The Exchange Traded Fund Market

The number of exchange traded funds (ETFs) listed on markets around the world is growing rapidly. However, the launch of so many new ETFs has also made this part of the investment landscape a much more confusing place. With that in mind, we have prepared this short guide to understanding exchange traded funds.

We will start with a short review of investment theory, and then use it to classify the many ETFs that are now offered in markets around the world. Let's begin with the definition of an "asset class." In our view, asset classes should be broadly defined, as they are distinguished by significantly different underlying economic return generating processes. Different statistical techniques can be used to perform this analysis, including correlation (true asset classes should have returns that have low correlations with each other) and principal components analysis (true asset classes should have different loadings on different return generating factors). For example, consider the difference between domestic investment grade bonds and emerging market equity. The underlying economic processes that generate the returns on these two investments are quite different from each other.

In contrast, the processes generating returns on "large cap" emerging market equities and "small cap" emerging market equities are quite similar, as evidenced by the high correlation between their respective returns. Hence we regard these two categories not as distinct asset classes, but rather as an example of "tilts" or sub-segments within the emerging market equity asset class.

An asset class's return generating process can be broken down into two parts. The first is common to all the securities that make up the asset class. It is often called the "systematic" or "non-diversifiable" return on the asset class. The second return source is either unique to a specific company, or common only to a subset of companies within the overall asset class (e.g., companies in the energy sector). Here is a simple example. Consider an asset class made up of only two securities, which have equal weightings on all possible measures (e.g., their market capitalization, their book value, their sales, etc.). The return on security A is 7%; the return on security B is 3%. The average return is 5%, which represents the systematic return on the asset class, which would be received by an investor who owned both A and B. The unique return on Security A is 2%, and on B it is (2%).

This simple example illustrates a number of critical points. First, at the asset class level, the unique returns (also called "alpha" returns) cancel each other out, leaving only the systematic return. Traditionally, this has been referred to as the "market" or "beta" return. Investing with the objective of earning only this broad asset class return should, in our view, be called "asset class investing", "market investing", "beta investing" or "passive investing." As you can see, the distinguishing characteristic of the market return for an asset class is that it requires no ability or attempt to forecast A and B's future returns. It simply seeks the return that comes from owning all the securities in the asset class.

Second, return is compensation for bearing risk. At the asset class level, you receive only systematic market return, which compensates you for bearing systematic market risk. This systematic return is composed of two parts: the risk free rate (which compensates you for deferring consumption) and an asset class risk premium. Most important, earning this asset class risk premium does not depend on skill.

A third insight from our simple example is that, when you take on additional unique risks that can be avoided through diversification, you may receive additional compensation in the form of positive alpha. In the short term, this can be due to either luck or skill. However, as you keep taking on unique risks over longer and longer periods, the probability increases that your average return will be either zero or (after your higher costs) negative - unless you have better than average forecasting skill, and can better (than the average active investor) distinguish between those unique risk exposures that will earn positive and negative alphas. This last point is an important one, and is often overlooked. Successful active investment doesn't depend just on having some forecasting skill. Rather, it requires that your forecasting skill be superior to the average level possessed by the other active managers against whom you are competing. And that is a far higher bar than most people like to admit to themselves.

A fourth point is that forecasting skill must be based on either access to superior information and/or use of a superior model to make sense of publicly available information. It also must be based on the existence of a financial market that is not perfectly efficient. In an efficient market, skilled forecasting is impossible because market prices already incorporate all the public and private information available about a security, and the pricing insights generated by many different models. As we have noted many times in our writing, we believe that financial markets are not always efficient. Rather, we see them as a complex adaptive system that, while strongly attracted to efficiency, seldom achieves it. Hence, we believe that skilled forecasting and successful active management that generate alpha are possible, though quite rare in practice (especially after the additional costs incurred are taken into account).

