Product and Strategy Notes: Closer Look at Asset Class Returns (2006 - 2008); Bank Stress Tests; More Bad News for Active Funds; New Products and Research Papers

A Closer Look at Asset Class Returns in 2006-2008

Because adaptive markets are constantly evolving, the ability to explain what happened in the past does not guarantee an equal ability to accurately forecast the future. Yet without an understanding of the past, the future is bound to be even more surprising when it arrives. With this in mind, we have taken a closer look at the dynamics of real asset class returns over the past three years, and reached some conclusions about their implications for our future approach to asset allocation.

Our starting point is the following table, which shows the correlation of real monthly USD returns between a number of asset classes between January 2006 and December 2008.

As you can see, the positive correlations between these asset classes were extremely strong, as was their average negative correlation with volatility. This is what people mean when they say that "correlations went to one during the crisis", and in so doing reduced the expected downside risk protection from holding a diversified portfolio. On the other hand, not all asset classes had such strong correlations with volatility over this period. The correlation between short term U.S. Treasuries and Volatility was positive, at .17. Correlations were essentially neutral with Swiss Francs, gold, and timber (note, however, that in this analysis we use the NCREIF Timber Index, instead of Plum Creek Timber, because the latter, in its REIT form, does not go back to 1990. However, the NCREIF series is appraisal based, and we have interpolated its values from quarterly to monthly, both of which distort its comparative meaning - e.g., by artificially reducing its standard deviation and correlation). Correlations with volatility were also reasonably low, though still opposite signed (i.e., their returns went down somewhat when volatility went up) for real return bonds (.34), domestic bonds (.25), foreign currency bonds (.24) and commodities (.25), as measured by a long position in a fund tracking the DJAIG Index.

We also checked to see if monthly returns for different asset classes were truly independent, as is usually assumed in asset allocation analyses. Our approach was to measure correlations of different asset class returns on their own one and two months prior returns. Using data covering the full 1990 to 2008 period, we found that while most returns were close to zero (as theory would lead you to expect) some clearly exhibited what is known as "autocorrelation." For example, one month autocorrelations (and again, remember that this only captures linear relationships) were .47 for inflation, .52 for real returns on short term Treasuries, .24 for foreign commercial property, .23 for the Swiss Franc, .18 for foreign currency bonds, .17 for domestic bonds and .16 for commodities. Using a two month lag, we found that short Treasuries still exhibited a significant autocorrelation, at .21, while real return bonds had a negative autocorrelation of (.26). This has an important implication. The usual practice in asset allocation analyses is to scale up monthly returns data to annual returns by raising them to the twelfth power. The underlying assumption is that the data are independent; however, the non-zero autocorrelations show that this isn't the case. Hence, using the "power of time" approach introduces an estimation error into the data. The way to get around this is to calculate average annualized returns not by adjusting monthly returns, but rather directly, on a rolling basis (e.g., January to January, February to February, etc.).

We next did a principal component analysis of the rolling annual returns realized from January 2006 to December 2008. PCA is a statistical technique that reduces the variation in a given set of variables to variation in a smaller number of independent underlying factors. For example, assume you have four variables in a data set. Variables one and two may have a very strong positive correlation with factor A (technically, principal component A), while variables three and four have a strong negative correlation with factor B. The art in this type of analysis lies in making inferences about just what those statistical factors represent in the real world. The first factor we extracted from this data set explained 49% of its variation (i.e., 49% in the variation of returns). It had very strong positive correlations with domestic property (.69), foreign property (.87), domestic equity (.84), foreign equity (.92) and emerging equity (.85). It also had moderately strong positive correlations with all other asset classes but two. Its positive correlation with short term Treasuries was only .12, and it had a very strong negative .86 correlation with volatility, as measured by the VIX Index. It doesn't take much art to interpret the real world meaning of this factor: it was the enormous uncertainty shock that hit the world's financial markets in 2008.

