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Using GARP Strategies for Indices

Key Takeaways from 2019 Australian Indexing & ETF Masterclass

S&P 500® Dividend Aristocrats®: Risk Decomposition and Sector Composition

S&P 500 Cboe Target Outcome Indexes – Investor Applications

Finding Better Beta in the International Small-Cap Markets

Using GARP Strategies for Indices

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Bill Hao

Director, Global Research & Design

S&P Dow Jones Indices

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In this blog (and three subsequent posts) we will explore using Growth at a Reasonable Price (GARP) principles for investment in indices. GARP is a well-known, much-practiced, fundamental-driven investment strategy. It seeks to balance between the pure growth strategy and pure valuation strategy, as the former tends to chase high growth yet expensive stocks, while the latter can take years to realize. At the heart of it, in practice, the GARP strategy seeks to invest in companies with the following characteristics: consistent earnings and sales growth; reasonable valuation; and solid financial strength combined with strong profitability.

The construction of a GARP strategy combines growth, value, and quality factors into one process. A rules-based, transparent index solution shows the details. In this blog, we will outline the framework and the steps involved.

Establishing the Multi-Factor Framework

We use a systematic bottom-up approach for stock selection and portfolio construction. As illustrated in Exhibit 1, we can summarize the steps as follows.

  • Define your investment universe.
  • Identify factors with the potential to capture your investment strategy.
  • Select sensible factors for multi-factor metrics.
  • Select constituents with well-defined rules.
  • Construct a constituent portfolio with a predefined weighting methodology.

For our S&P 500® GARP Index, we use the S&P 500 as the investment universe and a sequential filtering approach to select constituents. The objective of a GARP strategy is to invest in growth stocks with good quality and attractive valuation. Exhibit 2 shows the appropriate style and factor components[1] for the GARP strategy.

We use three-year EPS[2] and SPS[3] growth metrics to capture a firm’s growth. In order to maintain sustainable growth, a firm needs to be highly profitable (high ROE[4]) and not built on excessive leverage (low financial leverage ratio[5]). Moreover, we use the earnings-to-price ratio[6] to gauge a firm’s reasonable valuation. With these metrics, we implement a hypothetical GARP strategy.

Style and Factor Score Calculation

Score calculation is an integral step of any multi-factor strategy. We calculate the growth z-score and QV composite z-score for each of the stocks in the eligible universe. The growth z-score[7] is calculated as the winsorized z-score[8] average of two factors: the three-year EPS growth and three-year SPS growth. If a z-score for one factor is not available or properly calculated, the z-score of the other factor will be used as the growth z-score.

Next, we compute the QV composite z-score as the winsorized z-score average of three factors: financial leverage ratio,[9] ROE, and earnings-to-price ratio. A stock needs to have a quality and a value score. If the z-score for one of the quality scores can’t be properly calculated, the z-score of the other quality factor will be used.[10]

With style and factor scores in place, we proceed to the third step of the multi-factor investment process—security selection. Here we utilize a sequential filtering process, one of the several possible approaches to forming a multi-factor index.

In the next blog, we will take a deeper dive into the third and the fourth steps in our GARP strategy construction.

[1]   Outlier fundamental ratios are winsorized to ensure that the average values used to calculate the overall component score are less distorted by extreme values. For a given fundamental variable, the values for all securities are first ranked in ascending order. Then, for securities that lie above the 97.5 percentile rank or below the 2.5 percentile rank, their value is set as equal to the value of the 97.5 percentile ranked or the 2.5 percentile ranked security, whichever is applicable.

[2]   The three-year EPS growth is calculated as a company’s three-year EPS compound annual growth rate (CAGR) for each security in the index universe as of the rebalancing reference date.

[3]   The three-year SPS growth is calculated as a company’s three-year SPS CAGR for each security in the index universe as of the rebalancing reference date.

[4]   The ROE is calculated as a company’s trailing 12-month EPS divided by its latest BVPS for each security in the index universe as of the rebalancing reference date.

ROE = EPS/BVPS

[5]   The financial leverage ratio is calculated as a company’s latest total debt divided by its book value per share (BVPS) for each security in the index universe as of the rebalancing reference date.

