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On the back of a market rally and increased investor interest in minimum volatility strategies, I have been getting a lot of questions on whether those strategies now appear expensive compared with a standard capitalization-weighted portfolio. In short, clients are asking if minimum volatility has been “overbought” and is not likely to be effective going forward. My response is that while at first glance the answer would seem to be “yes,” a deeper analysis shows the answer is actually “no.” But more importantly than that, it’s important for clients to realize that minimum volatility should not be thought of as a “market timing” strategy. Let me explain.
As of May 28th the valuation  for the MSCI USA Minimum Volatility Index was 8.62x while the MSCI US Index – the comparable cap-weighted index – was 8.16. Based on this metric alone, investors may conclude that exposure to the minimum volatility strategy is “expensive” compared with a typical capitalization-weighted investment and thus potentially not attractive.
This is, however, not the whole story. Valuations will vary across investments when the underlying fundamentals justify reasonable valuation differences. In other words, differences in valuation could be justified by differences in fundamentals of the underlying companies, such as profitability or balance sheet strength. These distinctions are particularly important at the aggregate level where diversified strategies or baskets will “wash out” individual company-level differences and will tend to reflect broad fundamental characteristics of the underlying investments.
In the case of minimum volatility, profitability is one of those differences. To illustrate, the chart below shows the profitability (measured as Return-on-Assets) as well as the valuations (measured as Price-to-EBITDA) across a range of developed countries  as of the end of April. Close to 75% of the variation in valuation can be explained just by looking at the aggregate profitability level, with every percentage point in additional profitability explaining about 2 points worth of additional valuation. The US cap-weighted benchmark is on the “expensive” side with a valuation ration of 8.16x but this is partly explained by profitability levels higher than that of the average developed country.
The chart also shows that the minimum volatility index is indeed more expensive than the benchmark, but the aggregate profitability of the portfolio of companies in the minimum volatility index is also higher than the benchmark. Indeed, the minimum volatility exposure seems better valued in the sense that its higher profitability would justify higher valuations given the observed relationship between valuations and profitability across developed countries.
The bottom line is that assessing whether or not a particular exposure or strategy is attractive should include consideration of the many factors that drive valuations in the first place. Most importantly, however, investors should keep in mind that the case for including a minimum volatility strategy in a portfolio is not based on whether its valuation is attractive or not at any given moment. Instead, the key idea behind these strategies is to reflect market behavior that is additive to a portfolio and diversifying to a “traditional” investment style.
I have argued on this blog that behavioral biases as well as institutional constraints are a key driver of the “low beta effect” that is behind the better return per unit of risk that minimum volatility strategies have shown in the past. In fact, the MSCI USA Minimum Volatility Index has been more “expensive” than the standard benchmark every month since the end of 2011, but it has nevertheless delivered better returns per unit of risk as compared to the standard cap-weighted benchmark .
 Valuation ratios as computed as Price divided by EBITDA. Index-level valuation data is from Bloomberg for the standard MSCI country indices. EBITDA is a standard choice for cross-sectional analysis as it is generally less prone to large observations as simple bottom-line earnings.
 The chart shows all developed countries where their standard MSCI index contains 20 or more names. Index-level data for ROA is from Bloomberg as of May 28. ROA at the index level is computed aggregating Earnings and Assets separately and then dividing the sums.
 Data is from Bloomberg. Annualized returns from monthly data for the MSCI US index since end-December 2011 until end-April 2013 are 16.3% with annualized risk of 9.9% for a Sharpe ratio of 1.64. The equivalent numbers for the Minimum Volatility index are 17% annualized returns, 7.6% annualized risk for a Sharpe Ratio of 2.23.
Economists’ academic work and late-night comedy rarely go together, but a few nights ago Stephen Colbert capped a string of very lively media articles and blogs with a lengthy, comical segment on the Reinhart-Rogoff affair.
How did an academic economic research paper become the subject of late-night comedy? Back in 2010 Harvard economists Kenneth Rogoff and Carmen Reinhart (hereafter “RR”) published a paper on the relationship between public debt and economic growth. The paper quickly became very influential in policy-making circles, concluding that there seemed to be a correlation between countries with public debt levels above 90% of GDP and significant decreases in GDP growth. But last month economists Herndon, Ash and Pollin (“HAP”) uncovered an Excel spreadsheet error in RR’s original analysis that unleashed a firestorm of criticism of the paper’s original conclusions.
Given that RR’s work has been one of the most quoted pieces of economic analysis in recent memory, the controversy is attracting unusual media and political attention. The paper has played a central role in the ongoing debate around austerity and the best way to conduct fiscal policy in a persistently low-growth environment.
Instead of dissecting RR’s original work, I’d like to highlight three lessons for investors watching the debate and asking how it may influence their investment decisions.
1. Beware of simple rules of thumb
One of the key attractions of RR’s original analysis was the apparent simplicity of the results, including the 90% “threshold” of debt-to-GDP as a way of evaluating a country’s economic health and future prospects.