The second factor explains 18% of the variation in our returns data. It had strong negative correlations with real return bonds (.49), domestic bonds (.63), short term Treasuries (.86), and timber (.62, but again we caution about the uncertainty inherent in the NCREIF data series). It had moderately positive correlations with all equities and domestic property, and close to zero correlation with commodities and gold. After looking at a variety of economic data, this factor seems most consistent with changes in real bond yields. For example, looking back to the increase in real yields that occurred in 2006, we found that commentators generally believed this would be good for equities, as it would prevent the economy from becoming too overheated.

The third factor explains 12% of the variation in returns. It is highly correlated with returns on commodities, and to a lesser extent gold, timber, emerging market equities, real return and foreign currency bonds. It has a moderately negative correlation with domestic bonds, short term Treasuries and domestic equities and property. We interpret this factor as the commodities cycle, which peaked in July 2008, and brought with it rising fears of higher inflation, the sustainability of the U.S. current account deficit, and the future of the U.S. dollar exchange rate. Overall, these three factors - the uncertainty shock, changes in real interest rates, and the commodities cycle, account for 79% of the variation in real returns on our asset class series between 2006 and 2008. Intuitively, these explanations resonate with our memory of that period.

Our next step was to perform the same analysis on rolling 12 months returns data from 1991 to 2005 to see if these same factors were present. We admit to feeling somewhat akin to the 9-11 Commissions, going back to see what dots were present in the past that we had failed to properly connect. Sure enough, we found the same factors present in the data. The real interest rate cycle explained 19% of the observed returns, though the correlations were somewhat different (e.g., more strongly positive for domestic and emerging market equities, and more negative for volatility). The commodities cycle again explained 12% of return variation, with quite similar asset class correlations. However, uncertainty shocks had a much smaller impact in the earlier period, explaining 27% of variation, compared to 49% in 2006 - 2008. Moreover, in the earlier period, the correlation of volatility with this factor was about half as strong as in the later period, and the correlation with property and equity markets was also lower, though not by as much. Also, in the earlier period, commodities, gold, timber, and real return bonds had low correlations with the factor, while in the later period these were largely replaced by short term Treasuries, and to a lesser extent, timber. In sum, in the 1991 - 2005 data we see some indications of the impact of uncertainty shocks on asset class returns, but not to the degree that we saw in 2006 - 2008. The fact that the top three factors explain 79% of variation in the later period, but only 58% in the earlier period reinforces this point - there were clearly more factors with a relatively stronger affect on returns in the earlier period than there were over the past three years, which were dominated by the uncertainty shock.

In broad terms, however, the results of both PCA analyses are consistent with a view that asset class returns can be segmented into three different regimes. One is characterized by the normal business cycle, exemplified by rising and falling real interest rates. We would expect the supply and demand for returns on different asset classes to be relatively well balanced during this regime, which is most consistent with idealized markets that are in equilibrium and characterized by efficient pricing. The other two regimes represent departures from this equilibrium, in which we would expect to see less efficient pricing and wider gaps between the expected supply of and demand for returns on different asset classes. The dominant characteristic of the first disequilibrium regime is elevated uncertainty. The dominant characteristic of the second is elevated inflation. To test these ideas, we divided monthly real returns from 1990 to 2008 into three groups. Fifty high volatility months had changes (either positive or negative) in volatility of 20% or more. Fifty four high inflation months had a change in the CPI of .4% or more (i.e., almost 5% per year). The remaining months were deemed to be in the normal regime. The following table shows the average monthly return and standard deviation for each asset class under each regime, as well as within regime rankings of relative returns and risks.

This table illustrates a number of interesting points. First, the difference between the regimes is clear. Second, there are obvious benefits to hedging against the downside risks represented by the high uncertainty and high inflation regimes. Third, an allocation to volatility represents a potentially powerful way to limit tail risks, though at the cost of lower returns during the normal regime. In the past, we have noted that investable volatility products are based not on the VIX index, but rather on futures contracts on the VIX, which usually have much lower price fluctuations, which reduce their potential value as a hedging investment. However, this analysis has refined our views on these products. Even if you assume that the returns on VIX futures (which are now available to retail investors via Barclays VXX exchange traded note) equal only 33% of the returns on the underlying index, the above table suggests they may still be a good hedging investment in some portfolios. While further analysis will be needed to determine when that will be the case, we are encouraged by what appears to be a real opportunity for reducing the potential return impact of tail risk in portfolios.