Leverage = Total Debt/(BVPS * Common Shares Outstanding)

[6]   The earnings-to-price ratio is calculated as a company’s trailing 12-month EPS divided by its price (P) for each security in the index universe as of the rebalancing reference date.

Earnings-to-Price = EPS/P

[7]   Using the winsorized growth z-score, a growth score is computed for each of the securities. For a given security, if its winsorized growth z-score is above 0, then its growth score will be 1 plus the winsorized growth z-score. On the other hand, if its winsorized growth z-score is below 0, then its growth score will be the result of the reciprocal of 1 subtracts its winsorized growth z-score.

If average z > 0, Growth Score = 1 + z.
If average z < 0, Growth Score = (1 / (1 – z)).
If average z = 0, Growth Score = 1.

[8]   Computing a z-score is a widely adopted method of standardizing a variable in order to combine it with other variables that may have a different scale or unit of measurement. After winsorizing all the fundamental ratios, the z-score for each of the relevant ratios for each security is calculated using the mean and standard deviation of the relevant variable within each of the index universes.
In general, the z-score is calculated as follows:

??= -(??−??)/??

For each security, the average z-score is computed by taking a simple average of the relevant scores. Where there is a missing value, the average z-score is computed by taking a simple average of the remaining scores. A security must have at least one z-score for it to be included in the index.

[9]   The z-score for the financial leverage ratio is calculated as follows: ??= -(??−??)/??
where:

?? = z-score for a given security
?? = winsorized variable for a given security
?? = arithmetic mean of the winsorized variable in a given index universe, excluding any missing values
?? = standard deviation of the winsorized variable in a given index universe

[10] Outlier average z-scores are winsorized to ensure that the overall growth scores are less distorted by extreme values. To do this, for a given average z-score, the values for all securities are first ranked in ascending order. Then, for securities that lie above 4 or below -4, their value is set as equal to 4 or -4, whichever is applicable.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Key Takeaways from 2019 Australian Indexing & ETF Masterclass

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Stuart Magrath

Senior Director, Channel Management, Australia and New Zealand

S&P Dow Jones Indices

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In early March over 265 financial advisers came together in Melbourne and Sydney for S&P Dow Jones Indices’ 9th Annual Indexing and ETF Masterclass. This half day Masterclass provided advisers with the opportunity to hear from, and interact with, both local and international peers, S&P DJI subject matter experts, as well as leaders in the ETF industry in Australia.

The timing of this year’s Masterclass, just one month after the findings of a Royal Commission into Misconduct in the Financial Services industry could not have been better. The Royal Commissioner, Kenneth Hayne, made 76 recommendations for change across the industry, with a number of those recommendations affecting the financial advice industry. Perhaps the most significant recommendation is for the removal of all commissions, including those commission arrangements that were subjected to grandfathering under the Future of Financial Advice reforms earlier this decade.

Delegates at the Masterclass heard how shifting an advice business from being a stock-picking practice, or a practice that tries to select the best and most successful active fund managers, to a practice that focuses on asset gathering and asset allocation, sets advisers up for greater success. This is where ETFs come into their own, as they are perfect vehicles by which to express that asset allocation. With the increasing number of ETFs now listed on the ASX, and with increasing AUM, ETFs are the perfect tool by which to achieve great client outcomes.

At Masterclass this year, we were privileged to have Geoff Eliason from Peak Capital Management, and Registered Investment Adviser (RIA) firm based in Denver, Colorado join us. Geoff delivered the keynote address as well as participating in a panel discussing S&P DJI’s custom indexing capabilities.  Geoff’s firm has developed an algorithm that uses ETFs to hedge downside risk within portfolios, while participating in most of the upside. To keep Geoff’s firm honest, and to ensure that they follow the algorithm, Peak Capital commissioned S&P DJI to develop four custom indices that reflect the investment philosophy behind four of Peak Capital’s funds.  Investors with Peak can easily compare the performance of their investments against the independently calculated custom index, giving them the assurance that Peak Capital stays true to their algorithm.

A highlight of Masterclass this year was having three alumni from the annual S&P DJI Financial Adviser tour to the United States. One delegate from 2017, who has developed his own ETF model portfolios, is moving towards having custom indices developed for those portfolios so that he can show his clients how portfolios are tracking against an independently calculated index.

