thereby reducing the risk even further albeit at a reduced overall rate of return. On the graph, the annual rate of return on a 90-day T-Bill is about 1.2% (those were the days!). This is considered as safe an investment for the short term as is possible to find and is considered to be as riskless as cash. The line on the graph therefore starts at 1.2% plotted at zero standard deviation (it’s riskless!) and is drawn so that it contacts the efficient frontier curve at a point such that the slope of the line is a maximum. This is the point on the efficient frontier with the greatest Sharpe Ratio and is known as the market portfolio. The slope of the line is the Sharpe Ratio for the market portfolio and the current riskless rate of return. As seen on the plot above, the market portfolio provides about a 5.0% return which has a standard deviation of 3.4%. The Sharpe Ratio for this investment is therefore:

(5.0 – 1.2) / 3.4 = 1.12

which, as we have noted, is also the slope of the line of best capital allocation. Note that the line of best capital allocation is straight because the riskless investment has no standard deviation and there is no better ‘optimum’ allocation that will produce a resultant combined reduced standard deviation that would produce a convex curve like that for the optimum allocations of the other investment classes.

The market portfolio has the unique property that, taken in combination with a riskless cash component, it will produce an optimal return not on the efficient frontier and outside the suboptimal allocation space. It will lie on the line of best capital allocation. The combination will offer a larger return for a given amount of risk than any of the portfolios on the efficient frontier. In this way you can easily construct a composite portfolio that is still optimum but also has a reduced standard deviation. Unfortunately, this comes at the penalty of a lower overall portfolio return. The percentage of cash to include in the composite portfolio is easy to determine since the standard deviation is reduced linearly according to the slope of the line (the Sharpe Ratio).

For example, a portfolio of assets at risk returning 5.0% with a standard deviation of 3.4% can be reduced to one with an arbitrarily selected standard deviation of 2.0%. From the line of best capital allocation on the graph, such a portfolio returns about 3.2% and is constructed by reducing the component of optimally allocated market portfolio risky assets to 64% (3.2 / 5.0) x 100% and by adding a 36% (the rest) riskless cash component. Portfolios on the line of best capital allocation above the market portfolio can only be achieved by adding leverage to the market portfolio financed by borrowing at the riskless rate of return.

Tomorrow’s focus
In the final segment, we’ll look at Sharpe Ratios for optimally allocated portfolios in the current market environment.

*A traditional MPT portfolio allocates funds among the following asset classes: Large Company Stocks, Small Company Stocks, Long-Term Corporate Bonds, Long-Term Gov’t Bonds, Medium-Term Gov’t Bonds, T-bills, REITs, International Stocks, and International Bonds.

Note that the efficient frontier curve will be different assuming a different set of asset classes.

I’ve been reading that investment professionals still use the Sharpe Ratio to evaluate fund performance which led me to ask the question of some of my peers as to what they consider to be a “good” Sharpe Ratio. The answers I received were unexpectedly all over the map; some considered anything over 1 to be good. Others thought that only double digit Sharpe Ratios were worth bothering with while still others said that the Sharpe Ratio had no impact on their investment decisions.

This left me even more confused and I decided to explore the Sharpe Ratio in detail so that I could make my own informed decision concerning its validity. To help me in my investigation, I called on Professor Pat as he’s the resident StockMarketCookBook expert on all things related to portfolio theory.

In only two days, he presented me with an exceptional treatise on the subject of the Sharpe Ratio. The only problem I had with it was the length which I felt was too long for one blog so I broke it up into three distinct parts which will be run over the next few days. What follows is essentially his writing with some of my own thoughts tossed in.

Definition of The Sharpe Ratio
The Sharpe Ratio is named after William Sharpe, a 1990 Economics Nobel Laureate who won it for his work on the Capital Asset Pricing Model (known as CAPM) which shows how the market prices individual securities in relation to their asset class. Here the discussion is focused on the Sharpe Ratio which for a particular investment is a direct quantitative measure of reward to risk.