Fourth, gold (which is now more easily accessed via ETFs) also has attractive hedging benefits. However, as an asset class (as opposed to a liquid store of value, in the case of gold coins), gold apparently provides fewer hedging benefits than volatility. Again, more analysis will be needed to determine if this applies to all portfolios, or whether gold as a financial asset class distinct from commodities may in some cases have a permanent role. Fifth, and consistent with many other studies, the table also shows that relative risk rankings are much more consistent across regimes than relative return rankings. Finally, while we have not shown them, our analysis of the correlations between asset class returns under the three regimes found what many readers would expect: correlations are lowest under the normal regime, highest when volatility is high, and in the middle under the inflation regime.

As we noted at the outset, because adaptive markets are constantly evolving, the ability to explain what happened in the past does not guarantee an equal ability to accurately forecast the future. Yet an understanding of the past can surely help us to better prepare for the future, even if we cannot accurately forecast the exact form it will take. In our case, we have for sometime been working on a new portfolio construction methodology that will be based, in part, on an expanded regime switching methodology that incorporates the lessons we have just reviewed. Where we used good and bad regimes in the past, we will be moving to a three regime model, with more significant differences in the risk, return and correlation assumptions under each regime. In addition, because estimation errors are inescapable in any asset allocation analysis, we will also continue to employ shrinkage methodologies to limit their potential impact. We believe that these changes will further improve a portfolio construction methodology that has already proved its mettle under some very challenging circumstances. That said, we also reiterate two key points: all asset allocation methodologies contain inescapable shortcomings. For that reason, they must always be complemented with ongoing asset class valuation analyses (based on a mix of approaches, like our fundamental and scenario based methodologies), as well as a willingness to occasionally move beyond relatively passive risk management techniques like diversification and automatic rebalancing, and employ more active hedging measures like moving to cash or buying options.

Bank Stress Tests

We recently read a fascinating speech by Andrew Haldane, Executive Director for Financial Stability at the Bank of England, and, judging from his writing, a smart and witty man. In "Why Banks Failed the Stress Test", he presents a very good overview of three causes of the risk management errors that led to the 2008 crisis: disaster myopia (e.g., believing the Golden Age of Moderation would go on forever), network externalities (e.g., not taking system level issues - like rapidly falling liquidity - into account in a bank's risk model), and misaligned incentives (e.g., determining this year's bonus on the basis of trades and deals whose true profit wouldn't be known for years). Yet for us, the most interesting passage in the speech was the following: "A few years ago, ahead of the present crisis, the Bank of England and the Financial Services Authority commenced a series of seminars with financial firms, exploring their stress testing practices. The first meeting of that group sticks in my mind. We had asked firms to tell us the sorts of stress which they routinely used for their stress tests. A quick survey suggested these were very modest stresses. We asked why. Perhaps disaster myopia - disappointing, but perhaps unsurprising? Or network externalities - we understood how difficult these were to capture? No. There was a much simpler explanation according to one of those present. There was absolutely no incentive for individuals or teams to run severe stress tests and show these to management. First, because if there was such a severe shock, they would very likely lose their bonus and possibly their jobs. Second, because in that event the authorities would have to step in anyway to save a bank and others suffering a similar plight. All of the other assembled bankers began subjecting their shoes to intense scrutiny. The unspoken words had been spoken. The officials in the room were aghast. Did banks not understand that the official sector would not underwrite banks mismanaging their risks? Yet history now tells us that the unnamed banker was spot-on...When the big one came, his bonus went, and the government duly rode to the rescue...Stress testing was...regulatory camouflage." It is with this comment in mind that we look forward to the release, at the end of April, of the results in the U.S. of the stress testing results mandated by the U.S. Treasury. When you read them, remember this: their "more adverse" scenario assumes only a 3.3% decline of GDP in 2009, followed by a 0.5% gain in 2010, with unemployment reaching a maximum of 8.9% this year and 10.3% in 2010. If these relatively optimistic assumptions produce dire conclusions about the solvency of one or more reporting banks, it will be an interesting indicator, to say the least.

