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For several decades, market capitalisation-weighted indices, like the MSCI All Country World (ACWI) index or the S&P 500 index, have served as the foundation of a passive approach to investing. Many investors have long viewed these indices as an efficient way to gain broad exposure to a wide variety of equity markets. Yet today more advanced approaches to index construction may help improve on this traditional approach.
Defining factors and smart beta
A factor is a primary characteristic of a security that helps explain its behaviour over long periods. Factors have been studied for years by economists and finance theorists and some have been proven to be more rewarding than others. Foundational to factor investing is the definition of each factor – in other words the fundamental stock characteristics chosen to capture it. Depending upon the definition, two seemingly similar factors can deliver different results. For instance, the quality factor can be defined by applying different metrics, as we will later discuss.
Smart beta exchange-traded funds (ETFs) can allow investors to benefit from the many positive attributes of both traditional passive (ie, capitalisation-weighted) and active strategies. By redesigning a traditional index to tilt towards specific factors, a smart beta index can be actively designed to achieve a particular outcome. Delivered in an ETF, the smart beta product provides transparency and clarity into daily characteristics and holdings.
From very simple (single factor) to more complex (multi-factor) structures, investors use smart beta ETFs to seek a variety of outcomes, such as risk-adjusted returns or better performance. Our belief is that a multi-factor smart beta strategy focused on a combination of four factors – high quality, attractive value, strong momentum and low volatility – should lead to strong risk-adjusted returns over time.
European institutions increasingly adopting smart beta
Smart beta ETFs allow institutional investors to benefit from the effectiveness of factor investing. In fact, European institutions are leading the adoption of smart beta strategies when compared with North American or Asian investors, according to a recent survey from FTSE Russell1. In Europe, assets in smart beta ETFs have risen dramatically, with a compound annual growth rate of 45% per year since 2008, to reach a total of €42bn in assets under management, as highlighted in figure 1.
This rapid development may be explained by the unintended risks of investing in a market capitalisation-weighted index, such as concentrations in large-cap stocks and expensive stocks. These risks can be illustrated by two examples:
- Large-cap bias: the largest 2% of stocks in the MSCI ACWI index represent 26% of the benchmark’s market cap.
- Overvaluation bias: A market capitalisation-weighted benchmark gives a strong tilt towards overvalued stocks: the 10 largest stocks within the MSCI ACWI Index are 72% overvalued2.
These limitations can have a meaningful impact on investor outcomes, which is why we believe a different approach to index construction may better meet investor needs.
Single-factor or multi-factor ETFs?
Smart beta ETFs have evolved since the first single-factor ETFs appeared back in 2003. Investors today can choose from exposure to a single-factor or a multi-factor approach that combines factors in an equal or custom-weighted way (see figure 2). While all strategies offer their own merits, we believe that a multi-factor smart beta strategy with custom-factor weightings may provide improved long-term, risk-adjusted returns with better downside protection.
The importance of factor weights and factor measures
The quality, value, momentum and low-volatility factors each have the capacity to contribute positively to performance, but they tend to perform differently depending on market conditions. As we can see in figure 3, individual factor performance has varied considerably from year to year over the past decade as each factor has swung in and out of favour. Given this variability in performance, it may be difficult for investors to predict which factors would be in favour in a particular period. Even if such predictions were possible, frequently switching from factor to factor could increase an investor’s transaction costs; moreover, it would be time-consuming to execute. Given these considerations, investors may instead wish to consider combining factors into a single investment.
Franklin Templeton’s systematic modelling team has conducted proprietary research to identify a mix of factors that could serve as an attractive component for an investor’s core portfolio. As stated before, the definition of a given factor has a direct relation to its performance. We have therefore researched factor definitions and created a custom approach to factor measurement as we often find that smart beta strategies follow standard definitions.
In summary, our smart beta methodology focuses on both factor measures and factor weights.
