Indexology^® Blog

In case you missed our country classification announcement on Dec. 5, 2018, we will be starting the process of adding China A-shares to S&P DJI global benchmarks (including the S&P China BMI and S&P Emerging BMI) in September 2019 at a partial inclusion factor of 25%.

Given the size of China’s onshore equity market, the addition of China A-shares is a landmark event for the S&P Global BMI. Based on total market capitalization—unadjusted for foreign ownership limits and float adjustment—China is the world’s second-largest stock market and is nearly double the size of Japan and three times the size of the UK.

What exactly is happening? Effective at the market open on Sept. 23, 2019, China A-shares accessible via the northbound trading segments of the Hong Kong-Shanghai Stock Connect and Hong Kong-Shenzhen Stock Connect facilities meeting underlying index requirements will be added to the benchmark indices shown in Exhibit 2 at a 25% inclusion factor. In other words, each company will be represented in the index at one-quarter of its foreign ownership adjusted float market cap weight.

Large-, mid-, and small-cap securities as defined by each index methodology will be eligible for inclusion. Other indices that use these benchmarks as their universe will continue to exclude A-shares. Separate consultations will be conducted prior to any decision to initiate inclusion in other index series.

Why now? China A-shares have been under review for reclassification to emerging market status as part of our annual country classification reviews since 2013. During this time, we have closely followed the steps taken by Chinese authorities to improve market accessibility and have garnered feedback from a wide range of market participants including asset owners, asset managers, brokers/dealers, and other segments of the investment community. The decision to begin inclusion in 2019 was driven by a broad consensus among market participants that the Stock Connect facilities provide a robust access point for foreign investors. The reduction in trading suspensions was also cited as an important factor for inclusion. Furthermore, given the size of China’s onshore equity market, due to continued limitations placed on foreign investors, and to mitigate the impact on index users, market participants felt that a phased approach was warranted. Further increases in the weight of A-shares would be preceded by a market consultation and would likely require additional enhancements to improve accessibility.

What is the impact? China currently represents about 32% of the S&P Emerging BMI. At the September 2019 reconstitution, 1,241 China A-shares are projected to be added, representing a 5.5% weight in the index. At a hypothetical full inclusion, China would comprise about 45% of the S&P Emerging BMI, with A-shares representing 19% and offshore listings representing 26%.

For further details around the A-share inclusion process, please visit our Client Resource Center where you can access our FAQ, projected impact to key indices, and other related announcements.

Our SPIVA^® readers often ask what percentage of outperforming funds goes on to beat the market over the following years. Our latest research report, Fleeting Alpha: The Challenge of Consistent Outperformance, answers that exact question in detail.

In this blog, we demonstrate the difficulty and likelihood of consistently outperforming a benchmark.

Using trailing three-year returns from Sept. 30, 2012 to Sept. 30, 2015, we found that 298 large-cap funds (27.38%), 123 mid-cap funds (29.55%), and 101 small-cap funds (16.64%) outperformed the S&P 500^®, the S&P MidCap 400^®, and S&P SmallCap 600^®, respectively.

The following year, based on one-year returns as of Sept. 30, 2016, only 9.38% of large-cap managers, 11.54% of mid-cap managers, and 7.78% of small-cap winners beat the benchmarks. By the end of September 2018, only 2.73% of the 298 winners were able to maintain that status for three consecutive years. Exhibit 1 shows the decline in the percentage of managers who were able to outperform the markets continually.

Because cyclical market conditions can unduly influence a point-in-time snapshot like the analysis above, we also performed the same exercise on a rolling quarterly basis from March 31, 2003, to Sept. 30, 2018, and averaged the figures. This resulted in a smoother trend line that is more indicative of the long-term performance persistence (see Exhibit 2).

On average, there was a fair degree of outperformance persistence in the first year across most categories. However, we see an inverse relationship between the level of persistence and the time horizon; persistence declined in each subsequent year.

Over the long term, roughly 24%-26% of large-cap, mid-cap, and small-cap managers outperformed their benchmarks in a given year. Approximately 30%-33% of these managers went on to outperform again in the next year. There was a similar dramatic decline in the percentage from year 2 to year 3. From one year to the next, only about a third were able to beat the market again.

The probability of beating a benchmark for three consecutive years was only 2.4%-3.7%. Out of all the actively managed funds available in the U.S. (1,765 on average), only 30 large-cap managers, 10 mid-cap managers, and 20 small-cap managers possessed this rare skill. Market participants may want to reconsider chasing “hot hands” or picking managers based on past performance.

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.

In early March over 265 financial advisers came together in Melbourne and Sydney for S&P Dow Jones Indices’ 9^th 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!

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.