More Comparative 2008 Performance Data

We've been keeping an eye on the slow announcements of 2008 performance data from some well known asset managers, and comparing them to our model portfolios' results (and doing this conservatively, assuming no rebalancing and no increase in liquidity as we recommended in May 2007). Ontario Teachers Pension Plan is perhaps Canada's best known institutional investor. In 2008, they were down (18%) in nominal terms, compared to (6.6%), in nominal terms, for our 4% target real return portfolio, and (9.1%) for our 5% target real return portfolio. In the United States, Bridgewater Associates is one of the world's best known hedge fund managers. Its "All Weather" strategy portfolio is composed of passive positions (some of which are leveraged) in a broad range of asset classes. In 2008, it was down (20%), compared to (15.9%) for our 4% target real return portfolio, and (20.9%) for our 5% target real return portfolio. Finally, the California Public Employees Retirement System (CALPERS, which is the U.S. equivalent of OTPP), was down (27.1%) in 2008.

More Bad News for Active Funds

We have also been keeping up with the growing number of articles that take a critical look at the performance of actively managed funds in 2008 (e.g., "2008: The Worst Year Ever for Active Management?" by Arnott and West on indexuniverse.com, or "Managed Funds Offer Little Cover From the Bear" by Damato and Gullapalli in the Wall Street Journal). As the latter note, "fans of active stockpickers have argued that those managers should do better than index funds in a bear market, because they can move to cash or more defensive shares. But that may be mostly wishful thinking." In part this is due to the higher expenses charged by these funds, and the higher tax liabilities generated by their frequent trading. In part it is due to the difficulty of accurately forecasting outcomes produced by a complex adaptive system. And in part it is due to the fact that many active funds have mandates to stay fully invested in a given asset class (which, as John Redmond of Pan-Asset has noted, simply implies that someone further up the chain was responsible for not adjusting a portfolio's asset allocation in order to avoid severe losses in 2008). Whatever the true mix of causes, the end result is causing changes in behavior. More funds are flowing out of long-only active products that combine beta (asset class) and alpha (security selection) exposures, and into a mix of pure beta (broad passive index) and uncorrelated alpha products. And this is not just happening at the retail level. Another series of articles has noted the major active/passive rethink that is underway at insurance firms selling variable annuities products (see, for example, "Adjusting Annuities: Insurance Companies Moving to Passive Strategies for Better Hedging" by Douglas Appell in Pensions and Investments, and "Laggards Get the Boot" by Scism and Maxey in the Wall Street Journal). The underlying cause of the problems facing these firms is that they have offered minimum guaranteed annuity payouts, while offering annuity buyers a large number of actively managed investment fund options. When these active managers underperform their passive benchmarks, any hedging strategies used by the annuity provider (to manage the risk associated with the cost of making good on the minimum guarantees) become less effective, causing costs to rise and profits to decline. As Scism and Maxey note, "industrywide, issuers of performance guarantees took charges against earnings totaling $1 to $2 billion in the fourth quarter of 2008 because of the weak performance of actively managed funds." As a result, they are quickly shifting to a mix of variable annuity investment options that include a higher percentage of passive funds and a lower percentage of active ones. Finally, Pablo Fernandez (along with Vicente Bermejo) has just published a detailed study of mutual fund performance in Spain between 1991 and 2008 ("Rentabilidad De Los Fondos de Inversion"). Fernandez is an outstanding thinker, and we try not to miss anything he writes (unfortunately, this paper is only available in Spanish). The authors find that over the period analyzed, only 18 of the 1,025 funds (1.76%) with ten years of performance data outperformed the relevant index benchmark.

