Factor-based hedging and exposure strategies
Investor awareness of the importance of equity factors has been growing since the early 1990s, when carefully conducted studies by respected academics validated earlier research and investment strategies.
Some factor exposures, such as size, growth and value, have long been accessible though active and passive products. Other factors have historically been relatively difficult and costly to access.
It is getting easier, however, to obtain once "exotic" factor exposures, and many new indices have been introduced to do this. In this article, we aim to show the potential for the use of factor index-based investment products in implementing hedging and exposure strategies. We use the Russell-Axioma Developed ex-U.S. Large Cap Long-only indices for the purposes of illustration. These indices are similar in design to the Russell-Axioma U.S. Long-only Indices which cover US large-cap and small-cap equities.
ABOUT FACTORS AND FACTOR INDICES
"Factors" are the drivers of the consistent performance patterns observed in equity markets—the drivers that affect the risk and returns of stocks. For example, researchers have observed that the returns of a stock are systematically influenced by its size, or market capitalisation. The "size factor" identifies this effect, and indices can be constructed to isolate specific factors. Standard practice, pioneered by Russell, is to construct separate large-cap and small-cap indices. These indices provide representations of the large-cap and small-cap investment opportunity sets and index proxies for the aggregate behaviour of large- and small-cap stocks.
Style indices, such as the Russell Developed ex-U.S. Large Cap Growth and Value indices, can also be considered to deliver exposures to "style factors" (here, as the names indicate, growth and value).
After growth and value, momentum is the most widely studied equity factor. High-momentum stocks have historically tended to outperform low-momentum stocks, on both an absolute and a risk-adjusted performance basis. This phenomenon has been the subject of academic research for almost 20 years.2 High momentum is one of the Russell-Axioma factor indices.
The four other current types of Russell-Axioma factor indices are based on volatility and beta. The total volatility of a stock can be broken down into two components: systematic and idiosyncratic volatility. Beta is the common name for systematic or market-driven volatility. Idiosyncratic volatility is stock-specific volatility. Stocks with high betas tend to have high idiosyncratic volatility as well. However, stocks with low betas may still have high idiosyncratic volatility.
Classical financial theory, based on the Sharpe Capital Asset Pricing Model ("CAPM"), predicts that the expected return of a stock should be proportional to its beta.
According to this theory, idiosyncratic volatility is predicted to have no effect on expected return because it is a risk that can be diversified away.
Considerable evidence has accumulated in recent years, however, to show that beta is in fact a poor predictor of expected returns and that idiosyncratic volatility does reduce expected returns.3 Indeed, the performance of the Russell-Axioma factor indices is consistent with this research. Over significant holding periods, the low-beta and low-volatility indices have outperformed the high-beta and high-volatility indices.4
Factor indices provide an enhanced exposure to particular factors either by selecting constituents that exhibit high correlation with that factor, or by increasing the relative weighting of such constituents in the index. Apart from standard "long-only" indices, it is also possible to create market-neutral factor indices that provide exposure purely to the factor return. This is achieved by buying a group of stocks (usually from an underlying universe) that have high factor scores, while simultaneously selling short a similarly sized group with low factor scores.
Russell-Axioma's Long-only Developed ex-U.S. Large Cap and U.S. Factor Indices (Long-only) use optimisation to track a target index while minimising turnover and exposures to non-targeted factors. The term "non-target factor exposures" refers to exposure imbalances, which are defined relative to a parent index. Style, sector, industry, currency or country exposure imbalances are possible.