Investor Thinking - Blending equity factors to target better investment outcomes
Smart Beta has become a growing area of interest among UK pension schemes looking to improve on the long-term returns offered by traditional market cap equity indices. These Smart Beta strategies are often structured to capture various factors, such as Value, Momentum, Quality or Low Volatility. Since each factor has a different long-run risk and return outcome, pension schemes can use well-designed Smart Beta strategies to access factors and align their equity investments with their target outcome. In this paper, our Rosenberg Equities team highlights the performance patterns of these factors and explores how pension schemes can further manage their risk and return outcomes by blending exposure to more than one factor.
The rationale for Smart Beta investing
Market cap weighted indices have been the traditional way of investing in equities and the default approach for measuring the returns and risk that equities, as an asset class, offer. Today however most investors are aware of the pitfalls of a market cap weighted approach. The key failing of a market cap weighted approach is that prices determine a stocks’ weight in the index and stock prices are often not rational. Stock mispricing occurs for many reasons: investors have different information, circumstances and objectives. Most influential of all in our view is that investors are subject to behavioural flaws, such as fear and greed, loss aversion and herding. It is these inefficiencies that many believe ultimately explain the existence of the various factors that are well documented in academic and industry literature, such as Value, Momentum and more recently Low Volatility and Quality. The long-term returns and volatility for the Value, Momentum, Quality and Low Volatility factor portfolios compared to the overall market is shown in Table 1 overleaf, based on data calculated by our Rosenberg Equities team1 .
As Table 1 shows, the four factors have all delivered superior historical long-term returns compared to the market cap index. Momentum and Value factors have demonstrated the highest rates of return, but have the highest levels of volatility or total risk. Low Volatility and Quality factors exhibit lower levels volatility with somewhat lower levels of excess return.
It is our view that the reason the above four factors offer long-term superior performance compared to market cap indices is that they avoid investor behavioural flaws and instead provide a better connection with the fundamental characteristics of companies that ultimately drive a stock’s risk and return, namely corporate earnings. While the investment ideas underpinning factors aren’t new, what is new is that pension schemes can now access these factors in a more direct, transparent, and efficient way using well-designed Smart Beta strategies.
Blending risk factors to reduce dependency on a single source of return
The returns shown in Table 1 are annualised averages over more than 25 years, clearly a very long investment horizon. Over shorter time horizons an individual factor may suffer from periods during which it is not in favour. For example, Low Volatility delivered weak relative returns between 1993 and 2000 and Value has been out of favour for much of the past five years. This is the reason why all of the factors shown in Table 1 have non-trivial levels of tracking error compared to the market. Long periods of weakness may not suit the objectives of a pension scheme. Furthermore while pension schemes are long-term investors, downside volatility can lead to declining funding levels, which in turn can lead to increased contributions if these losses occur during a valuation period. So, instead of relying on the long-term risk and return characteristics of a single factor, pension schemes might look for a blended solution. By blending factors investors can reduce dependency on one source of return and further manage and target the risk and return outcomes offered through Smart Beta investing.
Finding the right blend
Understanding likely performance patterns of different factors is an important aspect of selecting the right blend. In our view a useful framework for analysing performance patterns is to look at each factor’s behaviour in the context of the corporate earnings cycle. To do this, we split the earnings cycle into four stages:
- Earnings expansion > Early cycle – Bull Market: Earnings are growing rapidly and equity market prices are rising.
- Earnings expansion > Mid/Late cycle – Bull Market: Earnings are growing but the rate of change is steady or slowing. Equity market prices are rising.
- Earnings recession – Bear Market: Earnings are in recession (falling by 20% or more) and equity market prices are falling.
- Recovery Market: Earnings are in recession (falling by 20% or more) but equity market prices are rising in anticipation of recovery. Since 1990 we have experienced three full earnings cycles. In Figure 1 we plot when each of the phases started and finished.
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