The quality factor originally was designed as a tool to gain confidence in the financial strength of dividend-paying equities, but investors have steadily evolved how they use it in their portfolios. Instead of looking at dividend-paying equities as a means to meet income needs, quality dividend payers can also be looked at as a means to grow assets through their core investments. Similarly, combining the quality factor with yield may make sense from a total return perspective, since dividend yield can be looked at as a way to gain exposure to the value factor.
Yet, we realized that even though a quality dividend strategy can be used to help meet capital appreciation goals, its application has certain limitations. Key among these is the exclusion of non-dividend payers from the starting universe of investable equities.
Originally, when we explored applying our dividend quality score (DQS) methodology beyond common stocks, we determined that each asset type required distinct modifications to the template. Using a cookie-cutter approach across asset types would not work.
For example, we realized that the unique aspects of real estate investment trusts (REITs) required a different application methodology than was needed for common stocks. REITs, because of their tax requirement to pass on the majority of their earnings directly to their unit holders, did not produce the same financial data as common stocks, such as net income. So we reexamined the metrics driving the DQS score and determined that we needed to modify the methodology for the REIT space. We use the resultant real estate quality score (REQS) in our global quality real estate strategy FlexShares Global Quality Real Estate Index Fund (GQRE).
All of these processes rely on the same foundational principles, but recognize that each asset type/class is unique — so much so that, to fruitfully apply our quality approach, each quantitative implementation has to be modified.
Applying quality to a non-dividend-paying large cap universe is a natural extension of our product suite and one that we believe enables us to better address the specific goals of large cap equity investors.
We think the domestic large cap space is exceptionally fertile ground for using our proprietary multi-factor quality approach. The extensive depth and history of financial data for these companies makes research quite robust, which in turn boosts convictions about how different factors perform in various market cycles. And, liquidity and capacity issues, which may affect small caps or certain foreign markets, are generally not a concern.
This progression also makes logical sense when you consider the pillars that drive our FlexShares quality scoring process:
We apply this methodology to companies on a sector basis in order to:
We believe applying these metrics is not only a great way to judge a company’s ability to maintain and grow its dividend, but also to gain insight into its overall financial health — and that holds true for dividend and non-dividend payers alike.
Quality-focused investors who want a total return strategy that stacks up against other large cap core options may find that the quality dividend strategy has too much mid-cap exposure, which could result in periods of performance drift versus large cap competitors. A large-cap-only quality strategy that includes non-dividend payers would access the full opportunity set and easily work with traditional investor core allocations and risk frameworks.
We realized that when the investment objective changes from a focus on yield to one of capital appreciation, it also changes the way the strategy seeks to capture the compensated risk factor, freeing up the design to examine other factors. For example:
Additionally, for tax-sensitive clients, maximizing dividend yield instead of value can be a less effective way to capture returns, because dividend income creates a potential tax obligation that must be paid in the year earned. In contrast, capital appreciation represented by unrealized rising asset prices can be flexibly managed by the investor vis-à-vis timing. This is an important distinction for investors seeking total return but not necessarily income.
While it may look deceptively simple, a lot of care went into determining the parent large cap universe. All risk controls and diversification constraints relate to this starting point. Our goal was to create an “investability” universe that tracks closely to legacy large cap benchmarks while also allowing the most robust opportunity set for our quality process.
Why not just start with a legacy benchmark? We examined legacy benchmarks and proxies and believed we could do better. We thought that a more robust starting universe would be advantageous if it allowed more dispersion in returns between companies with attractive factor exposures. Also, it would help limit concentrations in sectors with fewer qualifying constituents, such as telecom, utilities and materials.
We examined several different breakpoints for the starting universe. In the end, we chose the largest 600 companies that passed our investability screens. This starting universe provides the greatest number of securities for us to evaluate, while keeping tracking error, sector and style bets to a minimum.
Combining Quality and Value
While quality is a powerful factor on its own, we believe combining it with value makes each factor even stronger. By focusing on large caps and looking beyond dividend payers, a robust definition of value that combines several metrics can be used.
