Measuring Factor Exposures: Uses and Abuses
- Ronen Israel and Adrienne Ross
- The Journal of Alternative Investments
- A version of this paper can be found here
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What are the research questions?
- USES: Can investors really separate “alpha” from “beta”? What are the ins-and-outs of understanding the exposures in a portfolio and their contribution to “alpha”?
- ABUSES: Are there differences in the way strategies are constructed in academic articles vs. the way practitioners actually implement those strategies that are consequential for investors?
What are the Academic Insights?
- YES. But it’s a bit complicated. Two things are needed to separate alpha from beta: first, a risk model and second, use of regression to match the returns from a portfolio to the risk exposures present in that portfolio. What are the appropriate risk factors (CAPM, Fama-French, etc.) that are associated with or explain returns produced by a portfolio? Once identified, then regression analysis is conducted to decompose portfolio returns into their component sources of risk as they exist in the portfolio. Portfolio returns are regressed again the returns of the risk variables which accomplishes the separation need to isolate alpha. Any return that is not separated and associated with a risk exposure is real alpha. The results of several such regressions are presented in Exhibit 3. Four risk models are estimated beginning with the basic CAPM (model 1) and three Fama-French factors are added sequentially (models 2,3,4). Note that as risk variables are added, alpha decreases from 6.1% to 1.8% as it is essentially being reassigned to the risk exposures actually embedded in the portfolio.
- YES. While not intuitively obvious, the way that academics conduct research is designed to illustrate or test a theory and hopefully get the results published. The studies are definitely not designed to illustrate how investors should implement their significant results. Be aware of the following potential sources of discrepancies in the performance of actual portfolios and the academic results that are published. First, academics may not account for the costs of implementation including management fees, transactions costs, and taxes. Second, practitioners are generally interested in large and mid-cap universes for reasons of investibility. Academics may construct their studies to encompass all capitalization classes. Therefore, their results may be a function of the proportion of smaller capitalization ranges included in the study. Academic studies are often unconcerned about industry exposures and, for the most part, they utilize equal weighting schemes. And while the studies are very concerned with making risk adjustments to their results (i.e., determining the presence of “alpha”) they are not concerned with the practical requirements of maintaining a particular active risk stance in the portfolio. Factor studies generally construct portfolios using dollar and market neutral long-short positions to test their theories. And although practitioners may also construct long-short portfolios, they are often constrained by practical considerations such as the need to control active risk exposures and the need to maintain asset allocations. Finally, differences in how factors are measured (single component factors vs. multiple components) may produce more or less robust results.
Why does it matter?
When investors are choosing among managers and the various products offered, the comparison of alpha and beta can be tricky. Are the factors constructed similarly across managers and consistent with respect to implementation costs? Is the manager delivering a portfolio of factor tilts and is that consistent with the investment objective? Can any of the differences in performance relative to published research on factors be explained by the factor and portfolio construction process? Ultimately the investor will want to distinguish between managers offering a portfolio of factor tilts vs. managers delivering real alpha.
The most important chart from the paper
A growing number of investors have come to view their portfolios (especially equity portfolios) as a collection of exposures to risk factors. The most prevalent and widely harvested of these risk factors is the market (equity risk premium); but there are also others, such as value and momentum (style premia). Measuring exposures to these factors can be a challenge. Investors need to understand how factors are constructed and implemented in their portfolios. They also need to know how statistical analysis may be best applied. Without the proper model, rewards for factor exposures may be misconstrued as alpha, and investors may be misinformed about the risks their portfolios truly face. This paper should serve as a practical guide for investors looking to measure portfolio factor exposures. We discuss some of the pitfalls associated with regression analysis, and how factor design can matter a lot more than expected. Ultimately, investors with a clear understanding of the risk sources in an existing portfolio, as well as the risk exposures of other portfolios under consideration, may have an edge in building better diversified portfolios.
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