Sharpe devised the ratio which he called the ‘reward to variability ratio’ in 1966. It later became known as the Sharpe ratio as other economists and financial professionals attributed the ratio to him. It is a measure of how much excess return an investment provides over and above a riskless investment (e.g., T-bills) considering the additional risk (σ, the standard deviation of the returns on the investment under evaluation) that is entailed. It is mathematically defined as the following:

Sharpe Ratio = [Investment Rate of Return – Risk-free rate of return] / σ

The Sharpe ratio is there used to assess how well an investment compensates an investor for assuming additional risk. Higher values of the Sharpe ratio are considered to be better than lower ones. The investment community generally considers values over 1.0 to be good, over 2.0 to be very good, and over 3.0 to be excellent, but these can vary depending on the current financial climate. [Note: As of this writing, the risk-free rate is almost zero which means that the Sharpe Ratio is essentially just the investment return rate divided by the investment risk.]

Riskless Investment Returns
The measure used in calculating the rate of return of a riskless investment is typically short-term (90-day or less) U.S. Treasury Bills. This investment is considered to be as safe an investment as you can possibly find, and will exhibit no variation in base value during the holding period. Only the rate of interest will change as the bonds mature and are rolled over.

Other measures of what is considered “risk-less” can produce significantly different Sharpe Ratios. Long-term government bonds are not an appropriate measure for a risk-less security as market interest rate changes can significantly alter their values and in some cases can drive down the total return to very small levels or even produce losses.

Hedge Funds and the Sharpe Ratio
Implicit in the Sharpe Ratio is the assumption that returns on the investment follow a normal distribution (i.e., the bell-shaped curve). While this is a very good assumption for equity based stock index funds that are large and liquid it is not so good for strategically managed hedge funds that employ dynamic trading techniques, illiquid investments, or highly leveraged instruments such as options. For example, a hedge fund strategy that sells deep out-of-the-money options will show a higher than average Sharpe ratio–that is, until the market unexpectedly moves counter to the prevailing trend and the fund is hit with large losses.

Because of the Sharpe Ratio’s mathematical nature, high values must either employ a high return and/or a low risk. As history has shown, high return/low risk situations can’t be sustained for long periods of time.

Here’s an actual example of a supposedly stable fund with a high Sharpe Ratio whose demise nearly destabilized the global financial markets.

In the 1990’s, Long Term Capital Management, the bond arbitrage hedge fund touted as mathematically safe due to the supposed low probability of incurring significant overall losses on its massive portfolio, showed a very high Sharpe Ratio of 4.35. The fund managers employed highly leveraged strategies to make money on small arbitrage spreads. A financial crisis in Russia caused by defaults on government bonds and a resultant flight to quality resulted in massive losses for the fund which controlled nearly 5% of the world’s fixed income market.

Unable to make the loan payments on the huge debt incurred to finance its leveraged positions, they faced collapse. Had they gone into default it would have caused a worldwide financial meltdown. They were bailed out by the Federal Reserve and other creditors and taken over in 1998. So, what else is new?

Hedge fund illiquidity also works to distort the Sharpe Ratio. Investments in real estate, private equity, or mortgage backed securities for which there is no ready market can appear to be less volatile which helps their Sharpe Ratios. Fund managers tend to price these securities in a way that is, of course, favorable to the fund’s statistics and can produce an artificially high value for the Sharpe Ratio.

[Note: Illiquidity relates to how assets are valued. When there is no ready market how do you determine what something is worth? The recent mortgage crisis is an example where fund managers were valuing their own assets arbitrarily. The rate of return on an investment is related to how its value fluctuates. If it does not fluctuate then the standard deviation is low and the Sharpe Ratio is high. If that lack of fluctuation is artificial then the Sharpe Ratio is also artificial.]

Summary
Many hedge funds that currently report a high Sharpe Ratio may be employing strategies that are manageable at their current size but as they grow those same techniques may prove impractical and the Sharpe Ratio could drop dramatically. As a result, the Sharpe Ratio may not always be a good measure for evaluating a hedge fund’s risk/reward relationship.

To get a better picture of a fund’s risk/reward profile, investment professionals look at other aspects of performance including maximum portfolio draw-down and statistical measurements such as kurtosis and skewness. The Sortino Ratio, a modification of the Sharpe Ratio, focuses on downside volatility. Some think that it’s a more accurate representation of hedge fund risk but it, too, is subject to many of the same criticisms as the Sharpe Ratio. As one professor of risk management so aptly said, “Risk is one word, but it is not one number.” [Ref. 1]

Tomorrow we’ll look at the Sharpe Ratio in the context of Modern Portfolio Theory, so dust off your propeller hats!