It is of note that the 90% threshold was an estimate subject to both statistical error as well a wide range of possible interpretations.  But it has not been uncommon over the last three years to see this “rule of thumb” applied by many investors as a simple way of accounting for public debt in their investment decision-making process. A cursory look at the data shows that whatever the general relationship may be between public debt and economic growth, the specifics in each case can be quite different from the average.
To illustrate, consider the case of Germany, the United States and Spain shown in the charts below . Spain entered the financial crisis period with the lowest public debt burden of the three countries, but it has also suffered the worst growth decline and equity market performance of this small group. In contrast, the United States has showed the best performance in terms of equity-market returns and has experienced economic growth close to that of Germany. This is notwithstanding of the fact that it has the highest public debt burden of the three countries.
Many investors find it surprising that Spain’s public debt levels are lower than those of Germany and the United States. The association between poor economic performance and public debt levels has become a strong part of how investors think about markets — a testament to the popularity of the work that RR and others have published in the last few years.
2. Correlation is not the same as causation
For all the hoopla surrounding the Excel error, the most interesting part of the debate is not whether high public debt is associated with low economic growth (the preponderance of the evidence suggests that it is), but which causes which. Indeed, recent work by a number of academics suggests that the observed relationship may well be partly explained by low-growth countries accumulating public debt as opposed high public debt causing low growth.
In particular, if some of the relationship documented by RR arises from low-growth causing public debt, then the timing of debt-reduction programs may have significant impact on a country’s economic performance. This is because the benefits from public debt reduction may well be made up by the short-term impact of reduced government expenditure. Investors looking at the nature of debt-reduction programs when assessing their allocation to international markets should take care to note both the timing as well as the size of debt-reduction policy. We have cautioned in our own work that while long-term debt reduction should be on the menu for most developed countries, too much austerity too quickly can be counterproductive.
3. Don’t judge the book by its cover
Whatever the final outcome might be of this controversy, investors would be remiss in writing off RR’s larger body of work. Their 2009 book on the history of economic crises made a significant contribution to economists’ understanding of the nature of economic cycles — especially the differences in the shape and speed of economic recovery after downturns that are linked to a banking crisis. RR provided unprecedented depth and empirical support for the idea that banking crises are followed by very slow recoveries, a prediction that has proven all too accurate since the financial crisis.
A key finding of that body of work is that the nature of a banking crisis induces slow recovery for a wide range of reasons, including the need to repair balance sheets across private markets and individuals as well as regulatory, institutional and political changes that tend to follow banking crises.
The upshot is that investors should still be prepared for what is likely to be a continuation of the slow growth environment that we have seen over the the last five years. High public debt levels may or may not make the problem worse at the 90% GDP threshold. But either way a range of other post-banking-crisis issues remain, including only a very gradual working out of private debt overhangs, regulatory changes and higher-than-normal unemployment.
 See, for example, “Debt and Delusion” by Robert Shiller at the Project Syndicate, July 21 2011.
 The Debt-to-GDP data is from the IMF public debt database. Returns data are from Bloomberg and are based on MSCI local currency indices for each country.
With the Dow recently closing at a record high and exchange traded products (ETPs) kicking off 2013 with their strongest January-February inflows on record, there has been a lot of buzz about new cash coming into the market. If investors are finally deploying some of that cash that had been sitting on the sidelines, it raises an interesting question: What is the “right” amount of cash to hold in a portfolio? And is this a good time to take cash holdings and put them back into the market?
To answer these questions, I took a look at investor behavior around cash holdings during the last few economic cycles. In particular, I have taken data on individual investor cash holdings from the survey that the American Association of Individual Investors has been conducting since November 1987 (right after the stock market crash of 1987). Individual investors are asked to report on their allocation to equities, fixed income and cash during the month the survey is conducted. I’ve plotted that data on the chart below.
On the left axis, you’ll see respondents’ average allocation to cash as percent of the total individual investor portfolio. On the right axis is the cumulative return of the non-cash portion of the portfolio (shown as the value of a dollar invested at the beginning of the period).
It is interesting to see how the peak levels of cash holdings seem to correspond to the bottoms of market cycles, and the lowest levels of cash holdings seem to correspond with the top of market cycles. In other words, when stock prices were low, investors were holding on to a lot of cash. As stock prices rose, investors were putting their money back into the market. Indeed, cash holdings were high right after the 1987 crash and steadily declined as the market rallied in the 1990s. They bottomed out almost exactly when the tech bubble burst in early 2000.
We then saw investors pull back their investments and go back into cash as the market declined. This means investors ended up with peak cash holdings when the rally of the mid-2000s started around the middle of 2003. The cycle repeated itself into the peak of the market before the last crisis, with investors showing the highest level of cash holdings as the market bottomed in the middle of 2009.
This appears to be one more manifestation of trend-chasing often exhibited by investors. Indeed, the chart below shows that on months when the survey reports a reduction in cash holdings, the non-cash portion of the portfolio had, on average, much higher returns in the preceding period than in months when the survey reports increases in cash holdings1. In other words, when returns have been good investors reduce their cash holdings and invest more in risky assets, and vice-versa when returns have been poor.