We believe the combination of these different lenses of valuation informs a stronger understanding of how a company’s stock price is valued today, in the future and with respect to historic norms.
Combining Quality and Value with Momentum
Targeting undervalued large-cap companies that exhibit financial strength also provides a strong foundation on which to build a core investment strategy. However, by utilizing market-sensitive data (not just company-reported data), the model may be further improved to help avoid value traps. We use momentum to measure the market sentiment for the company as it is reflected in the stock’s price. We evaluate momentum in two ways:
Again, similarly to our quality process, we evaluate three layers of metrics:
Combining All Three Factors
While each of these three factors works well in isolation, the combination of the factors — and how they are combined — are just as important. We selected the three factors and determined the process for combining them quite deliberately. Each step has distinct and paired significance. Two factors that are lowly correlated or even negatively correlated can have a powerful effect on one another, and their combination typically smooths the cycle for each factor, dampening the swings that each would experience in isolation. Also, each is additive in achieving the objective of the fund, something that is often overlooked.
The goal of diversifying different factors is similar to that of diversifying a basket of stocks that are not highly correlated: both processes seek more desirable performance on a risk-return basis. Historically, value and momentum have negative correlation to one another.
The fact that low correlations between the factors may be beneficial when combined informs the decision on how best to conjoin the factors. We believe that the most effective way to capture factor exposures is by applying all three factors to the starting universe at the same time. Because of their low individual correlations, applying the factors separately then combining the results may actually dilute their combined effectiveness (i.e., you could end up with some exposures to poor quality, overvalued and negative momentum holdings.)
The Large Cap Quality measurement process creates an alpha score based on the three targeted factors, which maximizes the strategy’s exposure to quality, value and momentum. The strategy strives to harness each factor through its selection and weighting process. Additionally the strategy seeks to limit non-targeted factors by placing sector, security, style and market volatility bounds relative to the starting universe.
QUALITY LARGE CAP INDEX PROCESS
This simple but powerful investment process allows the strategy to focus on those traits that we find the most attractive, while it minimizes unintended exposures that are not part of the design. It also has the potential to make capturing the factor more efficient, since the generated tracking error supports the pursuit of compensated risks rather than non-targeted, idiosyncratic risks. We believe that the FlexShares US Quality Large Cap Index Fund (QLC) can replace a legacy large cap strategy in an existing core allocation model with few follow-on adjustments needed to the model’s sector, style and stock exposures. This ease of implementation may help minimize the time and expense associated with portfolio reconfiguration.
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The FlexShares approach to index-based investing is, first and foremost, investor-centric and goal oriented. We pride ourselves on our commitment to developing products that are designed to meet real-world objectives for both institutional and individual investors. If you would like to discuss the attributes of any of the ETFs discussed in this report in greater depth or find out more about the index methodology behind them please don’t hesitate to call us at 1-855-FlexETF (1-855-353-9383).
Dividend yield for an equity security is calculated using the (expected) annual dividend divided by the most recent closing price. To calculate annual dividend, we utilize the most recent dividend payment and multiply that payment by the expected frequency over a year. For example, if the most recent dividend for a company is $2.50 and dividends are expected to be paid quarterly (4 dividend payments/year), the annual expected dividend would be calculated as $2.50 x 4 dividend payments/year, or $10.
Tracking error is a divergence between the price behavior of a position or a portfolio and the price behavior of a benchmark.
Price-to-earnings (P/E) ratio is a valuation ratio calculated as a company’s current stock price divided by its earnings per share.
Shiller’s CAPE (cyclically adjusted P/E) – also known as the P/E 10 ratio – is a valuation measure, generally applied to broad equity indices, that uses real per-share earnings over a 10-year period. The P/E 10 ratio uses smoothed real earnings to eliminate the fluctuations in net income caused by variations in profit margins over a typical business cycle. The ratio was popularized by Yale University professor Robert Shiller, who won the Nobel Prize in Economic Sciences in 2013. It attracted a great deal of attention after Shiller warned that the frenetic U.S. stock market rally of the late-1990s would turn out to be a bubble.