The two other alumni discussed how off the back of their experience in the U.S. in 2017 and 2018, they returned knowing that they both needed to ‘rewire’ their practices away from stock-picking and active fund manager selection, to asset allocation and the use of ETFs as vehicles to express this allocation. Both advisers have been successful in transitioning client’s core holdings to ETFs while keeping some funds available for stock-picking.

The key message from this year’s Masterclass, in talking to advisers, is that they realize that it is difficult, if not impossible, to consistently outperform the market, either by selecting stocks, or selecting active managers. And this was supported with the timely release of the full-year 2018 SPIVA® Australia Scorecard that showed that a full 86.69% of actively managed Australian Equity General Funds were outperformed by the S&P/ASX 200 index. The result for actively managed Australian Bond funds was even more dire, with 98.36% of funds outperformed by the S&P/ASX Australian Fixed Interest 0+ Index.

To this end, Australian Financial Advisers are more and more inclined, and are in fact acting, to ensure their client’s core holdings are held in index-linked ETFs. This is music to our ears!

The posts on this blog are opinions, not advice. Please read our Disclaimers.

S&P 500® Dividend Aristocrats®: Risk Decomposition and Sector Composition

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Karina Tjin

Analyst, Strategy Indices

S&P Dow Jones Indices

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Today we examine the impact of the members of the S&P 500 Dividend Aristocrats Class of 2019 on the index’s factor risk and sector composition. In our previous blog, we introduced these new constituents and discussed their quality ranking.

Risk decomposition of the S&P 500 Dividend Aristocrats using Axioma’s US Fundamental Equity Risk Model MH4 supports the view that the index contains elements of the quality factor. Our definition of quality uses return on equity, accruals ratio, and financial leverage ratio. Of these three calculations, Axioma’s Risk Model decomposes risk into profitability (return on equity) and leverage. A snapshot of the index on Jan. 31, 2019, shows us that the index is more actively exposed to those two factors than is the S&P 500, with profitability’s active exposure at 0.04 and leverage’s at 0.13. Profitability’s contribution to risk is relatively small at 0.59%, and leverage reduces it by 0.33%.

Moreover, the average active exposure for the 10-year period studied was 0.33 and -0.27 for dividend yield and market sensitivity, respectively. In other words, the S&P 500 Dividend Aristocrats achieved higher dividend yield than its benchmark, and at the same time was less sensitive to the market. This shows us that the index was more quality oriented, had lower active exposure, and achieved higher yield than the benchmark.

Exhibit 2 shows that the S&P 500 Dividend Aristocrats had heavy Consumer Staples and Industrials sector biases. The Class of 2019 further shifts the bias toward the Industrials sector, from a weight of 20.8% to 21.6%. This is significant because half of the companies added to the index were from the Industrials sector and the index constituents are equally weighted (Consumer Staples index weight was 23.4%).

In the S&P 500, the Consumer Staples and Industrials sectors have much lower weights, of 7.2% and 9.5%, respectively. The weight difference between the benchmark and the S&P 500 Dividend Aristocrats suggests that the latter’s Consumer Staples and Industrials sectors may have more quality due to their greater ability to provide consistent dividends.  The new constituents of the S&P 500 Dividend Aristocrats Class of 2019 not only enhance the quality factor of the index, but also deepen the Industrials sector bias.

In our next blog we will discuss the historical performance of the S&P 500 Dividend Aristocrats.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

S&P 500 Cboe Target Outcome Indexes – Investor Applications

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Matt Kaufman

Principal and Senior Director, Head of Distribution and Product Development

Milliman Financial Risk Management LLC

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Many investments today target speculative returns, with uncertain levels of risk, over an uncertain period of time. While opportunistic, this approach to investing often brings a high degree of uncertainty. Outcome based investing encourages targeting a specific defined payoff profile, with an allowance for a specific defined level of risk, at a specific point in time in the future. Cboe S&P 500 Target Outcome Indexes work differently, by seeking to incorporate defined exposures to the S&P 500, where the downside protection levels, upside growth potential, and outcome period are all defined, prior to investing. This type of approach exhibits many applications for multiple investor types. We explore a few here:

  1. As a Growth Engine: Investors seeking enhanced growth with non-enhanced downside risk may consider replacing a portion of their domestic equity exposure with an Enhanced Growth Target Outcome Index.
  2. As a Risk Management Tool: Managing portfolio risk has historically been accomplished through fixed allocations to fixed income assets in order to meet a long-term risk tolerance level, or through more tactical measures that allocate away from stocks when markets become volatile. With Cboe Buffer Protect Indexes, the implied volatility of the S&P 500 is intended to contribute positively to upside caps. Conversely, in times of low implied volatility, upside caps tend to be lower. This feature is designed to reflect retained investment in the market through all market conditions (regardless of volatility). This is starkly different from most risk management techniques, which reduce equity exposure as volatility increases.
  3. To Meet a Downside Protection Goal: Risk tolerance levels are often established using long-term historical or stochastic volatility data; however, the downside risk investors actually experience over shorter periods may be remarkably different from their expected risk tolerance level. Target Outcome Indexes allow investors to know their downside protection level over an outcome period before they invest.
  4. A Measure of Gap Risk Protection: For many institutional investors, gap risk (the risk of a market movement from one level to another with no trading in between (e.g., the S&P 500 Index dropping substantially from the previous day’s close)) is difficult and expensive to mitigate. Because Target Outcome Indexes provide defined levels of protection over an outcome period, a measure of gap risk protection may be achieved, so long as the relevant investment product tied to the Index is held until the end of the outcome period.
  5. As a Complement to Guaranteed Lifetime Income: A successful retirement often includes multiple sources of income and risk management, which may include guaranteed lifetime income sources such as Social Security, pensions, and annuities. Target Outcome Indexes may be a complement to these income sources, providing liquidity and risk management to a retiree’s investment portfolio that might not otherwise be available.
  6. Take a View on the Market: Investors looking to take a tactical position on the market may often use enhanced growth strategies in modest bull or range-bound market environments that experience normal or elevated levels of volatility, and buffer protect strategies in bear or range-bound markets.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Finding Better Beta in the International Small-Cap Markets

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Hamish Preston

Associate Director, U.S. Equity Indices

S&P Dow Jones Indices

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S&P Dow Jones Indices recently launched the S&P Global SmallCap Select Index Series.  These indices aim to provide broad market exposure to small-cap equities around the world that have a track record of generating positive earnings.

As prior S&P DJI research highlighted, the S&P SmallCap 600® outperformed the Russell 2000 by around 2% on an annualized basis over the last two decades.  A large reason for this outperformance stemmed from the S&P 600’s significant exposure to quality; unlike the Russell 2000, the S&P 600 incorporates a positive earnings screen and so it has a greater bias towards profitable small-cap companies.

The newly launched small-cap select indices incorporate a similar positive earnings screen to that used in the S&P 600, and there is clear evidence it would have helped market participants seeking international small-cap exposure.  For example, all of the S&P Global SmallCap Select indices provided greater returns and lower volatilities compared to their less discerning counterparts over the last 15 years.  Since similar results are observed over shorter horizons, the performance characteristics suggest the select indices have raised the bar in small-caps and may serve as better benchmarks.

Another way to assess the effect of incorporating a profitability screen in the small-cap space is to switch benchmarks in our S&P Indices Versus Active (SPIVA®) scorecards.  Over the 15-year period ending December 2018, around three in every four managers lagged the S&P Developed Ex-U.S. Small Cap’s 7.5% annualized total return.  In contrast, around five in every six international small-cap funds underperformed the S&P Developed Ex-U.S. Small Cap Select’s 8.3% annualized total return.  Similar results were found in the U.S. small-cap category; nearly 90% of managers lagged the S&P 600’s 9.2% annualized gain over the 15-year period, whereas nearly 80% failed to beat the Russell 2000’s 7.5% annualized return.

As a result, incorporating earnings screens in the small-cap universe has been an effective way to eliminate unprofitable companies without sacrificing returns or resulting in higher volatility.  And while the majority of active international small-cap funds underperformed the traditional small-cap benchmark, an even higher proportion lagged the S&P Developed Ex-U.S. Small Cap Select.  Hence, market participants seeking broad market exposure to international small cap space may be better served by selecting this next generation of small-cap benchmarks.

 

 

The posts on this blog are opinions, not advice. Please read our Disclaimers.