The fifth point from our simple example is that asset class or passive investing, as we define it is not quite the same as "indexing" which is often used as a synonym for it. In our example, we measured the performance of our two-security asset class by constructing an index, which put equal weights on A and B. The index return was equal to 5%. But suppose that A and B differed from each other in ways besides their return. For example, suppose A had higher revenues and book assets, but lower market capitalization than B. What weights would we then use to construct our index to measure the systematic return on our asset class? Reasonable people can and do disagree on the right answer to the question of how best to measure market returns. This is a critical point, because the use of two different benchmark indexes to measure market return will lead to two different estimates of the size of an active manager's alpha. If that manager's compensation is in any way tied to the amount of alpha he or she generates (as is usually the case), this creates a potential conflict of interest. An active manager has an incentive to choose a market benchmark that maximizes reported alpha, while the investor who is hiring the active manager has an incentive to use the most accurate market benchmark possible. As a practical matter, this benchmark decision is often made by a third party. In the case of institutional investors (e.g., pension plans), it is often made by a consultant hired by the plan sponsor. In the case of individual investors, it is often made by either a financial adviser or a rating service (e.g., Morningstar or Lipper). However, we note that the potential conflict of interest will still be present if the party making the benchmark decision (i.e., the consultant or rating service) derives any economic benefit from the generation of more alpha rather than less by active managers. In our view, this is often the case, with too little recognition of the conflicts of interest that are present.

With that introduction, let us move on to the market for managed investment products (e.g., hedge, mutual and exchange traded funds). Broadly speaking, these fall into three categories. Some products offer only systematic (beta) returns. Their objective is to provide a return equal to the average return on a broad asset class, such as domestic equity (however it is measured by the index provider). Because capturing these returns requires no forecasting skill and only minimal trading, these asset class index funds charge very low expenses.

At the other end of the spectrum, some funds offer only unique (alpha) returns. An example of this type of fund is an "equity market neutral" hedge fund. The manager of such a fund attempts to do two things: (a) utilize his or her superior forecasting skill to identify securities and transactions that will produce positive alpha; and (b) use other transactions to eliminate the fund's exposure to systematic (beta) returns. Because of the additional operations involved in comparison with a "beta only" fund, this "pure alpha" fund must charge higher expenses. Also, based on the assumption that it is easier to run a "beta only" fund than one that earns "pure alpha", the manager of the alpha fund also expects to receive a larger portion of the fund's returns, as compensation for the use of his or her relatively scarcer skill. We also note that you can construct an index to measure the average performance of all equity market neutral fund managers, even though there is no systematic (beta) return involved. As we said, beta investing is not the same thing as indexing.

Other products seek to provide investors with a bundled mix of beta and alpha returns. Most actively managed mutual funds are in this category. They buy and sell securities, but don't eliminate their exposure to beta returns. To maximize their forecasting advantage, many active managers restrict their investing activities to a sub-segment of the broad asset class (e.g., small companies' shares, or shares of healthcare companies), which provides a convenient basis for classifying these funds into sub-segments of the broad asset class.

Finally, in between "pure beta" and "pure alpha" funds lie products that use a low-cost indexed approach to track the performance of different sub-segments of the broad asset class, that are identified using a clear, publicly disclosed set of rules. For example, sector and style (e.g., small cap value) exchange traded funds are examples of these products, as are bond ETFs that track different maturity indexes. Clearly, because they hold portfolios of securities that differ from the composition of the overall asset class, the returns they produce are a type of alpha. Yet, because these approaches to earning alpha have become well-known and embodied in a rules-based index, they are, confusingly, often called "beta" or "factor beta." (Alpha in beta clothing, if you will). As a result, the meaning of "alpha" has shrunk, and is now sometimes taken to include only active manager returns net of the return not only on the broad market but also on one or more sub-segment indexes (i.e., "factor betas") that an investor can buy for a relatively low price. To put it differently, "alpha" is now often taken to mean only an active manager's gross return, less the return on the market and the return on relevant "factor betas" to which the active manager has decided to be exposed via the investments his or her fund makes.

This alpha arguably comes four sources. The first is market timing, or the skill to profitably switch between asset class or factor exposures. The second is security selection, which reflects superior skill in forecasting the returns of individual assets. The third is skill in profitably providing insurance to other investors, for example by selling them put options that limit their downside risk (but increasing the manager's). This is theoretically an attractive source of alpha, as it is not an inherently zero sum game. The fourth source of alpha involves earning a fee for providing liquidity to other investors. Again, this has the advantage of not being an inherently zero sum game. However, liquidity within some asset classes (with equities in the lead) is rapidly migrating to "factor-beta" status.

In our view, the creation of "factor beta" index products has been both a blessing and a curse. On the one hand, they have made it possible to implement a wider range of forecasts at lower cost. On the other hand, they have probably created a dangerous amount of confusion in many investors' minds. Too many people appear to be under the illusion that they can earn alpha over a long-term holding period simply by using these "factor beta" index funds to permanently tilt their portfolios one way or another. In a reasonably efficient market, this should be impossible. Rather than alpha, a long-term one way factor beta tilt (e.g., toward small cap value stocks) should produce either lower returns but with lower risk than the overall market, or higher returns with higher risk. To believe that it will produce positive alpha requires acceptance of two additional premises.

