Indexology^® Blog

The S&P 500 finished Q1 2019 up 13.07%. This was the best first quarter return since the 13.53% posted in Q1 1998. January’s 7.87% return was the best start to the year since 1987 (13.18%). The hot start cooled some with the 10th-best February since 1987, at 2.97%. In March, the upward momentum slowed further, posting a 1.79% return, making it the 15^th-best March since 1987. Overall, 32 of the 42 segments of the U.S. equity market had lower consecutive returns from January to February and then February to March.

To begin the month, on March 1, the S&P 500 closed above 2,800 for the first time since Nov. 8, 2018. Throughout March, the S&P 500 challenged the 2,800 resistance level multiple times. It closed at 2,854.88 on March 21, which was the highest point since the index closed at 2,880.34 on Oct. 9, 2018. The S&P 500 hovered around 2,800 as the market awaited new developments in the U.S.-China trade talks and the Federal Reserve’s outlook dimmed. Fears of slowing growth also factored into decelerating the upward momentum.

As was the case in 2019, the S&P 500 posted positive returns during all three months of the first quarter on eight other occasions over the past 30 years, most recently at the start of 2013. April was also positive six of those eight times. In every year that the first three months were all positive, the S&P 500 not only ended the year positive, but also ended higher than the Q1 index level.

The S&P MidCap 400^® (-0.74%) and S&P SmallCap 600^® (-3.53%) were both negative for the month of March. Thirty-three of 42 segments of the U.S. equity market had lower returns in March than in February, but only 19 were negative. Nine of 11 large-cap sectors were positive, compared to 6 of 11 mid-cap and only 4 of 11 small-cap sectors. Information Technology was the best-performing large-cap sector, with a 4.75% return in March, but it was the seventh-best sector in both the mid- and small-cap segments. Financials was the worst-performing sector in all three size segments. Additionally, growth outperformed value for the third consecutive month.

In a previous blog, we took the first and second steps in our Growth at a Reasonable Price (GARP) strategy construction. We introduced the GARP investment strategy and showed how it can be implemented systematically. In this blog, we will take the third and fourth steps: using a multi-factor sequential filtering process for security selection and establishing constituent weights.

Multi-Factor Sequential Filtering Process

There are a number of approaches one can take to construct multi-factor portfolios—mainly integration,[1] sequential filtering, and optimization. We use the sequential filtering method because it is easy to understand and effective in achieving its targeted factor exposures.

Multi-factor sequential filtering selects stocks using two layers of filters, as shown in Exhibit 1. In the first step (filter 1), stocks are ranked by their growth z-scores, with the top 150 stocks remaining eligible for constituent inclusion. In the second step (filter 2), those 150 stocks are then ranked by their quality & value (QV) composite z-scores. The top 75 stocks are selected to be included in the strategy after applying a 20% buffer rule.[2] The 20% buffer is applied to reduce portfolio turnover.

Constituent Weights

Once constituents are selected at each rebalance, eligible securities are weighted by their growth score[3] to achieve the strategy’s growth exposure. To limit the impact of extreme values, the maximum weight of a security is capped at 5%. Individual GICS^® sector exposure is capped at 40% to broaden the strategy’s sector exposure.

In this and a previous blog, we discussed our GARP strategy construction process. In coming blogs, we will present the empirical results of the strategy performance, its sector composition, and its performance attribution.

^[1]   S&P Quality, Value & Momentum Multi-Factor Indices Methodology, February 2019.

[2]   Buffer Rule: A 20% buffer is implemented as follows:

1. Stocks in the top 150, based on growth z-score, are ranked by their QV composite z-score. The top 60 stocks are automatically chosen for index inclusion.
2. Stocks that are current constituents that fall within the top 90 based on their QV composite z-score are chosen for index inclusion in order of their QV composite z-score.
3. If at this point 75 stocks have not been selected, the remaining stocks are chosen based on their QV composite z-score until the target count is reached.

[3]   Please see Footnote 7 from the last blog for growth score computation.

At the recent Australian Indexing & ETF Masterclass series held in Melbourne & Sydney, we asked the audience, 83% of whom were Financial Advisers, a number of questions using a mobile-enabled polling tool. Over 260 people attended the Masterclass across the two cities, and 189 of these people used the polling tool. 767 discrete votes were received across the six polls that we conducted. The results provide a snapshot of Financial Adviser views on ETFs, current usage, planned future usage and the prime considerations in selecting ETFs. Let’s look at the results in detail.

Our first poll asked the audience to nominate a range that represented their usage of ETFs in client portfolios. The largest response was for the highest range.

1. A full 29% of advisers indicated that between 76-100% of their client portfolios use ETFs;
2. 12% of respondents indicated that their client portfolios use between 51-75% ETFs; and
3. 24% of respondents polled said that between 26-50% of their client portfolios were ETF driven.

These results combined indicate that 65% of advisers are using ETFs for at least 25% of their client portfolios.

Our second poll question asked attendees the split between Australian-listed vs internationally-listed ETFs used in client portfolios. The splits reported were as follows:

1. 16% of respondents only use Australian-listed ETFs;
2. 16% of respondents mainly use Australian-listed ETFs;
3. 46% of respondents split usage 50:50 between Australian-listed and internationally-listed ETFs;
4. 18% of respondents mainly use internationally-listed ETFs;
5. 4% of respondents only use internationally-listed ETFs.

Our third poll sought to ascertain the indexing and ETF topics in which attendees are most interested. The results came out as follows:

1. 35% of poll responses nominated Equities & Sectors;
2. 22% nominated Smart Beta/Factors;
3. 15% nominated Fixed Income;
4. 13% went for ESG; and
5. 10% put their hands up for Commodities.

We also asked attendees, whether they expect their usage of ETFs to increase over the next 12 months:

1. 76% indicated their use of ETFs will increase;
2. 21% indicated there would be no change in use; and
3. 3% indicated their use of ETFs will decrease.

When asked a follow up question, as to the expected increase in ETF use over the next 12 months respondents provided the following results:

1. 12% expect to increase usage by 81-100%;
2. 12% expect to increase usage by 61-80%;
3. 20% expect to increase usage by 41-60%;
4. 32% expect to increase usage by 21-40%;
5. 22% expect to increase usage by 1-20%; and
6. 2% expect no increase in their use of ETFs.

Our final poll questions to the Masterclass audience asked what their prime considerations are when selecting ETFs. The results were as follows:

1. 32% look to the ETF issuer’s reputation in making a selection;
2. 22% look at the liquidity of the ETF and 22% also consider the expense ratio;
3. 13% consider past performance; and
4. 10% consider the Index provider’s reputation.

These results, pleasingly, demonstrate that there is a propensity to increase the use of ETFs as tools within client portfolios to achieve investment objectives. While just over 1/3 of respondents nominated Equities and Sectors as their topic of greatest interest, it is also pleasing that other topics have a solid level of interest also, indicating, that while equities are the core of ETF use, other asset classes are also on advisers’ radar.

The responses also provide valuable insights into the topics that we can address at future adviser-facing events, including our 10^th Annual Indexing and ETF Masterclass scheduled for Q1, 2020.

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.