‘Value investing’ seems like a fairly straightforward concept, but many investors have realized that there are nuances to value investing—especially in light of large losses in some value-oriented strategies in 2008-2009. The idea seems simple: buy stocks that are relatively low-priced, as measured by dividend yield, price-to-earnings ratio, or price-to-book. There are studies going back decades that have noted that buying stocks at low P/E ratios tends to yield have average total returns than buying stocks at higher P/E ratios (see Malkiel’s A Random Walk Down Wall Street, for example). Fama and French identify ‘value’ as a statistically robust predictor of higher returns. Rob Arnott has further popularized this idea with ‘fundamental indexing.’

One challenge for value investors is that any given statistical metric of value can mean different things. Dividend yield, in particular, is a problematic measure. The dividend yield can be high because a company is solid and throws off robust earnings that are returned to shareholders, but dividend yield can also be high for a company in distress that has seen its stock price plummet but that has not yet seen its dividends reduced to reflect its current status. There are other challenges, too. Some companies employ ‘leveraged’ dividend strategies in which they will pay out more in dividends than their actual earnings. This surely cannot be compared to companies that only pay shareholders a fraction of real earnings.

Back in November 2008, I wrote an article about what appeared to be a substantial disconnect in the way that options of individual stocks were being priced. This was due to the very high level of fear in the market. This anomaly was most easily exploited by selling options. In June of 2009, I wrote an article looking at how well this strategy had worked out. In this article, I look at the very different options opportunities that exist today. The anomalies that existed in late 2008 have largely disappeared, but there are a range of other opportunities that look substantial. In describing the market opportunities in options, this article introduces some more sophisticated concepts in options valuation and trading.

Today, VIX stands at around 28. The implied volatility of long-dated options on SPY (December 2010 and December 2011) is 27%-28%. VIX measures the implied volatility of options expiring soon. What this means is that the options market is suggesting that volatility is stabilizing at a value of around 27%-28%. This level of volatility is almost twice the long-term average for the S&P 500 (which is around 15%) but is far lower than levels of late 2008.

The prices at which options on various stocks and ETFs trade provide a great deal of insight. Back in November of 2008, I noted that options on a range of defensive stocks were highly over-priced relative to the price of options on the S&P 500. In early 2007, I noted that options prices were signaling a massive increase in volatility in major asset classes. The prices of options have provided remarkably consistent and reliable signals through the very volatility market conditions of 2008-2009.

The key variable in options prices is implied volatility. The price of an option is determined by the following variables:

Monte Carlo portfolio planning tools allow investors to account for the effects of volatility on their long-term plans. These models simulate many possible future outcomes and calculate the probability that an investor’s portfolio will be able to provide a long-term income stream or that it will meet some other goal. These models must generate statistical projections for the future risks and returns of all assets in a portfolio, as well as accounting for the relationships between asset classes.

In light of recent years—especially 2008—there has been growing misconception that these tools were inadequate to show investors the potential for substantial losses and thus are of limited use in long-term planning. This may be true for some Monte Carlo models, but certainly not all. In this article, I demonstrate a straightforward way to stress test these models, using Quantext Portfolio Planner (QPP) as an example. The technique that I will present is related to Moshe Milevsky’s SORDEX ratio (pdf). The basic idea behind stress testing is to see how some extreme but possible event will impact long-term plans. Milevsky proposes the following:

The Wall Street Journal ran an article on May 2, 2009 called “Odds-On Imperfection: Monte Carlo Simulation.” The sub-title is “Financial Planning Tool Fails to Gauge Extreme Events.” The main point of the article is that Monte Carlo Simulations did not predict the potential for a market meltdown on the scale of what we experienced in 2008. This article reinforces some common misconceptions about Monte Carlo planning tools and probabilistic models in general. As the author of a Monte Carlo planning package, I got quite a few questions about this article.

The main premise of the WSJ piece is that “there is little chance your Monte Carlo simulation, named for the gambling mecca, would have highlighted a scenario like the market slide just seen. Though these tools typically run a portfolio through hundreds or thousands of potential market scenarios, they often assign minuscule odds to extreme market events.” The author then frames this point as a general critique of the use of probabilistic portfolio management tools like Monte Carlo Simulation.

