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Could smart beta be the Holy Grail of investing? Much has been written about this latest index innovation, which allows investors to pinpoint exposure to a particular market or select factors. These alternatives purport to offer a smarter way to gain exposure to the drivers of investment returns and an improvement over the commonplace strategy of putting money in index funds. Although smart beta investing has its critics, the strategy is gaining in popularity among institutional investors. Research firm Morningstar reported in May that assets in these strategies increased by 59 percent in 2013 and now account for nearly $300 billion. A January 2014 survey by asset manager Russell Investments found that 32 percent of institutional investors had a smart beta allocation, although it was less than 10 percent of the average investor’s equity portfolio. In another January report Cogent Research, a division of Market Strategies International, said that nearly half of the institutional investors it surveyed expected to invest in smart beta strategies within the next three years.

Smart beta — also known as factor investing, a label that better captures the rationale behind the strategy — promises indexlike management in terms of costs and transparency while affording exposure to more than just the static market factor. The claimed benefit is that by using a set of rules to manage exposures to one or more dynamic factors (things like value, momentum and volatility), investors can achieve a better risk-return profile, essentially outsmarting the static beta that is captured in a traditional index fund.

But I say not so fast. If factor exposures drive portfolio returns, is the time-honored asset-class-driven allocation misguided? Wouldn’t a smart beta approach demand agreement on which factors to include and exclude? And what about unintended tilts? Would factor exposure in a smart beta portfolio be constant? From a theoretical standpoint it’s hard not to be a fan of factor-based investing — until one realizes the complexity surrounding this type of strategy.

Traditionally, investment returns have fallen into one of two categories: alpha or beta. Ever-elusive alpha is the risk-adjusted return above the return of the overall market; it represents manager skill. Beta is the return of the broad market, typically represented by a benchmark like the S&P 500 index. Smart beta — a hybrid product crafted by financial engineers — is meant to deliver the best of both worlds: market-beating returns at indexlike prices.

Despite their great promise, smart beta strategies have their limitations. To begin, it’s much harder to allocate assets along factor lines than by asset class, especially without the use of short positions (a constraint for many investors). If investors are going to move from asset-class allocations to factor allocations, they will need access to sophisticated risk models, which could be a challenge for smaller institutions. In addition, the rules-based approach used by smart beta strategies makes them vulnerable to front running. Still, smart beta is likely to offer a net benefit to investors, if for no other reason than that it will make them smarter about the factors that drive returns.

The idea of building an index to capture the systematic component associated with a particular type of investment is hardly new. The first factor-based index arguably was the Dow Jones Industrial Average, which was launched in 1896. Investors who observed its performance vis—vis their own portfolios would have been struck by the degree to which their portfolios’ returns were correlated with the returns of the index. In modern vernacular the portfolio returns could be deconstructed into two pieces: the first a function of the return of the market as measured by the DJIA and the second a function of the particular stocks they owned. If the portfolio mirrored the constituents of the DJIA, the majority of the return and the volatility of the portfolio could be attributed to the same systematic sources that drove the returns of the DJIA.

There is now substantially more understanding about the drivers of returns in various markets, including equities. fixed income and currencies. Obviously, decomposing the return of an equity portfolio requires knowledge of considerably more than just its exposure to the overall market. Within the equity markets, for example, the initial realization that small-cap stocks have a different return distribution from large-cap ones compelled investors to evaluate the small-cap exposure of their portfolios differently — and, more important, to make this decision part of their risk management process. This led to the now-common practice of viewing a portfolio’s return in the context of the market returns as well as the style of the underlying portfolio.

As we have gained a greater understanding of the different types of systematic factors that affect returns, we are increasingly moving to a world in which the portfolio returns are attributed to multiple sources. In the case of equity portfolios, as illustrated in the chart below, investors are increasingly able to single out the portion of a portfolio’s return that is attributable to systematic factors (beta) from that which is idiosyncratic to the portfolio (alpha). This latter component — which is, one hopes, positive — represents the true value of active management.