The first is that some investors will systematically, over long periods of time, and for one or many reasons, make valuation mistakes. There is some evidence that this may happen. For example, immediate liquidity needs will always force some investors to sell securities they know are undervalued. And some investors will, because of overconfidence or their use of a momentum strategy, tend to buy securities that are overvalued. However, the second premise is that there are permanent barriers that prevent other investors from arbitraging away most of the alpha that these mistakes are expected to produce, by buying (and bidding up the price of) the undervalued securities, and selling short the overvalued securities. The evidence suggests that this premise is much weaker than the first one (see, for example, "The Limits of the Limits to Arbitrage" by Brav and Heaton). Moreover, if both these premises are true, historical data should show significant positive risk adjusted returns from permanently tilting one's portfolio towards a sub-segment. But this is not what we find.

One way to measure the effectiveness of an active management strategy is by using something called the "Information Ratio." To calculate this, you start with the return on the sub-segment tilt (e.g., the return on a small cap value index) and subtract from it the return on the broad asset class index. Over many periods, the average of this result is the active return on this strategy or its "gross alpha." If you subtract the expenses you pay to the active manager from this, it is the "net alpha." The next logical step is to relate this to the amount of risk that was taken to earn the alpha. This is measured by the standard deviation of the alphas, which is known as "active risk" or "tracking error." The Information Ratio therefore measures the risk adjusted return of the active strategy, by dividing the active return (alpha) by the active risk (tracking error) that was taken on to earn it. Information ratios of .50 or more are generally considered excellent performance by an active manager (although this varies by asset class, with higher IRs generally needed for top quartile performance in asset classes where returns are more volatile).

The following table shows the annualized net alphas, tracking errors, and information ratios for four common sub-segment tilts over two different ten year periods, covering 1979 to 1988, and 1989 to 1998. All the data are in nominal terms, and are based on the Wilshire Indexes.

1979-1988	Net Alpha (assumes 25 bp expenses)	Active Risk	Information Ratio
Large Value	1.60%	7.00%	0.23
Large Growth	-0.85%	3.54%	-0.24
Small Value	6.16%	7.77%	0.79
Small Growth	1.31%	9.29%	0.14
1989-1998
Large Value	-2.01%	5.70%	-0.35
Large Growth	3.06%	4.51%	0.68
Small Value	-3.70%	7.44%	-0.50
Small Growth	-3.25%	10.97%	-0.30

This table makes a central point: over the twenty years covered by the data, there was no "free lunch." As is true of all active management returns, alpha could only have been earned through the use of superior forecasting skill, and not simply by a permanent tilt toward one or more sub-segments of the U.S. equity market.

Philosophically (and practically, if you are an active manager), "factor beta" index products also create a "where will this all end?" issue with respect to the morphing of "alpha" into "factor beta". In theory, there are multiple criteria (factors) that could be used to automatically divide the securities in a broad asset class into smaller sub-segments, whose average returns can then be measured by an index and termed a "factor beta." Why just stop with industry sectors and sub-sectors, company market capitalization, and ratios like market/book and price/earnings that are used to define "value" and "growth" categories? Why not use some measure of economic profits, or non-market capitalization measures of size, or the absolute amount of dividends paid, and create indexes and ETFs that track each of them? In fact, as further described below, exchange traded funds based on all of these concepts have recently either been registered or launched.

The clear implication is that in a world of digital information and low cost computing power, the number of possible categorization/segmentation schemes, and thus indexes and sources of "factor beta" is very large indeed. This logically suggests that many more factor-beta index tracking ETFs should be introduced in the future. What then, will "alpha" signify when this ETF innovation process has run its course? Logically, it will refer to unique returns that, to the extent they can be forecasted, require the use of (a) superior non-public (though not illegal) fundamental information about company or industry (e.g., research into changing customer needs, and which companies are best positioned to satisfy them); (b) superior insight into the future behavior of other investors; and/or (c) the use of a model that generates asset price forecasts from public information (about either fundamental information or investor behavior), whose assumptions are not made public and/or are constantly updated.

So, to sum up this section, investment economics has not changed. An asset's return generating process still have two parts: one systematic and one unique (and diversifiable). However, these basic ingredients have been repackaged and combined into a confusing range of investment products. Some of these offer systematic "market returns" on broad asset classes (call this "classic beta"); some other products offer sources of unique (alpha) returns at the sub-segment level at a relatively cheap price ("factor beta"); and yet another set of products offers relatively expensive sources of unique returns, which are thankfully still called "alpha."