In an excellent article, Don Dion recently described a series of reasons why emerging markets have not provided investors with shelter in the current bear market. He dissects the now-dismissed notion that was popular in the early to mid-2000s that emerging markets were ‘de-coupling’ from the U.S. and thus would not be too sensitive to a meltdown in the U.S. and other developed markets. In a nice turn of phrase, he suggests that rather than being de-coupled, emerging markets have become ‘unhinged.’

The lack of de-coupling is not the whole story. Not only are the correlations between emerging markets and developed markets not low, but the Betas between emerging market funds and the developed markets tend to be high. This means that emerging markets will often amplify the swings in the U.S. I have been surprised that nobody is pointing out that even as many pundits were promoting ‘de-coupling,’ the statistics never supported this idea. Back in May 2006, I wrote an article that demonstrated these effects. The table below (from the original article) tells the story:

There are some solid arguments that dividends will represent the bulk of returns from stocks for a number of years into the future. Regardless of whether this is correct or not, there is a broad consensus that capital appreciation will be sufficiently low that we will see, on average, 8% annual returns per year from domestic equities. This is generally in line with estimates from a range of sources, albeit on the conservative end. These factors suggest that even investors who are not specifically focusing on income may do well to spend some time looking closely at dividend yields. That said, the dismal performance of dividend focused index funds like DVY over the last couple of years makes it clear that it is unwise to blindly invest on the basis of yield.

For the two years through March of 2007, DVY had average annual returns of -35.8% per year. The S&P 500 (IVV) had an average annual return of -25.7% per year, with about the same level of volatility. The Dividend Aristocrats, an index of stocks with a consistent increase in dividends over the trailing 25 years, out-performed the S&P 500 by a margin of more than 15% in 2008. That still makes for a bad year for the Aristocrats, of course, but this level of out-performance sure tempered the worst beat market in 70 years. Now, here’s the interesting part: the out-performance of the Dividend Aristocrats was predicted two years ago on the basis of portfolio theory.

One of the notable features of 2008 was the ‘volatility shock’—the massive and rapid rise in market volatility. It is also notable that the high probability of a volatility shock was clearly signaled by the options markets and by a range of experts well ahead of time. Volatility had been running way below the long-term average for equities as well as being at about half of the long-term implied volatility on equities for several years. I wrote about this issue quite a bit in 2007 and I was struck by the fact that the risk of a volatility shock was not discussed more until it happened. VIX, which measures implied volatility on S&P 500 options near expiration, skyrocketed to historical highs in 2008. Volatility on all asset classes tends to be correlated, and we saw massive rises in volatility on essentially all asset classes. The coupling in volatility across asset classes was another concept that got little attention leading up to 2008, but it is a consistent feature of financial markets.

Increases in volatility are costly to investors not just because they increase the “bumpiness” of returns, but because there is a negative correlation between returns on many asset classes and VIX. When VIX goes up, returns tend to go down (see this article). I documented this effect in August of 2007 and the statistics were striking. The returns on the S&P 500 and the returns on VIX (percentage changes in VIX) were correlated at -67%. The returns on EFA and EEM were correlated to VIX at -43% and -52% respectively. These are high negative correlations and show the danger signal of a rise in VIX. Further, these high negative correlations are seen in a wide range of asset classes. The negative correlation between VIX and returns reflect increasing investor risk aversion. There is also a strong positive correlation between corporate default risk and implied volatility—something I wrote about in early 2008—so the potential for a substantial rise in volatility also leads to the potential for a big increase in corporate defaults, as we have now seen.

We have just experienced the most bruising capital markets environment since the 1930s—and it may not be over yet. Time will tell. There are pundits who believe that the market is still over-valued and there are those who believe it is under-valued—with everyone struggling to be heard above the noise. The sheer scale of the meltdown is dramatic, but equities are still not at the lowest levels that they have hit in previous calamitous periods. On the basis of some fundamental measures such as trailing ten-year P/E ratios or Tobin’s Q, equities are cheap but could get quite a bit cheaper before they match the fundamentals of previous bottoms. Equities are cheaper than they have been in a long time, many experts note, but they could go lower if past crises are any indication. Well, perhaps this is not exactly a stunning insight. There are plenty of negative voices out there, but it is worth being a skeptic. Nouriel Roubini, the so-called ‘prophet of doom’ who is predicting further market declines, has all of his 401(k) assets invested in equities.

