IN THIS LIST

Degrees of Difficulty: Indications of Active Success

Factor Indices: A Simple Compendium

From Grass to Mass: An Index-Based Approach to Measuring Greenium in Green Bonds

Low Volatility and High Beta: A Study in Backtest Integrity

Harnessing Multi-Factor Strategies Close to the Core

Degrees of Difficulty: Indications of Active Success

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Craig Lazzara

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

EXECUTIVE SUMMARY

• Strong theoretical arguments and extensive empirical data support the view that we should expect most active managers to underperform most of the time. But most of the time is not all of the time, and most active managers are not all active managers. So it is reasonable to ask whether active performance tends to wax and wane.

• We examined fund performance in various market environments to see whether certain conditions correlate with better active performance. We found that active managers were particularly challenged in periods when dispersion was low, stock prices rose, and market leadership came from extremely large stocks.

• Active managers seemed to perform less poorly in years when the low volatility factor underperformed. This suggests that managers, as a group, have a tilt against low volatility stocks.

INTRODUCTION: PASSIVE VERSUS ACTIVE

The debate between passive and active investing has a long history, but in recent years it has escalated to the forefront of investor awareness. A summary of the arguments advanced by the advocates of passive investing would include the following.

• Alfred Cowles’ (1932) paper on the unimpressive predictive power of stock market forecasters1

• William Sharpe’s introduction of the Capital Asset Pricing Model (1964)2 and Eugene Fama’s random walk hypothesis (1965),3 providing a theoretical underpinning for owning the market portfolio rather than relying on active stock selection

• Pleas from Burton Malkiel (1973)4 and Paul Samuelson (1974)5 that someone (anyone!) launch a prototype capitalization-weighted index fund

• Charles Ellis’ (1975) argument that the professionalization of the investment management business made consistent outperformance unlikely6

• Sharpe’s (1991) simple demonstration that “after costs, the return on the average actively managed dollar will be less than the return on the average passively managed dollar.” 7

In addition, numerous observers, prominently including our own firm, have followed in Cowles’ footsteps in accumulating empirical data on the performance of active managers.8 The results confirm what theory predicts: most active managers underperform most of the time.

However, while active managers as a group cannot outperform, there is no theology to say that individual managers cannot outperform, or do so consistently.9 Even if we expect that more than half of active managers will typically underperform, theory does not tell us whether the underperformers will be 51% or 91% of the total. It is reasonable to ask if there are some market conditions that are conducive to relatively favorable (or, more precisely, relatively less unfavorable) active results.

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Factor Indices: A Simple Compendium

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Phillip Brzenk

Managing Director, Global Head of Multi-Asset Indices

S&P Dow Jones Indices

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Anu R. Ganti

U.S. Head of Index Investment Strategy

S&P Dow Jones Indices

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Craig Lazzara

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

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Rupert Watts

Head of Factors and Dividends

S&P Dow Jones Indices

INTRODUCTION

Passive management has become so prominent in the investing landscape that we sometimes forget that the entire history of index funds spans only 50 years. Indices, of course, have a more extensive pedigree than index funds, having been developed initially simply as a means of summarizing the returns of a given stock market. As such, it was natural for at least some observers to compare the returns of actively managed portfolios to index returns, thus using indices as benchmarks for portfolio management.  It was the observation that many (nay, most) professional investment managers routinely underperformed index benchmarks that led to the creation of the first index funds, i.e., to the use of indices as investment vehicles.

The first generation of index funds was designed to replicate an asset class; for example, the S&P 500® is the most common representative of large-capitalization U.S. stocks.  But not all active managers can be usefully evaluated by comparing them to large-capitalization U.S. stocks; specialist mandates (perhaps emphasizing value, or small size, or low volatility) are common among investment managers, and indices have evolved in order to provide appropriate benchmarking.  Factor indices—understanding a “factor” as an attribute with which excess returns are associated—are a prime example of this trend. 

Factor indices can help the clients of specialist managers disentangle how much of the manager’s performance is attributable simply to factor exposure, and how much is attributable to the manager’s stock selection beyond the factor.  Like their first-generation counterparts, factor indices can be used as both benchmarks and investment vehicles.  In the latter use, we can speak of “indicizing” a factor or set of factors—i.e., delivering in passive form a strategy formerly available only via active management.

