A Closer Look at "Factor Beta" Index Products
Broadly speaking, investment products 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 activities performed by an active manager compared 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.
The largest group of investment products seek to provide investors with a bundled mix of beta and alpha returns. Most actively managed mutual funds are in this category.
Traditional actively managed funds 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, there are products that use a low-cost indexed approach to track the performance of different sub-segments of a broad asset class, with the sub-segment identified through the use of an index that is based on a clear, publicly disclosed set of rules.
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 actively choose to hold and 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.
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 active 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 actively adjust their portfolio’s exposure to various sources of risk (and therefore potential returns).
In a reasonably efficient market, it should be impossible to consistently earn alpha this way, through a long term “factor beta” tilt. Rather than alpha, these factor exposures (e.g., a long-term tilt toward small cap value stocks) should produce either lower returns but with lower risk than the overall asset class, or higher returns with higher risk.
To believe that factor beta investments will will produce true alpha requires acceptance of two additional premises.
The first is that some 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.
For example, in their paper "Do Noise Traders Move Markets?" Barber, Odean and Zhu found that "stocks heavily bought by individuals in one year underperform stocks they heavily sold by 4.4% in the following year." In addition, multiple studies have found that overconfidence is a hallmark of human nature (e.g., see "Sensation Seeking, Overconfidence, and Trading Activity" by Grinblatt and Kelojarju). Many algorithmic trading strategies are designed to exploit these systematic human tendencies.
The second premise is that there are permanent barriers that prevent other investors (and especially algorithmic trading strategies) 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).
If both these premises are true, then historical data should show significant positive risk adjusted returns from permanently tilting one's portfolio towards a sub-segment. But that is not what you see in the historical return data for various factor beta tilts.
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).
Over long periods, the low or negative Information Ratios for various factor betas (e.g., small cap, value, etc.) show that there is 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 raise 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 fund products that track each of them?
In fact, this is just what we have seen happen over the past decade, with an explosion of “smart beta” products which have further muddied the distinction between indexed and passive investments.
So, to sum up, investment economics have not changed.
Assets’ return generating processes still have two parts: one systematic and one unique (and diversifiable). However, these basic ingredients are being repackaged and combined into an increasingly wide and confusing range of investment products. Some of these offer systematic "market returns" on broad asset classes (call this "classic beta"); some offer sources of unique (alpha) returns at the sub-segment level at a relatively cheap price ("factor beta"); some offer a combination of alpha and beta (classic “actively managed funds”), and other products offer relatively expensive sources of uncorrelated unique returns, or “pure alpha.”
Next up: The arguments for and against index mutual versus Exchange Traded funds (ETFs).