The aggregate risk tolerance of the market has shifted, from an attitude that risk doesn’t really matter as a consideration to one in which investors are very risk averse. It was not so long ago now that retail investors were pouring money into emerging markets, heedless of the historical risk levels associated with this asset class. By their actions, investors were betting that the high risks associated with a range of foreign countries had been conquered—but this idea was not well supported. Today, investors seem convinced that the wheels are—or have a meaningful probability—of coming off the system. As investors sell off their equity holdings, they have invested heavily in bonds. The flight to government bonds, the safest of all asset classes, has created what many see as a bubble in bonds.

One of the most striking features of 2008 was the fact that correlations between most asset classes went up substantially: everything declined at the same time. One of the principal motivations behind diversifying is that all of your holdings will not decline at the same time. Declines in one class will be buffered by gains in another—or at least lesser losses in others. This effect has not provided much buffer in 2008.

The table below shows the trailing three-year correlations in monthly total returns between a range of ETFs (I have used ^DJC as a proxy for DJP because of DJP’s short history).

Ben Stein recently expressed a feeling that many people share with regard to 2007-2008: how could equities lose so much value so fast^[1]? Ben puts it this way:

“…we have learned that even the most rigorous back testing of portfolios did not work during this period. The reason was simple -- no back test allowed for as much stress as markets were under from late 2007 to fall 2008. There simply was no postwar historic precedent for markets to be as volatile on the downside as they were in 2007-08. Thus, back testing (very similar to stress testing) that called for maximum falls of, say, 33 percent simply did not work when markets fell as far and fast as they did in 2007-08.”

Jeremy Grantham’s asset class projections are worth paying attention to. Grantham has demonstrated a fairly remarkable ability to predict returns on major asset classes. The Economist published an article in August of 2008 that shows just how good Grantham’s asset class outlooks have been for the last ten years. Also to his credit, he specifically told investors to be in cash in January of 2008.

Grantham’s outlook for the next seven years (from October 31, 2008) looks good for major equity classes. For large-cap equities, Grantham is calling for annualized return of 8.4%. He projects that “high quality” domestic stocks will return 12.9% per year (annualized). His projection for U.S. government bonds is 3.8% per year. Grantham also provides projections for the additional value that active management can add, but we will ignore this component for the time being. [Note: Grantham’s projections in the link below are in terms of “real return,” which is return minus inflation. You can get the actual returns by adding 2.5% to the “real returns” because Grantham assumes that the long-term rate of inflation is 2.5%.]

There are many experts who believe that investors ought to invest in funds that track a broad index [pdf file], and should not invest in smaller numbers of individual stocks. This idea has always rather bothered me because the arguments supporting it seem to be based on some simplistic assumptions. Is it really more intelligent to hold all 500 stocks in the S&P 500 than to hold a smaller number of stocks?

The arguments against holding individual stocks seem to hinge on the idea that investors have only two choices. They will either (1) buy an index fund or (2) randomly buy some number of individual stocks. This approach assumes that investors have no information at all to help them in distinguishing between stocks. The studies that support this thinking typically show that the returns of an index are disproportionately determined by a small number of big winners and that if you are picking stocks by chance, you have low odds of picking any of these rare but enormous winners if you pick a small number of stocks. Similarly, a random stock picker has reasonable odds of ending up with a portfolio that contains a higher exposure to individual stocks that suffer substantial losses, and thereby to suffer much higher losses than the index.

At the start of November of 2007, I published an analysis in which I provided outlooks for a series of sector ETFs . The projections were generated using Quantext Portfolio Planner (QPP). That analysis suggested that most major asset classes were due for a substantial reversion to the mean—downwards. In particular, the worst looking asset classes were emerging markets (EEM), developed international markets (EFA), and telecom (IXP).

One of the strongest results from that analysis was a back-of-the-envelope calculation that suggested that most major asset classes would need to under-perform TIPS (TIP) for the next three years in order to bring long-term returns into equilibrium. The asset classes that were projected to under-perform TIPS in this approach included the S&P500 (both market cap weighted (IVV) and equal weighted RSP, EFA, EEM, IDU (utilities), RWR (REITs), and a number of other major sectors. Given that TIPS have a much lower risk level than any of these broad asset classes/sectors, an investor might logically have chosen TIPS in preference to any of these asset classes.

