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Persistently Disappointing

Disentangling Diversification

Explaining Optimized Weights

Combining Sectors and Sustainability

S&P/ASX Small- and Mid-Cap Indices: Differentiators in a Large-Cap Dominated Market

Persistently Disappointing

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Craig Lazzara

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

If you’ve ever read a prospectus (or, for that matter, an S&P DJI research report), you know that “past performance is no guarantee of future results.” At one level, if you understand that, you understand the most important thing about S&P DJI’s Persistence Scorecards. For the U.S., Europe, Latin America, and Canada (with Australia coming soon!) our recently released Persistence Scorecards are unanimous in showing that historical outperformance is not a predictor of future outperformance.

At a deeper level, why do we care? We know from SPIVA® and other data that most active managers underperform most of the time. But even in the most challenging years, there is a range of active performance results; some managers will always do better than others, regardless of how many outperform passive benchmarks like the S&P 500®. Do the top performers get there because of genuine skill or merely because of good luck?

There is, after all, no theology that precludes the existence of a (presumably small) subset of genuinely skillful active managers. If such a group existed, how would their abilities be evidenced in performance data? As a thought experiment, we can consider a hypothetical set of managers who achieve above-median performance in a particular period, and ask how they perform in subsequent periods.

If every above-median manager in period one got there simply by being lucky, we would expect half of them to be above median again in period two. If the repeat-success rate were substantially above 50%, we might begin to suspect that the above-median managers were genuinely skillful. But if fewer than 50% of the period one successes were above median in period two, that would support the view that their period one success was due to luck. If we understand something about persistence, we may be able to make inferences about manager skill.

And that is exactly what the Persistence Scorecards let us do. Exhibit 1 illustrates, using 10 years of U.S. equity data and asking to what degree above-median performance in the first 5 years predicted above-median performance in the second 5 years. The answer is: not at all. In every fund category, the winners in years 1-5 were unlikely to repeat their success in years 6-10.

There are, of course, other ways to test for persistence. We could examine performance relative to a benchmark rather than to a peer group, with different lookback periods (one year or three years rather than five years), with different cutoffs (quartiles rather than halves), and for different asset classes (bonds as well as stocks). Our Persistence Scorecards do all of these things and, mutatis mutandis, the results are the same.

Results produced by genuine skill are likely to continue, while those due to luck are likely to prove ephemeral. The data suggest that good active performance often owes more to luck than to skill.

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

Disentangling Diversification

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Anu Ganti

U.S. Head of Index Investment Strategy

S&P Dow Jones Indices

We frequently hear that “it’s a stock picker’s market.” The recent market environment could equally well be characterized as a sector picker’s market.

To measure the importance of sectors, we decompose total market dispersion into within-sector and cross-sector effects. Exhibit 1 shows that the contribution of cross-sector effects to total S&P 500® dispersion has trended upward this year, implying that the rewards for skillful sector selection have increased. 

The recent travails of the banking industry have weakened the Financials sector, with the S&P 500 Financials down 4% YTD as of May 18, 2023. Meanwhile, the overall market has marched to a different drumbeat, with the S&P 500 up 10%. As Exhibit 2 illustrates, the Financials sector was the third-biggest detractor from S&P 500 performance, while Information Technology was the dominant contributor YTD.

As a result, investors who overweighted Information Technology or underweighted Financials would have been well rewarded, with the S&P 500 Ex-Financials up 12% YTD. However, it is worth noting that the outperformance of IT has been much greater than the underperformance of Financials, with the S&P 500 Information Technology up 28% YTD.

As sectors evolve over time, so does their diversification potential. As we previously explored for Energy and Information Technology, in Exhibit 3, we calculate the spread in trailing 12-month volatility between the S&P 500 and the S&P 500 Ex-Financials. When this spread is positive, the inclusion of Financials increases volatility in the benchmark; when negative, the sector acts as a diversifier. Note the negative spread for Financials so far this year.

The Financials sector has become a volatility diversifier because its correlation with the rest of the market has recently declined, as shown in Exhibit 4.

While the Financials sector has recently reduced volatility, this hasn’t always been the case. For example, during the depths of the 2008 Global Financial Crisis, the Financials sector was a major source of volatility. Despite the sector’s faltering relative performance so far this year, its current risk positioning might potentially prove auspicious for judicious sector allocators. 

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

Explaining Optimized Weights

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Ben Leale-Green

Former Associate Director, Research & Design, ESG Indices

S&P Dow Jones Indices

Transparency is commonly seen as a key principal of sustainable investing,1 helping investors understand companies’ sustainability profile and sustainable investment strategies, as well as potentially contributing to investor impact.2 Sustainability is also multifaceted, illustrated by the differences in many of the Sustainable Development Goals (e.g., no poverty and life below water represent very different goals) and mathematically demonstrated by weakly correlated, or uncorrelated, corporate sustainability KPIs (see Exhibit 1).

While addressing multiple uncorrelated goals defines the sustainability problem well, this raises a challenge from an index construction and transparency perspective. The simplest methods are highly transparent but may lack ability to capture the multifaceted nature of sustainability with precision. Alternatively, optimization is excellent at meeting multiple objectives simultaneously; however, it raises a transparency challenge. We propose tackling this challenge with our “weight attribution” methodology, allowing the best of both worlds: sophistication of index construction with transparency of weight allocation.