References
1. “The Sharpe Ratio can oversimplify risk,” Investopedia.com.
2. “Risk gets riskier,” Hal Lux, Institutional Investor, October 2002.

Image by Free-StockPhotos.com

I’ve been receiving numerous inquiries into the Stock Market Cook Book’s portfolio analytical tool, the SMC Analyzer, which is a powerful software tool that applies proven market timing strategies to Modern Portfolio Theory (MPT). Although Analyzer results have been discussed in previous blogs (see 5/12-13/09, 7/07/09), I feel that some of the points made earlier need to be reiterated.

One of the most frequently asked questions is this:  How did a portfolio based on market timing perform during the recent credit crisis as compared with a traditionally long-only MPT portfolio? Although I’ve already answered this question in the May articles, I decided to update the results to this July and take an in-depth look at how both portfolios would have been allocated on a month-by-month basis beginning in July 2008.

Modern Portfolio Theory (MPT) in a nutshell
Briefly, MPT tries to maximize returns given a certain level of risk. It attempts this by allocating a portion of one’s portfolio over a spectrum of asset classes, each with varying degrees of inter-correlation. For example, an MPT-allocated portfolio would most likely contain the asset classes from both large and small-cap domestic (US) stocks, US government and corporate bonds, real-estate (typically in the form of REITs), and international stocks and bonds. Commodities such as precious metals and oil are also popular asset additions. (Hedge funds can also play a role in portfolio diversification.)

MPT attempts to achieve the desired return via the robustness (that is, the degree of return versus the associated risk) of a particular asset class combined with the degree of correlation (or rather, un-correlation) among the other candidate asset classes. It’s a complex mathematical model for which Harry Markowitz won the Nobel Prize. So far, the theory works well over a very long time-frame, but it does dismally over short, volatile periods because of the requirement that it always must be long in the asset classes determined by the model.

Not so for the SMC Analyzer!

How the SMC Analyzer is different
The SMC Analyzer uses a market timing oscillator that is specifically optimized to each asset class. When the oscillator is positive, it will tell you to be long that asset class; when it turns negative, you’ll be instructed to either go short (if that is what you selected and if that asset class is “shortable”) or else move into the safety of T-bills or another risk-free asset.

So, how did both of these approaches compare over the past year?

Portfolio comparison
Below is a chart that graphically depicts how a traditionally MPT-allocated portfolio would have fared compared with a market-timed portfolio as determined by the SMC Analyzer. In the latter portfolio, it was assumed that when the oscillator for that asset class was negative, those funds that would have been designated by MPT for that asset class would be put into the safety of Treasury bills. In both portfolios, a compounded annual return of 10% is assumed.

You can see that the tradition portfolio failed miserably—not only could it not yield a positive return but the investor would have been down 26% and at a huge risk of nearly 27%. Note, too, that the maximum draw-down was a whopping 45%, on par with the major averages.

However, had you been in a market-timing adjusted portfolio, not only would you have realized a positive return, but also at greatly reduced risk and without any significant draw-down. This is truly amazing!

To see how these differences in results were achieved, let’s look at the asset allocations recommended by MPT for each portfolio on a month-per-month basis. The top table gives the recommended allocations for the traditional MPT portfolio while the bottom portfolio gives the recommended allocations for the market timing portfolio.

Observations
Here are several observations derived from the above tables:

1. The risk, as measured by the standard deviation of possible returns, is less than half that in the market timing model.

2. The reason that MPT failed so miserably during the recent downturn is because it is looking towards the longer term and it figures that the only way the portfolio will ever be able to achieve a 10% return is by being long in the highest risk (as well as the highest returning) asset classes, i.e., stocks.

3. In contrast, the SMC Analyzer saw that the market was heading down and re-allocated the monies apportioned to Small-Cap Stocks into the safety of Medium Term Government Bonds and T-bills. It wasn’t until March of 2009 that the stock oscillators began to turn positive indicating it was safe again to invest in those asset classes.

4. You can also see that in order to achieve the monthly asset allocations as recommended by the market timing model involves significantly less trades, a plus for those investors who are worried about commission costs eating into their portfolio profits.

Summary
I hope this exercise will put to rest many of the questions that have been raised about the SMC Analyzer’s market timing model. For further information regarding the SMC Analyzer, take the features tour or test drive the Analyzer for yourself. Both are located in the left-hand column of the home page, www.stockmarketcookbook.com.