In October and November of 2008, we have seen the equity and bond markets descend into a state of extreme risk aversion. Investors need look no further than the VIX index to see this. VIX is often referred to as the “fear index” because it goes up when investors drive the prices of options upwards in an attempt to buy some protection for their portfolios (pdf). VIX has been closing in the 70 to 80 range for weeks—albeit with periods of lower volatility in between. VIX actually measures the implied volatility of options on the S&P 500 index that are very close to expiration—think near term---30 days or so. While many investors are focusing on the short-term swings, there is a great deal of information available by looking at longer time horizons (i.e. longer dated options). There are also substantial opportunities to manage portfolio risk for the investor willing to pay attention to the levels of implied market risk in options.

After analyzing the broad market conditions, I find that selling covered call options into this market looks attractive—but not selling covered calls on just anything. As investors have become highly risk averse, they have driven the prices of options so high that even some very stable stocks have ended up with options prices that seem excessively high. Investors can sell covered calls on these stocks, thereby realizing a considerable portion of the upside potential and providing a cushion against potential declines in price.

It was not very long ago that investors had become incredibly complacent about market risk. Over recent years, I have written a number of articles that suggested that market volatility was in temporary decline, but that volatility was likely to return.  The writing was on the wall for a substantial increase in volatility. This has obviously occurred, and market volatility as measured by VIX is at extremely high levels.

The greatest danger in low volatility environments is that investors tend to become increasingly aggressive because nothing bad has happened for a while. This factor was a substantial component in the surge in investment in emerging markets, even though these markets have historically been incredibly risky. Too many investors assumed that emerging market stocks would remain docile and well-behaved, despite considerable historical evidence of high risks in these markets. 

Risk management is a concept that surprisingly few investors spend time to understand. This is unfortunate. The paradigm of effective investing rests upon the idea that investors look at the expected returns from an investment compared to the potential risks. Investors try to get as much expected return as they can for bearing a certain level of risk. Along with this, investors must determine how much risk they can or should take on. How can investors rationally weigh potential gains against risks if they have no way to estimate risks? Most investors have some way that they estimate the potential returns—whether it is fundamental analysis, analyst price targets, etc. It is the rare investor who has any way to consistently estimate risk, however—either in individual holdings or in the total portfolio. This situation is somewhat absurd. 

It has been twelve years since a group of influential economists (including Nobel Laureate Bill Sharpe) lobbied (unsuccessfully) for the need for improved risk information for investors in mutual funds. Needless to say, the push for understanding risk gets muted when the market is going up—but it is my hope that this most recent market downturn will have the silver lining of focusing attention on the need for improved risk management for all investors—and not the just the large institutions that are the predominant users today.

Quantitative Asset Allocation

Many investors seem to be in the process of losing faith in asset allocation. In September and October of 2008, it seems that all asset classes have moved together—straight down. The positive benefits of asset allocation rely upon certain asset classes having low correlations with one another—when one dives, others don’t. While correlations tend to go up during fairly short periods of panic selling in crashes, the value of managing a portfolio using asset allocation has been consistently demonstrated. In this article, we present a useful data point in this regard.

Combining Strategic and Tactical Asset Allocation

In a recent article, I discussed Tactical Asset Allocation [TAA] and its relationship to Strategic Asset Allocation [SAA]. To take advantage of both TAA and SAA, it is necessary to build a highly diversified portfolio that has a projected future return that is greater than the returns that the portfolio has generated in recent years (i.e. the assets in the portfolio have generated less-than-expected returns in recent years). The recent under-performance means that the portfolio is due for a reversion to the mean and provides the potential for a performance boost beyond long-term expected returns (the TAA component). The core Strategic Asset Allocation ensures that the portfolio derives the maximum available diversification benefit and has a total volatility that is consistent with the needs of the investor(s).

The term “market timing” means different things to different people.  Several basic truths are unassailable.  First, most investors hurt their performance by chasing hot asset classes.  Second, there is evidence for momentum effects in asset class performance—which suggests that it might be possible to time the market successfully.  Third, the price you pay for something matters.  In the institutional world, market timing is typically referred to by a more respectable name: Tactical Asset Allocation [TAA].

TAA means that a portfolio manager considers the current valuation of an asset when considering its potential impact on the total portfolio.  In other words, I am not talking about momentum investing or any sort of high-frequency buying and selling.  TAA is a form of market timing, but implies something considerably more nuanced than most investors’ understanding of that term.