Weight attribution aims to explain active weights within an index, using relevant data points. This model is interpreted, leading to understanding of which factors are influencing stock weights and to what extent (see Exhibit 2).3

We use the S&P PACT™ Indices (S&P Paris-Aligned & Climate Transition Indices) to demonstrate transparency gains from weight attribution. Seven sustainability metrics are included in the optimization, leading to the question of what really drives the weights of companies. Exhibit 3 shows the interpretation, which yields understanding of the relative importance of sustainability factors when reweighting the indices.

In this example, we see similarities across regions, with the “Transition Pathway” being the most important factor driving weights. The S&P/ASX 300 Net Zero 2050 Climate Transition ESG Index, S&P/ASX 300 Net Zero 2050 Paris-Aligned ESG Index and S&P UK Net Zero 2050 Paris-Aligned ESG Index are different here, where the transition pathway constraint was softened. We also gain an understanding of how other sustainability factors drove weights of stocks within the indices, providing much needed transparency to optimized weights.

Model selection: in modeling the active weights, the random forest algorithm is preferred over other models due to the following benefits:

  1. Stronger explanatory power than linear regression (see Exhibit 4), likely caused by the ability to capture non-linear relationships between weights and sustainability factors. In particular, random forest works well for long-only strategies, where linear regression predicts negative weights, which cannot be possible (see Exhibit 5);
  2. The outcome has low sensitivity to model tuning (see Exhibit 6) and is less prone to overfitting than other tree-based or non-linear models; a common property by design.4

Weight attribution can bring transparency to optimized indices, which can capture the multidimensional nature of sustainability well. This can help market participants understand sustainable investment strategies and potentially contribute to investor impact.

 

1 Peterson, Douglas L, “Transparency and Impact: The Essential Principles of ESG,” S&P Global, 2022.

2 Kolbel, Heeb, Paetzold and Busch (2020) outline what they consider “determinates of investor impact”. Transparency may aid these impact mechanisms.

3 The model’s hyperparameters are tuned using a Bayesian optimization, utilizing the following utility function:

Where MSE is the mean square error, lambda is a scaling factor and a 5-k-fold cross-validation is used for the out of sample MSE.  Model interpretation uses SHAP values. SHAP values aim to understand the marginal contribution that each feature makes to the predicted outcome of a model (in the example used, the impact a climate/ESG factor has on the reweighting of stocks within S&P PACT Indices).

4 López de Prado, Marcos, “Advances in Financial Machine Learning,” Wiley, 2018.

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

Combining Sectors and Sustainability

What happens to index performance and diversification when sectors and sustainability are combined? S&P DJI’s Stephanie Rowton and Invesco’s Chris Mellor discuss how new index-based tools are helping market participants track a diverse mix of companies with strong sustainability credentials.

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

S&P/ASX Small- and Mid-Cap Indices: Differentiators in a Large-Cap Dominated Market

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Sean Freer

Director, Global Equity Indices

S&P Dow Jones Indices

Large-cap, Australian-listed companies have continued their robust 2022, outperforming the small- and mid-cap segments YTD as of April 30, 2023. However, a fast-changing economic environment may support considering small and mid-cap indices in Australia.

The range of returns for Australian-listed companies in 2022 was among the widest seen in 20 years. Broad dispersion was exhibited across sector, style and market cap segments. The calendar year outperformance of large (S&P/ASX 50) versus small- and mid-cap companies (S&P/ASX Small Ordinaries and S&P/ASX MidCap 50) was 20.28% and 8.14%, respectively. This is the second-largest outperformance of large versus small and mid-caps over the past 20 years.

Mid- and Small-Cap Indices Offer Sector Diversification

Financials make up over 30% of the S&P/ASX 50 and 5 of its top 10 companies by index weight. The big four banks (CBA, NAB, ANZ and WBC) have benefited from rising interest rates and investor preference for dividend income in an inflationary environment. Different segments and sectors may out- or underperform during the various stages of an economic cycle. As inflationary pressures abate and interest rates seemingly level out, mid- and small-cap indices may offer diversification benefits away from the banks.

Presently, the S&P/ASX Small Ordinaries has more exposure to Consumer Discretionary and Real Estate companies, while the S&P/ASX MidCap 50 has more weight in Industrials and Information Technology companies relative to the small- and large-cap indices.

Small Caps Offer Most Compelling Relative Valuations

The different composition of the market cap segment indices has resulted in distinctive performance outcomes. As of April 30, 2023, mid-caps were the best-performing market segment over the three- and five-year periods. The S&P/ASX Small Ordinaries has underperformed during this period, however, it does exhibit the most compelling relative valuations.

The trailing 12-month price/earnings ratio for the S&P/ASX Small Ordinaries and S&P/ASX Mid Cap 50 are well below their long-term average, at approximately 9x and 14x earnings, respectively. Meanwhile, the three-year average of the 12-month trailing P/E ratio for the S&P/ASX Small Ordinaries recently moved lower than the S&P/ASX 50 for the first time in over 10 years.

Mid- and Small-Cap Active Managers Underperformed in 2022

Advocates of active management often argue there is more alpha to be found in the mid- and small-cap space. Furthermore, the broad dispersion of returns exhibited in 2022 is said to provide more of an opportunity set for skilled stock picking. However, S&P DJI’s SPIVA® Australia Year-End 2022 Scorecard shows just 23.4% of Australian Equity Mid- and Small-Cap active funds beat the S&P/ASX Mid-Small in 2022, while over 80% underperformed on a risk-adjusted basis. Funds in this category lost 19.8% and 22.0% on equal- and asset-weighted bases, respectively, for the same period.

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