New Products

Imitation, as they say, is the sincerest form of flattery. With that in mind, we note the launch of a number of new products. Claymore will soon launch an ETF that, like a recent exchange traded note from Elements (ticker LSC), tracks the performance of the Standard and Poor's Commodity Trends Indicator, which takes long and short positions in a basket of commodities. The strongest selling point will be the ETF versus ETN structure, as the latter requires that a buyer take credit risk exposure to the issuer (in the case of LSC, this is HSBC bank), while the former does not. That makes it likely that we will switch to this product in our model portfolios once it becomes available. Elsewhere, we see that Lyxor will launch a new ETN that tracks the price of gold via derivatives, with the remaining principle invested in sovereign bonds to limit the underlying credit risk. Full credit to the structuring team, but we think the marketers have their work cut out for them trying to convince investors that this offering is superior to ETFs that are backed by, and redeemable in, actual physical gold. Finally, we note that Deutsche Bank's x-trackers have launched an ETF that tracks a broad hedge fund index (a similar product to one IndexIQ has in the works in the U.S.). We reiterate our problem with these products: the mix of strategies they track includes not only those that pursue uncorrelated alpha (which is very attractive), but also expensive (think 2 and 20 to the underlying hedge fund managers) long-only strategies. A far more attractive approach would be to launch an ETF that tracked an index that only included uncorrelated alpha strategies.

Interesting Research Papers

Four recent studies are likely to be of interest to financial advisers and individual investors. The first is "Debt Literacy, Financial Experience and Overindebtedness" by Lusardi and Tufano. They find that "debt literacy is low, especially among women, the elderly, minorities, and those with low incomes and wealth....Individuals with lower levels of debt literacy tend to transact in high cost manners, pay 46% more in credit card fees, and are more likely to report their debt loads are excessive or that they are unable to judge their debt position." It is clear that the cost of poor debt literacy are likely to be extremely high on a national or indeed a global basis. In "Socially Responsible Investing in the Global Market", Cortez, Silva, and Areal examine the performance of SRI funds in Europe and the US between 1996 and 2008. They conclude that "socially responsible funds in most European markets do not show significant performance differences in relation to conventional and socially responsible benchmarks, while US funds show evidence of underperformance." They also find distinctive tilts towards small cap and value companies by SRI funds, as well as a significant tendency towards investing in home country SRI companies (i.e., "home bias"). In "Sex Matters: Gender Differences in the Mutual Fund Industry", Ruenzi and Niessen find that female mutual fund managers are "more risk averse, follow less extreme and more consistent investment styles and trade less than male managers. Although their average performance does not differ, male managers achieve more extreme performance outcomes and show less performance persistence. Nevertheless, female managers receive significantly lower inflows, particularly from institutional investors." We could not help but consider these findings in light of those from another study: "The Good, the Bad, or the Expensive: Which Mutual Fund Managers Join Hedge Funds?" by Deuskar, Pollet, Wang and Zheng. They conclude that "a mutual fund manager with superior past performance is more likely to start managing an in-house hedge fund while continuing to manage mutual funds. However, a mutual fund manager with poor past performance is more likely to leave the mutual fund industry to manage a hedge fund...In addition, the managers of mutual funds with greater expenses are more likely to enter the hedge fund industry. The magnitude of such expenses is negatively related to subsequent performance in the hedge fund industry. Hence, hedge funds do not acquire superior performance for their investors by hiring these expensive managers." The next time a salesperson pitches you on hedge funds, ask them what they think of this study.

| Global Asset Class Returns | Uncorrelated Alpha Strategies Detail | Asset Class Valuation Update | This Month's Letters to the Editor: Further Discussion About Carbon Credits as an Asset Class and Model Portfolio Performance 2004 - 2008 | April 2009 Issue: Key Points | Economic Update: Situation, Scenarios, and Asset Allocation Implications | Product and Strategy Notes: Closer Look at Asset Class Returns (2006 - 2008); Bank Stress Tests; More Bad News for Active Funds; New Products and Research Papers |