Swedroe: Inside The ‘Smart Beta’ Hype

February 02, 2015

While Wall Street investment firms have done a very poor job of delivering good risk-adjusted returns to investors, their well-tuned marketing machines have done a great job creating demand for products where none should really exist. Their latest creation is a type of investment offering referred to as “smart beta.”


To ensure you understand why most of what is called “smart beta” is really nothing more than a marketing gimmick, we’ll begin with a definition of beta and a brief history of asset pricing models; namely, models that explain the sources of returns of diversified portfolios.


In The Beginning …

To begin, beta is simply an investment portfolio’s sensitivity to movements in the overall market. The first formal asset pricing model was the capital asset pricing model (CAPM). John Lintner, William Sharpe and Jack Treynor are generally given credit for its development in the 1960s.


The CAPM used a single factor (beta) to evaluate the risk of an individual stock of an index, a mutual fund or a portfolio of funds. If beta is greater than 1, there’s more risk and a higher expected return than the market. If beta is less than 1, there’s less risk and a lower expected return than the market. In other words, there’s nothing “smart” about it. It’s just exposure to risk.


It’s important to understand that CAPM didn’t explain all the differences in returns of diversified portfolios. Returns left unexplained are called alpha. And alpha can be positive (above benchmark) or negative (below benchmark). In the context of our explanation of smart beta, one might say that positive alpha is “smart,” or lucky, and that negative alpha is “dumb,” or unlucky.


… And Then Came Fama-French

CAPM was the operating model for about 30 years, until the publication of work by professors Eugene Fama and Kenneth French. Their research led to a new three-factor model that would become the standard for use in portfolio analysis. This model summarized prior research, which had found that even after accounting for differences in beta, small stocks outperformed large stocks and value stocks outperformed growth stocks.


An important benefit of the three-factor model was the revelation that what looked like alpha was very often “beta”—or simply exposure to a type of risk, or factor, that can explain returns. Instead of just the previously known exposure to market risk, the new model identified additional types of beta.


It demonstrated that returns could also be explained by size beta or value beta.


In other words, active managers who “tilted” their portfolios away from a marketlike portfolio (so that they had more exposure to small and value stocks than the marketlike portfolio) outperformed the market because of their exposure to these other factors.


So This Is What ‘Smart Beta’ Is?

And this is where the smart-beta concept comes from. Let’s look at a simple example of how a marketing department can create a smart-beta product.


Consider an S&P 500 Index fund. The fund owns all 500 stocks in the index. But it doesn’t invest an equal amount in each stock. Rather, the fund’s weights are determined by market capitalization. Thus, the largest stocks constitute a disproportionate share. For example, take a fund where Apple, the largest single holding, currently makes up 3.85 percent of the total holdings, and the five largest together make up about 11 percent.


Since the market-cap weighting methodology leads to higher weightings for large and growth stocks, and because we know there’s a tendency for small and value stocks to outperform, a fund could expect to outperform if it used an equal-weighted methodology and held an equal 0.2 percent weighting for each of the 500 stocks.


Doing so would give it more exposure to the size and value factors. And the marketing machines will call that smart beta. But it’s not. It is just giving a fund more exposure to factors with higher expected returns. In other words, it’s just marketing. And most of what is called “smart beta” is simply designing a fund that has more exposure to the factors that explain returns than a marketlike portfolio.


An excellent example of what Wall Street refers to as “smart beta” can often be found in funds based on Research Affiliates Fundamental Indices. The evidence shows that the performance of these indexes is well explained by the multifactor models, and not by their fund construction rules. Therefore, they aren’t actually better, or smarter, beta. They just have more exposure to the factors.


For investors who are interested in a more detailed and far more amusing explanation of the Fundamental Indexes, I recommend that you consider reading this piece by Cliff Asness of AQR.



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