By Michael Rawson, CFA
Over the past two decades, the share of passively managed equity fund assets has risen. While some lament that passive investors have consigned themselves to merely average returns, the truth is that the average has been pretty good. The return of large-cap U.S. stocks has been about 10% per year on an annualized basis since 1926. This is the baseline upon which we can judge the performance of any U.S.-stock fund. In fact, most of the movement of our funds can be explained by exposure to a broad market index. This is called beta in industry parlance. Any additional return that an active portfolio manager might deliver on top of the average is called alpha. If we graph monthly fund returns on a y-axis versus index returns on the x-axis and then fit a line through the points, what we have is a linear model called the capital asset pricing model, or CAPM. The two terms, alpha and beta, refer to the intercept and slope of the line.
Alpha can be an indication of the unique skills a portfolio manager brought to the table. Active managers that can deliver alpha command higher fees, while beta is often obtained through low-cost passive funds. But alpha can also be a sign that the model is incomplete and failed to capture everything going on in the portfolio. As the science of investing has evolved, researchers have uncovered other common sources of return.
The traditional beta from the CAPM indicates the extent to which a portfolio was exposed to the market. But there are other betas. Any characteristic or attribute of our portfolio that is systematically related to risk or return can be accounted for using this statistical approach. Naturally, we want to look for attributes that are associated with return. Value, size, momentum, liquidity, and quality have been identified historically in the data, and there is some theory that suggests these are not spurious relationships. The table below shows the average monthly excess return for the market along with returns from going long a basket of stocks with high values of the factor and short a basket of stocks with low values of the factor. The data are from Andrea Frazzini. More information about these factors can be found in this article by my colleague, Alex Bryan.
We analyzed all passive and active funds and ETFs in the U.S.-equity category, first using a single-factor model, also called the CAPM, and then with a multifactor model that includes these other factors. In the next table, we show the average monthly return and standard deviation along with median values for the alpha and betas. We also show the R-squared, which tells us the how good the model is at explaining the movements in our portfolio, and the tracking error, which indicates how much movement in the portfolio the model could not explain.
The average monthly return for passive funds was 1.01%, and the average MKT beta was 1.04. Multiplying that beta times the average return to the MKT factor of 0.93 yields 0.97. Adding in the alpha of 0.04 to 0.97 brings us to the average monthly return of 1.01. Thus, exposure to the market beta provided us with 0.97 of our monthly return, while alpha provided just 0.04.
Moving to the multifactor model, we can decompose the 1.01% monthly return by source by multiplying each beta by the factor returns. Market beta provided 0.95 (beta of 1.02 times factor return of 0.93), while exposure to small-cap stocks provided 0.04 (beta of 0.24 times factor return of 0.16). In the multifactor model, the alpha for passive funds was negative 0.01. Essentially, all of the return was provided by exposure to beta factors.
The charts below show the total monthly return broken out by multifactor beta or by alpha, sorted by alpha.
Old-fashioned market beta provides the bulk of return--hence, our conclusion that investors should be perfectly content with a fund that provides nothing but beta. Having exposure to some strategic beta factors can help on the margin. It is clear that beta provides the majority of return, even among funds that manage to capture significant alpha.
The single-factor model explains roughly 96% of the variation in monthly returns for passive funds and 94% for active funds. When we move to the multifactor model, the explanatory power improves to 99% for passive funds and 97% for active funds. The 99% R-squared for passive funds suggests that almost of the movement in passive funds can be explained by the systematic factors and the residual tracking error is only 0.46. In contrast, active funds have a residual tracking error of 0.95. We expect our active funds to swing for the fences in hopes of earning a high return. Almost all passive funds have very low tracking error, whereas the distribution of tracking error for active funds is much wider. Some active funds have low tracking error and may be closet index funds.
Taking a look at a few specific funds might shed more light on this approach. With its fundamental weighting, PowerShares FTSE RAFI US 1000 (NYSEARCA:PRF) is often thought of as the prototypical "smart beta" fund. In a single-factor CAPM, the fund has a significant alpha of 0.09% per month. But in the multifactor framework, that alpha is negative, as it has been replaced by tilts to strategic betas.
Gold-rated AMG Yacktman Service (YACKX) had an average monthly return over the period of 0.89%. In the single-factor CAPM, the market beta was 0.71 and the alpha was an astounding 0.23% per month. Multiplying the market beta by the return to beta of 0.93% results in a return to beta of 0.66%. Adding to that the 0.23% alpha equals a 0.89% average monthly return. However, in the multifactor model, we can see that a large share of Yacktman's alpha came from big tilts toward value and quality stocks. Multiplying these betas by the factor returns results in a return contribution from beta factors of 0.98% and an alpha of negative 0.09%.
The multifactor model used here is by no means perfect. It does not take into account other definitions of value, such as earnings or cash flow, and it does not take into account a manager's ability to time exposures. What it does show is that performance evaluation is getting smarter and that there are low-cost ways to get exposure to systematic characteristics associated with return.
Morningstar has defined strategic beta as any index fund that attempts to improve upon the risk or return characteristics of traditional index alternatives, such as S&P 500 and Russell 2000 Index funds. For example, iShares MSCI USA Momentum Factor (NYSEARCA:MTUM) attempts to enhance returns by investing in stocks with positive momentum, while PowerShares S&P 500 Low Volatility (NYSEARCA:SPLV) attempts to reduce risk.
There will often be some gray areas between what might be called strategic beta, traditional index, and even a truly active fund. However, the tools we use to diagnose these products can be applied to any fund, and the outcome can inform our expectations about future performance. Despite the attention alpha receives, beta is the most significant source of return for most funds.
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