How effective is this cash-timing behavior that investors seem to follow? Unfortunately, it has not shown to be very effective. The chart below shows that returns, on average, are about the same in the month after cash holdings are reduced compared with months when cash holdings are increased. Even more discouraging, average returns in the year after cash holdings are decreased are actually smaller than when cash holdings are increased.
So, what is the right level of cash holdings for an investor? That of course depends on each individual investor. But the charts above illustrate that many investors could benefit from setting cash holdings at levels to meet expected near-term expenditure needs, such as covering daily expenses or paying regular bills like mortgage or car payments. In other words, keep cash stable at levels that match expected near-term expenditures and then focus on managing the rest of the portfolio to fit longer-term investment objectives and risk levels rather than using cash as a market or risk timing tool. This particularly applies to investors with long-term investment horizons where the probability that a typical strategic portfolio (such as the traditional 60% stock/40% bond portfolio) will underperform cash over longer periods is small.
To illustrate this last point I have constructed a hypothetical 60/40 portfolio with data going back to 1925, and I’ve compared its returns over various horizons with the return of cash investments over time. The chart below shows the probability that over various horizons (from 3 months to 10 years) the return of cash will be higher than the 60/40 portfolio.
Across all the 10-year periods from 1925 to 2012, roughly only 1 in 10 showed cash with better returns than the 60/40 portfolio. Even for horizons as short as 1 year, only 1 in 4 of all 1-year periods since 1925 showed cash with better returns than the 60/40 portfolio. These statistics are, of course, no guarantee than the next year or next 10 years may not be one of those periods. But for most investors, time spent making sure their overall portfolio matches their risk tolerance and investment objectives will likely be more productive than time spent managing their cash holdings.
 The return of the non-cash portion of the portfolio is, for every month, the weighted average of the total return of the S&P 500 and the total return of 50/50 mix of intermediated Treasury bonds and long-term corporate bonds. The weights are given by the relative weights to equity and fixed income reported by the survey for that month. Data for the S&P 500 returns as well as government and corporate bond returns is from the Morningstar SBBI database.
 The 60/40 portfolio is constructed as the 60/40 weighted average of equity and fixed income monthly total returns. For equities I used equity returns as reported by the Morningstar SBBI database. For fixed income I use a 50/50 mix of the returns for intermediate government bonds and long-term corporate bonds, also from the Morningstar SBBI database. For cash returns I use 1-month Treasury bill returns as reported by the Morningstar SBBI database.
It’s 23 days into 2013. So, how well are you sticking to that New Year’s resolution to go to the gym?
It’s no secret that one of the most common resolutions is to exercise more. Gyms regularly take advantage of this trend with membership discounts and promotions at the start of every year. But we’re all very familiar with stories on just how short-lived these resolutions can be, with the Marist annual poll showing over 40% of Americans answering “no” to having kept to their 2012 resolution even part of the year.
Why, then, do we pay for monthly gym memberships when many of us know that it is unlikely that we will keep our resolutions? Part of the problem, according to research by psychologists and behavioral economists, is overconfidence in our ability to resist temptations of all kinds, including the temptation to slack off and skip the gym. If I tell myself that I will actually go the gym once a week all year, then a membership is very appealing. I have assigned a higher weight to my own assessment of my ability to keep to my plans than the evidence from my own past and the well-publicized evidence that a large proportion of people fail to keep to their gym-attendance plans. In short, I’m overconfident in my ability to keep hitting the gym all year long.
As is the case with a wide range of behavioral research, these findings can be applied to finance and asset allocation. One of the most interesting applications is the idea that overconfidence may partly explain “home bias.” This is the tendency that many investors have to allocate larger proportions of their portfolio toward securities in their home country, forgoing the benefits of a well-diversified global portfolio. In this case, an investor’s perceived familiarity with “local” companies or investments leads to over-confidence in one’s ability to invest in those companies compared with unfamiliar foreign companies. This leads to a willingness by investors to ignore the diversification benefits of a global portfolio because it may be perceived as being composed of unfamiliar or unknown companies and investments.
This year, instead of a going-to-the-gym resolution, consider a resolution to increase the diversification in your portfolio. If you do hold a disproportionate share of your portfolio in local assets, consider diversifying your portfolio into international markets that may help to offset the slow-growth scenario we are likely to see in the United States in 2013. These include emerging markets with higher economic growth prospects and lower correlation to the U.S. market, and where valuations are still attractive as compared with developed market valuations.
And come February, you also might consider cancelling that gym membership if your overconfidence got the best of you.
 See, for example:
“Paying not to go to the gym” by Stefano DellaVigna and Ulrike Malmendier.
“Whatever is Willed Will Be: A Temporal Asymmetry in Attributions to Will” by Erik Helzer and Thomas Gilovich
 See, for example, “Home Sweet Home: Home Bias and International Diversification among Individual Investors” by Anders Karlsson and Lars Norden