Is Smart Beta Really So Smart?

Smart beta is the hot thing in investing strategies, marketed as a new way to diversify and reduce risk. But Eugene Podkaminer ’01 argues that common smart beta strategies recycle long-established methods and likely aren’t the most efficient way to achieve those goals.

It’s nearly impossible for a 401(K) participant or chief investment officer to open an investing publication today without encountering the term “smart beta.” This makes for an opportune moment to take a deep breath and re-examine what smart beta is (and isn’t) and what role it may serve in an investor’s portfolio. If nothing else, readers of this article will be more articulate at cocktail parties than most about index construction, and that must be good for something.

An apparently new innovation, smart beta promises robust returns with lower risk and less correlation with other investments. A closer look reveals that what typically drives smart beta risk and return characteristics are decades-old factor tilts.

Smart beta is best defined by what it isn’t rather than by what it is. Smart beta is not a traditional capitalization-weighted index (CWI); instead it is commonly understood to be a mechanical, transparent, rules-based strategy which tilts towards certain factors, typically long-only (no shorting allowed) and within a single asset class (like U.S. stocks). We define three common examples below.

Though widely used, standard capitalization-weighted indices, including the S&P 500, Russell 2000, and NASDAQ, have their shortcomings. Securities with inflated prices can balloon to take up a larger and larger share of an index, like technology stocks during the dot com bubble or financials prior to the 2008 crisis. Investors who like to avoid bubbles (wouldn’t we all?) have loudly, and often correctly, criticized traditional indices for promoting stocks with poor fundamentals only to see values come crashing down after the bubble has popped.

An increasing number of investors have been calling for different ways of constructing indices, with weights determined by approaches as diverse as risk, simple averages, or quality of cash flows. These investors see CWI as inefficient, and have conviction that they can be “smarter” about index construction. I refer to strategies which do not use capitalization weights as “alternative indices” because they represent an alternate approach to the norm.

So-called “smart beta” alternative index approaches aim to combine elements of passive index-tracking and active fund management to deliver the best of both worlds: transparent construction, the promise of diversification, and robust excess returns—and all at low cost.

Three common approaches are:

  • Low volatility strategies, which promise to reduce total risk (as measured by standard deviation) of the index by carefully selecting less risky stocks.
  • Fundamental indices that shun market capitalization as the appropriate measure of economic size. Instead they focus on various alternative measures including sales revenue, cash flow, dividends, and stock buy-backs.
  • Equal weighting, the simplest form of index construction. In light of the more complex approaches discussed above, just averaging an entire universe eliminates many complicated choices.

In order to understand “smart beta” we first need to recall the genesis of “beta,” which has lately been much abused. A quick look through my dusty business school notes reveals that Bill Sharpe popularized the concepts of beta and alpha through the Capital Asset Pricing Model (CAPM) introduced in 1964. By streamlining the tenets of Markowitz’s earlier Modern Portfolio Theory into a compact and practical framework, CAPM introduced “the market” as a factor through a simple yet powerful relationship: a portfolio’s return is comprised of alpha, the portion of return not explained by the market, plus the return of the market multiplied by a beta term.[1] Over time, the word “beta” has come to be synonymous with the capitalization-weighted return of the market, while “alpha” is used as a measure of a manager’s contribution to performance.

Beginning in the late 1980s, Sharpe and many others began to recognize that for many active (i.e., non-index) strategies, a sizeable portion of the “alpha” attributed to manager skill by the CAPM could be reproduced using simple rules-based approaches.

The CAPM framework was extended by using the Arbitrage Pricing Theorem (APT), which expands beta from a single market measure to include any number of factors. APT enables us to think in terms of multiple betas (or factors), including style (growth and value), capitalization (large, mid, small), and momentum (persistence among “winners”).

This led to the development of rules-based “style” indices such as the Russell 1000 Growth Index or the S&P 600 Small Cap Value Index. These indices represented both a more accurate way to measure the “true” alpha being generated by a strategy, and a cheaper way to passively access the persistent factor exposures inherent in a strategy.

Conceptually, many of today’s “smart beta” strategies are really no different from the original, decades-old style indices. While each of these newer strategies may emphasize a different set of market exposures, they all use fairly transparent, rules-based approaches to efficiently and cheaply implement a combination of factors. The challenge for investors is in deciding which factors to emphasize (if any).

Analysis of the three examples above reveals persistent factor tilts:

  • Low volatility stocks can have a value bias because dull companies that do not grab headlines tend to be less volatile than the hot technology firm that just released the new must-have mobile device or the pharmaceutical company that just obtained FDA approval for a drug. Over the past 10 years (through December 2013), this strategy has exhibited a 0.93 correlation with the S&P 500. Despite lower risk than cap-weighted indices, it is still highly correlated with them.
  • Fundamental indices may use sales figures adjusted for leverage to minimize heavily leveraged companies; operating cash flow as a proxy for balance sheet health; and dividends plus stock buybacks to reflect overall enterprise health and management confidence. The goal of such strategies is to take advantage of the disconnections between share prices and other fundamental metrics of company success. However, analysis shows these approaches have a strong value bias as well.
  • Intuitively we know that an equal weighting scheme will introduce a significant small cap bias because the stocks that have a lower market capitalization are held at the same weight as major multinational corporations. The portfolio will likely also have a value tilt because equal weighting will expand the presence of value stocks, which tend to trade at lower multiples.

And therein lies the rub. Smart beta strategies are often marketed as diversifiers or volatility reducers. But if the alternative index is composed of the same securities as a CWI (e.g., large cap U.S. stocks), then no matter how much you re-weight, a very high degree of correlation remains. The same goes for volatility. Certainly some low volatility strategies have lower risk, but there is no reason to expect that a different weighting scheme will magically result in an overall equity portfolio which is appreciably less risky.

Alternative indices are one way to express a belief in value and small cap factor tilts. For an investor who really believes in value and small cap (or other factors), the most efficient way to implement is by adopting a risk premia strategy that can invest across multiple asset classes and permits short-selling, which is far more compelling than a single-asset class, long-only alternative index strategy.

Risk premia, or risk factor, strategies break down asset classes (like U.S. equity) into their fundamental building blocks—for instance, exposure to economic growth, interest rates, inflation, liquidity, and other variables. These factors can then be remixed into a portfolio which is, in theory, more diversified than traditional approaches because it relies on a wider pool of compensated premia.

This essay is drawn from Eugene Podkaminer’s white paper “The Education of Beta: Can Alternative Indices Make Your Portfolio Smarter?”

For more on risk premia and factor-based investing, read Yale Insight’s interview with Podkaminer on the theory and practice of the approach.

1 Technically the CAPM model explains “excess returns” or returns above the “risk-free rate” (traditionally defined as the return on 90-day T-Bills). It also includes a residual term. The above translation omits these components of the model for the sake of simplicity. [Back]

Senior Vice President of Capital Markets Research, Callan Associates