Now let's move from investments in general, to the exchange traded funds (ETF) market in particular. Again, we will start with some definitions. The first is between an open end investment company (e.g., an OEIC in Europe, or a mutual fund in the United States) and a closed end investment company (e.g., a unit trust in Europe, or a closed end fund or ETF in the United States). When a person invests in an open end fund, the fund issues new shares to the investor and receives his or her money in return. That money is then invested in securities issued by other companies. If the person wants to switch out of this investment, he or she sells his shares back to the fund, which redeems them. It raises the cash needed to pay the investor by selling some of the securities it owns. In sum, at an open end fund, the number of fund shares outstanding goes up and down as investors purchase and redeem them, which also causes fluctuations in the value of the securities owned by the fund.

In contrast, a closed end fund issues shares only once to investors, at the time of its initial public offering. It then uses these funds to make investments in securities issued by other companies. If an investor wants to sell his or her shares in a closed end fund, he or she can only do so by finding another investor willing to buy them - the closed end fund does not continuously issue and redeem shares. To facilitate the matching of buyers and sellers, closed end funds are listed on stock exchanges. Exchange traded fund are quite similar to closed end funds, and are also traded on a stock exchange. Historically, the main difference between them was that exchange traded funds tracked an index, while closed end funds were actively managed vehicles. However, as we shall soon see, this distinction is no longer true.

At the highest level, it is now possible for investors in many markets around the world to use exchange traded funds to gain indexed exposure to many, if not all, broadly defined asset classes. For example, the following table shows the different ETF products from companies like Barclays (iShares), Rydex, State Street (SPDRs and StreetTRACKS), and Vanguard that could be used to accomplish this in the United States:

Broad Asset Class	Exchange Traded Funds
Real Return Bonds	TIP
Domestic Investment Grade Bonds	AGG
Foreign Currency Bonds	No ETF. However, could use actively managed Closed End Funds (IMF or JGG), or ETFs that track foreign currencies (and earn local money market rates of interest), such as FXE, FXB, FXC, and FXF.
Domestic Commercial Property	VNQ, RWR, ICF, IYR
Foreign Commercial Property	No ETF yet, but many are in registration.
Commodities	DBC tracks the Deutsche Bank Liquid Commodity Index, while GSG tracks the Goldman Sachs Commodities Index. However, we prefer the Dow Jones AIG Commodities Index, which is more equally balanced between energy, metals, and agricultural commodities. For this, use DJP, the Barclays iPath exchange traded note (ETNs) that tracks this index.
Timber	No ETF
Domestic Equity	TMW, VTI, IWV, IYY, ISI
Foreign Equity	EFA
Emerging Equity	EEM, VWO
Equity Volatility	No ETF

There are also a wide (and growing) range of ETFs available that enable investors to take "factor beta" active management tilts (potentially involving both long and short positions) within most of these asset classes. For example, domestic equity investors can take size (large, mid, small, and microcap), style (growth and value), sector (e.g., energy) and sub-sector (e.g., oil and gas exploration) tilts. Using new exchange traded funds from ProShares, they can also make leveraged investments (whose returns are 1.5 to 2 times greater than the return on the underlying index), and take short positions (these funds’ return is the inverse of the return on the underlying index). Foreign equity investors can also take size, style and sector tilts, as well as regional and country tilts. Emerging markets equity investors can take regional and country tilts. Investment grade bond investors can take maturity and credit tilts. Commodity investors can tilt toward oil, gold and silver. Other tilts (e.g., toward energy, metals and agricultural sub-sectors) may be on the way. We also understand that domestic property investors will soon be able to take sector tilts (e.g., industrial, office, retail, lodging and housing), while foreign property investor will be able to take regional tilts.

So far, so good. However, it gets more confusing. A growing number of new exchange traded funds are based on an indexing methodology that, in our opinion, essentially makes them functionally indistinguishable from actively managed mutual funds. In our view, the critical distinction is who is making the active management forecast and resulting investment decisions. In the case of the low-cost alpha products noted above, which offer a wide variety of sub-segment tilts, it is the investor who is making the forecast, deciding to tilt his or her investment within a given asset class, and then implementing this decision via an ETF that tracks a corresponding index. The new "active EFT" products operate in a very different manner. They typically track an "index" that includes securities selected on the basis of a model (i.e., an algorithm) designed to identify investments that will outperform a given benchmark. As the model operates over time, the securities included in the "index" also change. In our view, this is nothing more or less than a relatively low cost quantitative active management strategy cleverly placed in an index "wrapper" to enhance its customer appeal.