This realization of the magnitude of returns stemming from systematic factors has led to the desire to harvest these sources of return in a cost-effective manner. This approach is based on a sound theoretical foundation — namely, that individual securities are expected to earn a return through their exposure to systematic factors. These factors are rewarded, on average, but they will perform poorly in bad times and more than compensate for this poor performance in good times. The goal of a portfolio manager amounts to controlling these systematic factor exposures and diversifying away those risks that are not systematically rewarded. The management of the systematic risks in a portfolio can be active — requiring forecasts of the different factors’ returns — or passive, in which the exposure of the portfolio to the various factors is kept static.

The recognition that some or all of an active manager’s returns may emanate from systematic factors is undoubtedly one of the reasons for the current enthusiasm for smart beta. Why bother hiring an active manager if investors can simply allocate some of their investments directly to these systematic sources of return? (See also, “ Will Smart Betas Make Hedge Fund Managers Obsolete? ”) In addition, if returns are driven by exposure to certain factors, shouldn’t investors understand and manage their overall allocations to these same risk factors? Moreover, if security returns are driven by more than a single market factor, wouldn’t it make good financial sense to diversify one’s portfolio across these different types of systematic risk? In a nutshell, these arguments serve as the rationale for the host of smart beta exchange-traded funds and portfolio solutions that are being offered today. Although the discussion around smart beta tends to be equity focused, the theory is applicable to other asset classes, including currencies, fixed income and commodities.

The idea that factor exposures drive a portfolio’s returns more than asset classes leads one to the realization that focusing on asset allocation is misguided. Instead, investors should concentrate on how they allocate to the key factors that drive asset returns. This requires a dramatic recasting of our view of the world — and obviously some agreement on those key factors, or betas. Ideally, the key factors should be uncorrelated with one another; each should capture a unique type of systematic risk or investor behavior.

Risk factors can be static or dynamic. A static factor is one that does not require much active management, such as exposure to the overall equity or fixed-income market. The returns for most asset classes can be thought of as static risk factors. A static diversified capitalization-weighted portfolio, even absent rebalancing, will do a reasonable job of capturing the overall return of an asset class.

A dynamic factor would require at least some modicum of active management — and consequently some associated transaction costs. One example involves investing in cheaper-than-average securities. In equity markets this value trade could be accomplished by investing in securities with lower-than-average price-earnings ratios and shorting ones with higher-than-average P/Es. In the currency market the carry trade, as it is known, could be achieved by borrowing in low-interest-rate currencies and investing in high-interest-rate ones. Similarly, in fixed income an investor could hold long positions in higher-interest-rate parts of the yield curve and short the lower-interest-rate parts — this is sometimes referred to as riding the yield curve. Regardless of the asset class, maintaining exposure to this type of factor requires that the portfolio be continually rebalanced. Dynamic factors often go beyond asset classes and involve active management to maintain exposure to the factor. The process can involve human judgment or a set of rules. Call it what you may, capturing a dynamic factor involves a set of decisions unlike those required for investing in a static factor.

Other examples of dynamic factors that go beyond asset classes include volatility: the tendency for low-volatility securities to outperform high-volatility ones. This effect has been noted in equities, fixed income and currencies. The momentum, or trend, factor — which captures the propensity of securities that have strong relative performance over the past six to 12 months to continue to outperform — also works across equities, fixed income, currencies and commodities.

When it comes to the static factors, there is some agreement that definitions are based on the broad asset classes. There is much less consensus concerning the precise number and construction of the dynamic factors. For example, though most market pundits would agree on value as a dynamic factor, there is no agreement, even among equity analysts, on whether it should be defined based on P/E, price-to-book or dividend yield. Some providers of risk models — which are designed to use these systematic factors to describe the returns of an equity portfolio — often include all three of them as factors, in addition to several others.