Reconsidering factor definitions: custom measures
While many smart beta strategies utilise factors such as quality, value, momentum and low volatility, many also employ standard, widely accepted approaches to measuring these factor exposures. We sought to develop custom factor measures, which we believe could provide a more comprehensive evaluation of a stock’s exposure to each factor, as described below and summarised in figure 4.
l Quality factor: There are varying definitions of quality in the marketplace, with some definitions using just a single metric to capture it. Our measurement criteria seek to replicate traditional financial statement analysis. We have a comprehensive definition of quality that incorporates multiple dimensions, such as high profitability, strength of balance sheets from a debt quality perspective, efficiency in the use of assets, and low volatility of earnings.
l Value factor: Franklin Templeton’s measurement of value applies a blend of trailing and forward price/earnings ratios because we believe it provides a more nuanced calibration of a firm’s outlook from a valuation perspective. In addition, we utilise another metric – dividend yield – which others often consider a stand-alone factor, but we include as one of our criteria.
l Momentum factor: Some investors seek stocks with strong momentum in order to help avoid value traps. We take a prudent approach and measure this factor as the average of a firm’s momentum over the prior six and 12 months.
l Low-volatility factor: Some investors look to low-volatility stocks to defend against potential market downturns. Stocks that demonstrate lower than average variability of returns are often considered low-volatility stocks.
Reconsidering factor weights: not all factors are created equal
When we look at factor weightings in a multi-factor smart beta ETF, our approach as fundamental stock-pickers is driven by the following beliefs. First, factor weightings should be rooted in economic rationale, as best represented by the quality and value factors. Second, exposure to momentum may help identify investment trends and avoid value traps. Third, exposure to low volatility may help provide defence against market downturns.
Weighting each factor equally would clearly be the simplest approach to constructing a multi-factor smart beta index. However, as many investors recognise today, not all factors are created equal.
As we considered the relative weightings of quality and value, we noted that our custom approach to the quality factor clearly produced higher risk-adjusted returns than the value factor, with improved information ratios of 0.45 and 0.13, respectively. Consequently, we have assigned a 50% weighting to the quality factor and a 30% weighting to the value factor. We believe these weightings tilt sufficiently towards quality to make a meaningful difference in returns over time while also retaining balance and helping to ensure diversification.
Momentum and low volatility also play important – if smaller –roles in Franklin Templeton’s multi-factor approach, with each assigned a 10% weight. Momentum may help identify investment trends and avoid value traps, while low volatility may help provide a defensive measure against market downturns.
We then examined performance characteristics of two different scenarios that use Franklin Templeton’s custom factor measures – one that combined factors in equal weights, and a second that combined factors based on the strategic weights outlined above – and compared them to the MSCI ACWI index. The results of our analysis, presented in figure 5, yield two key insights for the 10-year period:
- Combining Franklin Templeton’s custom factors in equal weights improved hypothetical risk-adjusted returns (as measured by the Sharpe ratio) versus the MSCI ACWI Index.
- Combining Franklin Templeton’s custom factors using the strategic weights – 50% Quality, 30% Value, 10% Momentum and 10% Low Volatility – yielded even stronger hypothetical absolute and risk-adjusted returns.
Smart beta ETFs designed for stronger core portfolios
As investors seek out smart beta as a way to improve on the inefficiencies of traditional index investing, or to complement and diversify within their portfolios’ active management segment, it’s imperative that they understand the methodology behind the strategy.
Our research set out to determine whether our factor methodology refinements could yield market returns with lower risk than the cap-weighted index. The results were encouraging: we learned that rigorous definitions of factors, as well as factor weights, had a positive impact on overall returns. Moreover, they yielded improved hypothetical returns – both on an absolute and a risk-adjusted basis.
Leveraging multi-factor solutions enables investors to minimise the cyclicality associated with single-factor investing, and helps them to avoid trying to time the market. We believe that customised factor definitions and weights may be combined to seek better risk-adjusted returns over the long term.
Chandra Seethamraju is director of systematic modelling at Franklin Templeton Investments
1 Source: FTSE Russell, Smart Beta: 2017 Global Survey Findings from Asset Owners, June 2017
2 Based on current price-to-earnings (P/E) vs 10-year average P/E, 1 January 2007–31 December 2016.
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