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.
No Correct Way
It’s important to understand that having more exposure to factors than the market does isn’t, in and of itself, either good or bad. There’s no one “right” portfolio. You should decide for yourself what the right portfolio is for your unique financial situation. And it should be based on your unique ability, willingness and need to take risk.
My book, “The Only Guide You’ll Ever Need for the Right Financial Plan,” can help you determine the right portfolio for your situation.
Don’t Throw The Baby Out With The Bathwater
Despite having spent so much time exposing much of what is called “smart beta” as a marketing gimmick, I should add that you shouldn’t totally dismiss the idea of smart beta out of hand.
The reason is that, while multifactor models do a much better job of explaining returns than the original CAPM, there still remain anomalies that the models cannot explain. Among those anomalies is that any asset with a lotterylike distribution has been shown to have poor risk/return characteristics. Exposure to these assets results in negative alphas (below-benchmark returns).
So, let’s look at an example of “smart beta.”
An Example To Consider
To begin, there can be many portfolios that have the same exposure to beta. Let’s assume that we start with a mutual fund (fund A) that that owns the total U.S. market. By definition, it will have a beta of 1.
Along comes a manager of fund B who says we can create smarter beta by screening out all the stocks that have been shown to have these lotterylike distributions. Those include shares of initial public offerings, “penny stocks,” stocks in bankruptcy and extreme small growth stocks.
Fund B will also likely have a beta of 1, but it can be expected to produce a higher return in the long term. Since the betas are the same, it seems perfectly appropriate to say that is smarter, or better, beta. Or you could say it’s alpha. The difference is just semantics.
It’s also important to understand that while index funds are excellent investment vehicles, their very nature tends to create some negatives. However, these negatives can be minimized and there are opportunities to enhance their positives. If you’re interested, you can read an article I wrote for Advisor Perspectives titled “Structured Portfolios: Solving the Problems with Indexing.” It covers nine areas of opportunity to add value.
The evidence demonstrates that incorporating the findings from academic research can result in the design of portfolios that produce returns superior to total-market portfolios and pure index funds. But be confident that the academic findings are reliable.
There should be a logical explanation (either risk-based or behavioral) for the findings. In addition, the findings should remain persistent over long periods of time and across asset classes and markets. And they should also survive transactions costs.
Whether you call the results “smart beta” or alpha, the outcome is the same—superior risk-adjusted returns.
Larry Swedroe is the director of research for the BAM Alliance, a community of more than 150 independent registered investment advisors throughout the country.