Correlation is a statistical measure of how two securities move in relation to each other. Correlations are used in advanced portfolio management.
Alpha is a measure of performance on a risk-adjusted basis. Alpha takes a fund’s volatility (price risk) and compares the risk- adjusted performance to its benchmark index. The fund’s excess return relative to the benchmark’s return is its alpha.
An investment in FlexShares US Quality Large Cap Index Fund is subject to numerous risks, including possible loss of principal. Fund returns may not match the return of the respective indexes. The Funds are subject to the following principal risks: asset class; concentration; counterparty; derivatives; equity securities; issuer; large cap; management; market; market trading; momentum; new fund; passive investment; tracking error; valuation; and value investing risk. A full description of risks is in the prospectus.
FlexShares US Quality Large Cap Index Fund (QLC) is is passively managed and uses a representativesampling strategy to track its underlying index. Use of a representative sampling strategy creates tracking risk where the Fund’s performance could vary substantially from the performance of the underlying index. Additionally, the Fund is at increased dividend risk, as the issuers of the underlying stock might not declare a dividend, or the dividend rate may not remain at current levels. The Fund is also is at increased risk of industry concentration, where it may be more than 25% invested in the assets of a single industry. Finally, the Fund may also be subject to increased volatility risk, where volatility may not equal the target of the underlying index.
The FlexShares Credit-Scored US Corporate Bond Index Fund (SKOR) and the FlexShares Credit- Scored US Long Corporate Bond Index Fund (LKOR) are subject corporate bond risk, which is the risk that the issuer is unable to meet principal and interest rate payments on the obligation and may also be subject to price volatility due to such factors as interest rate sensitivity, market perception of credit worthiness of and general market liquidity. When interest rates rise, the value of corporate debt can be expected to decline. The Funds may invest in derivative instruments. Changes in the value of the derivative may not correlate with the underlying asset, rate or index and the Funds could lose more than the principal amount invested. The Funds are also non-diversified meaning the Fund’s performance may depend on the performance of a small number of issuers because the Funds may invest a large percentage of assets in securities issued by or representing a small number of issuers.
FlexShares Global Quality Real Estate Index Fund (GQRE) is subject to real estate sector risk in addition to the general risk of the stock market. Investing in securities of real estate companies will make the Fund more susceptible to risks associated with the ownership of real estate and with the real estate industry in general, as well as risks that relate specifically to the way in which real estate companies are organized and operated. Real estate companies may have lower trading volumes and may be subject to more abrupt or erratic price movements than the overall securities markets. The value of real estate securities may underperform other sectors of the economy or broader equity markets. To the extent that the Fund concentrates its investments in the real estate sector, it may be subject to greater risk of loss than if it were diversified across different industry sectors. The Fund is also subject to the risk that its investments will be affected by factors that impact REITs and the real estate sector generally. Investing in REITs involves certain unique risks in addition to those risks associated with investing in the real estate industry in general. REITs whose underlying properties are concentrated in a particular industry or geographic region are also subject to risks affecting such industries and regions. By investing in REITs through the Fund, a shareholder will bear proportionate expenses of the REITs in addition to expenses of the Fund.
FlexShares Quality Dividend Index Fund (QDF), FlexShares Quality Dividend Defensive Index Fund (QDEF) and the FlexShares Quality Dividend Dynamic Index Fund (QDYN) are passively managed and use a representative sampling strategy to track their underlying index. Use of a representative sampling strategy creates tracking risk where the Fund’s performance could vary substantially from the performance of the underlying index. Additionally, the Funds are at increased dividend risk, as the issuers of the underlying stock might not declare a dividend, or the dividend rate may not remain at current levels. The Funds are also is at increased risk of industry concentration, where it may be more than 25% invested in the assets of a single industry. Finally, the Funds may also be subject to increased volatility risk, where volatility may not equal the target of the underlying index.