The returns of factor investment strategies often look more impressive in academia than in practice.
A version of this article was published in the December 2016 issue of Morningstar ETFInvestor. Download a complimentary copy of ETFInvestor here.
Often ideas don't work as well in practice as theory would suggest. In the investment world that is often due to differences between how investment ideas are formulated in theory and how they are put into practice. Many rules-based investment strategies claim heritage to independent academic research demonstrating that certain factors like value, profitability, momentum, and small size have been associated with higher expected returns. But there are critical differences between the way academic researchers have documented those factors and how investment managers design actual strategies. These adjustments are often necessary to mitigate transaction costs and increase capacity, but they can prevent a strategy from fully capturing the returns shown in the academic literature.
A review of the commonly accepted formulation of the value factor that Eugene Fama and Ken French popularized in their 1992 paper, "The Cross-Section of Expected Stock Returns," will help illuminate these differences. In deriving their version of the value factor, the pair begins with a universe representing all stocks listed on the New York Stock Exchange, Nasdaq, and American Stock Exchange. Stocks with market capitalizations lower than the median stock on the NYSE go into the small-cap group, while those above this threshold go into the large-cap group. Fama and French rank the stocks in each size group once a year in June by their book/price ratios at the end of December. Those in the top 30% (by count) are allocated to the small- and large-value buckets, while the bottom 30% go into the growth buckets, as Exhibit 1 illustrates. Stocks in each bucket are weighted by market capitalization. The value factor is calculated as the average return on the two value portfolios minus the average return on the two growth portfolios.
Because the value factor gives equal weightings to the large and small portfolios, it over-represents small-cap stocks, which have historically offered the biggest value premium. It also overweights value stocks, because stocks that represent the cheapest 30% of the market by count tend to have a smaller aggregate market capitalization than the most expensive 30%.
Lost in Translation?
The approach that most value and growth indexes apply is a clear departure from Fama and French's formulation. For example, the Russell 1000 Value and Russell 1000 Growth indexes use market capitalization (rather than stock count) to set the thresholds for value and growth. In this case, both indexes target stocks representing about half of the Russell 1000 Index's market capitalization. This illustrates a further point of difference from the academic factor: Most indexes don't throw out stocks with moderate value and growth characteristics. And they don't rely on stale valuation data.
These differences can help explain why the Russell 1000 Value Index only outpaced the Russell 1000 Growth Index by 1.10% annually from January 1979 through October 2016, while the Fama-French value factor returned 2.95% annually. A regression analysis of the value index reveals that it captured about 40% of the returns from the value factor over this span, controlling for the index's exposure to market risk, size, and momentum. Because the value factor (as with the other academic factors mentioned here) is constructed as the return on a long-short portfolio, it would be unusual for a broadly diversified long-only fund to capture most of this factor's return.
The Russell 2000 Value Index had a better run against its growth counterpart. From January 1979 through October 2016, it outpaced the Russell 2000 Growth Index by about 3.5% annualized. However, a regression analysis shows that it still only captured about 48% of the academic value factor's returns. Differences in exposure to the market risk, size, and profitability factors help explain the remaining return gap.