Probably the best know examples of this approach are the exchange traded funds that use something other than market capitalization to determine index weights. In Research Affiliates' fundamental indexing methodology, a company's weight is determined by a mix of its revenue, cash flow, book value and dividends. In Wisdom Tree's dividend weighting methodology, a company's weight it determined by the absolute size of the dividend it pays relative to other companies. These algorithms are quite straightforward, but are still designed to produce index returns that are higher than those on a comparable market capitalization weighted index.

PowerShares uses a more complicated multifactor screening model to identify the securities and their weights in the indices that underlie its exchange traded funds. Another example of this approach is the First Trust IPOX-100 ETF. It tracks the IPOX Composite Index, which is described as "a rules-based value-weighted index measuring the average performance of U.S. IPOs during their first 1000 trading days. Index constituents are selected based on quantitative initial screens and range from large, mature companies and fast growing IPOs to IPOs underperforming the market."

Many more of these "active index" exchange traded funds are on the way. For example, Claymore Investments has registered ETFs that will be based on a sector rotation strategy, purchases and sales of shares by corporate insiders, and shares with low analyst coverage. Similarly, First Trust has registered a new exchange traded fund that will invest in the forty companies (from with a group of 250 large companies) with the highest economic profit (their screen is based on the Deutsche Bank Cash Return on Invested Capital methodology, which is a variant of other residual income valuation techniques such as Stern Stewart's Economic Value Added or Boston Consulting Group's Cash Flow Return on Investment). And in Europe, ABN Amro has created products based on an index that is a rules-based approach to seasonal timing of equity markets. An interesting question is whether the returns from all these products will be referred to as new sources of "beta." On the other hand, something tells us that nobody will end up calling a new product Deutsche Asset Management has registered a new source of beta. The DB Currency Index Value Fund, will, if it is approved by the Securities and Exchange Commission, essentially be an ETF that gives investors access to a hedge fund-type "pure alpha" currency strategy.

As with all active management methodologies that attempt to generate alpha through the use of a superior model, these new "active index" exchange traded fund products run two risks. The first is that the models they use will be copied by competitors, who will bid up the prices of the securities it identifies as undervalued, and thus reduce their expected returns. This risk seems particularly acute for those "active index" ETFs whose model assumptions are publicly available (like fundamental and dividend weighting). The second risk is that changes in the structure of the economy will invalidate the assumptions of the models that underlie these new active ETFs. Just because a model has worked in the past does not mean that it will work in the future.

In sum, the exchange traded fund market has come a long way from its origins, and now contains a growing mix of "beta" and "alpha plus beta" products. Their existence has also made it possible to create your own equity market neutral "pure alpha" product, for example by investing $1,000 in a long-only "active index" product and another $1,000 in an ETF whose returns are equal to the inverse of the relevant asset class or sub-segment index. Granted, using two ETFs is not a very efficient way of doing this; the return would be higher if you could offset the market risk by selling futures instead (which raises the question of how much longer it will be until such products appear). But still, today's ETF market represents a great leap forward from a few short years ago. And it helps point the way to what may lie ahead.

In so far as all these new exchange traded fund products have expanded the choices available to well-informed investors, and enabled them to implement strategies at a lower cost than before, they represent a positive development. On the other hand, not all investors are well-informed, and most do not possess forecasting skills that are consistently superior to those of the average active manager (particularly after expenses, trading costs and taxes are taken into account). This seems especially true of individual investors. For example, in their recent paper "Do Noise Traders Move Markets?" Barber, Odean and Zhu found that "stocks heavily bought by individuals in one year underperform stocks heavily sold by 4.4% in the following year." In addition, study after study has found that overconfidence is a hallmark of human nature (e.g., see "Sensation Seeking, Overconfidence, and Trading Activity" by Grinblatt and Kelojarju). In light of these findings, the expanded range of exchange traded fund products may also have created more potential for investor disappointment. Exciting though all these new ETFs may be, they have not changed the mathematical fact that for every positive alpha earned there is a negative one somewhere else. As always, the best advice for most investors is to proceed with caution into the brave new world of exchange traded funds, with a healthy respect for the immense difficulty of being a consistently successful active manager, and a prudent awareness of the fact that low cost asset class index funds will almost certainly outperform a rapidly rising percentage of active managers as the investor's time horizon lengthens.