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, , | November 15, 2013

  • Compensated risk factors include market, size, value, momentum and profitability
  • Engineered or “smart” beta refers to investment strategies designed to capture intentional exposure to one or more factors

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Eugene Fama won this year’s Nobel Prize in Economic Science for ground-breaking research on asset pricing and the relationship between risk and return. Although he is most notable for his work on efficient markets, which paved the way for low-cost index funds, Fama also contributed heavily to the development of our understanding of the complex, multi-factor relationship between risk and return.

William Sharpe began illuminating the relationship between risk and return by differentiating between two forms of equity risk: systematic and idiosyncratic. Systematic risk is the risk of investing in the fully diversified equity market. This market risk has historically been compensated with a meaningful return premium above the Treasury bill rate. When the equity market return is defined as a premium above Treasury bills, it is called the market factor. Idiosyncratic risk is the risk associated with undiversified security concentration. Although idiosyncratic risk offers a lot of return variation, this additional risk is not priced by markets to earn a return premium because it is easily diversifiable. Sharpe won the 1990 Nobel Prize in Economic Science for identifying that an asset’s or portfolio’s expected return depends on its sensitivity (beta) to the market factor.

In addition to the market factor, prominent economists including Fama, Kenneth French and others over the past 22 years have identified four factors that explain and predict equity returns:

  • Size factor – the returns of small stocks over the returns of large stocks

  • Value factor – the returns of value stocks over the returns of growth stocks

  • Momentum factor – the continued returns of high- versus low-returning stocks

  • Gross profitability factor – the returns of stocks with high gross profits-to-assets over stocks with low gross profits-to-assets

Fama and French (1992) showed that market, size and value factors explain more than 90% of the return variation across diversified equity portfolios. In other words, the three factors largely explain diversified risk and return. Momentum (1993) and gross profitability (2012) round out the picture.

Importantly, the five factors all differ from each other, offer evidence of true average return premiums and explain the observed patterns of diversified portfolio returns. Exhibit 1 shows the compound annualized return premiums of each factor and its correlation to equity (the market factor) over the longest available history. 1

All of the factor premiums are positive and statistically significant, which means they are all likely true return premiums making for potentially worthwhile investments. Clearly, momentum offers the highest return premium but it is also the most difficult to invest in because it requires high turnover, and the associated trading costs and taxes reduce the amount of net premium available in practice.

Each factor is uncorrelated to equity, which offers the opportunity not only to capture additional return from factor risk, but to combine factor exposures and achieve a diversification benefit when capturing that additional return. Momentum is particularly diversifying to portfolios that own exposure to the value factor. This is because momentum is negatively correlated to value – but unlike growth (the inverse of value), it offers a positive return premium.

In periods like the late 1990s when value underperforms, exposure to momentum acts to stabilize the investment returns and relative performance of a value tilted portfolio.

Factor investing requires a long-term view. Exhibit 2 shows rolling annualized factor return premiums for U.S. size, value, momentum and profitability factors from inception to 2012. These factors are risk premiums, meaning they offer a return premium on average. But this average is surrounded by uncertain positive and negative return outcomes, as seen in Exhibit 2. In all cases, there is more area in the graphs above the 0% axis than below it. The returns persist over sufficiently long time horizons with no clear evidence of deterioration. The graphs show that factor returns are firing at different times, providing for a potential diversification benefit when combined in an equity strategy. And finally, the annualized factor premiums appear largely unpredictable, suggesting that factor timing is ill-advised.

Engineered beta refers to investment strategies designed to capture intentional exposures (betas) to one or more factors. We next use simulated historical returns to illustrate the potential benefits of engineered beta portfolios of diversified factors. Exhibit 3 shows five equity portfolios. The first portfolio (Total Market) is the total capitalization weighted market of all U.S. equities. Since 1963, the common inception of all five factors, it has produced a compound annualized gross return of 9.8% and standard deviation (volatility risk) of 15.6%. For Market + 1 (M1), we continue to capture 100% exposure to the equity market (1.00 market beta) plus 14% exposure to the size factor (0.14 size beta) and a 16% exposure to the value factor. The result is a higher 11.0% compound annualized return with similar risk as the Total Market portfolio.

Although we have invested in additional sources of risk, they are diversifying. M1 has a similar tracking error to the Total Market as the Standard & Poor’s (S&P) 500, but with a positive return premium, unlike the S&P 500.

For Market + 2 (M2), we push out further the size and value tilts to 22% and 24%, respectively. This increases returns relative to M1 with only a marginal increase in risk. M2 + Momentum shows the diversification benefits of adding momentum to a size and value tilted portfolio. As we noted, the momentum factor is more difficult to invest in than size or value, but it is very diversifying and adding a measured 6% exposure stabilizes M2 and produces higher returns with similar risk. Gross profitability is a newly discovered factor, and the research to understand it more fully is underway. The last portfolio in Exhibit 3 illustrates a combination of value and profitability. Importantly, Sharpe ratios – which measure return per unit of risk – increase as equity portfolios move from left to right in Exhibit 3. 2

The portfolios we illustrated here capture different degrees of factor exposure possible in unleveraged, long-only equity portfolios. These strategies may be implemented using relatively low-cost, engineered solutions. Engineered beta strategies offer investors a way to benefit from a more sophisticated understanding of market efficiency based on the work of at least two Nobel Laureates.


1 U.S. return data spans 1926 –2012 for market, size and value factors, 1927–2012 for the momentum factor and 1963 –2012 for the profitability factor. Sources: Ken French data library and Robert Novy-Marx.

2 Sharpe ratios are calculated as annualized arithmetic return divided by annualized standard deviation.

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