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From Grass to Mass: An Index-Based Approach to Measuring Greenium in Green Bonds

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Brian D. Luke

Senior Director, Head of Commodities, Real & Digital Assets

S&P Dow Jones Indices

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Catalina Zota

Associate Director, Fixed Income Product Management

S&P Dow Jones Indices

EXECUTIVE SUMMARY

  • Green bonds have historically exhibited a green premium—or "greenium"—meaning they have lower yields compared to non-green bonds with otherwise similar characteristics.
  • Rapid growth and increasing differentiation within the green bond market has led to better ways to measure greenium across global bond markets.
  • An index-based approach illustrates the level of greenium across bond markets; comparing current levels to historical ones suggests shrinking greenium in many major markets.

INTRODUCTION

Green bonds are tied to specific environmentally friendly projects of an issuer. The borrower agrees that the use of proceeds will be invested in environmentally friendly projects such as alternative clean energy, low carbon assets (e.g., green buildings, factories, or vehicles), or sustainable usage of water, pollution, or natural resources. In exchange for this commitment, the issuer seeks economic benefit in the form of a lower borrowing cost. First tapped by supranational borrowers such as the European Investment Bank and the World Bank, the index market value of green bonds surpassed USD 1 trillion in September 2021, expanding from sovereign and quasi-sovereign bonds to corporate and securitized debt.

The green bond market, as measured by the S&P Green Bond Index, has grown since its 2007 debut: growth in the market value of green bonds averaged 70% annually over the past decade, compared with 3% for the global bond market (see Exhibit 1). Along with surging growth, investor demand for green bonds has remained strong.

From Grass to Mass: An Index-Based Approach to Measuring Greenium in Green Bonds: Exhibit 1

S&P Global Ratings' research on the European credit market observed initial sustainable bond yields to be lower compared with conventional bonds, incentivizing issuers. Despite a lower yield, or greenium, investors absorbed the liquidity of green bonds, further stimulating supply. In cases of no greenium at issuance, the research highlighted economic incentives for the investor in the form of outperformance. Tracking historical performance of two nearly identical German government bunds demonstrated additional spread tightening of 5 bps of the bund that was classified as green in the year since issuance.

Historical pricing appeared to demonstrate a price premium for green over non-green, or vanilla, bonds, as green debt represents just 2% of the overall market. More recent evidence suggests mutual benefits for investors and issuers alike as green and vanilla bond yields converge over time. This paper analyzes factors contributing to the changing relative valuation between green and vanilla bonds. Markets covered include European government agency and corporate bonds, as well as U.S. corporate and municipal markets.

Many issuers have repeatedly tapped the green bond market, allowing for issuer-based credit curves. In select cases, they provide good comparisons, but this is rare. Often, new green bonds cannibalize matured vanilla bonds, creating new issue bias. Applying a comprehensive credit valuation approach, this paper analyzes green and vanilla bonds by issuer, sector, and credit rating.

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Low Volatility and High Beta: A Study in Backtest Integrity

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Craig Lazzara

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

EXECUTIVE SUMMARY

  • In April 2011, S&P Dow Jones Indices launched the S&P 500® Low Volatility Index (Low Volatility) and the S&P 500 High Beta Index (High Beta). Their recent 10th “birthday” allows us to compare the backtested performance with which they were introduced with actual live performance.
  • Low volatility and high beta strategies are designed to access specific patterns of returns relative to the market. Low volatility should attenuate the market’s returns (in both directions), while high beta should amplify them.
  • The actual performance of both Low Volatility and High Beta has been consistent with these expectations.

Low Volatility and High Beta: A Study in Backtest Integrity: Exhibit 1

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Harnessing Multi-Factor Strategies Close to the Core

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Andrew Innes

Head of Global Research & Design

S&P Dow Jones Indices

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Rupert Watts

Head of Factors and Dividends

S&P Dow Jones Indices

EXECUTIVE SUMMARY

Factors that outperform over time are also prone to extended periods of underperformance, which are difficult to time. For investors seeking exposure to factor risk premia but with greater diversification and reduced cyclicality, multi-factor strategies may be more suitable than single factors.

Accordingly, S&P DJI presents a new series of multi-factor indices, collectively known as the S&P QVM Top 90% Indices, covering the U.S. large-cap, mid-cap, and small-cap universes (S&P 500®, S&P MidCap 400®, and S&P SmallCap 600®, respectively). In this paper, we analyze the indices' methodology and performance characteristics. Multi-factor scores are based on the average of three separate factors: quality, value, and momentum (QVM). This new index series encompasses a high proportion of the universe, whereas existing multi-factor indices are typically more concentrated.

Different multi-factor strategies produce different outcomes and positioning. Construction matters. These new indices select constituents in the top 90% of the universe, ranked by their multi-factor score and weighted by float-adjusted market capitalization (subject to constraints).

The indices generated moderate outperformance by removing the lowest-ranked decile of stocks. This plus float-adjusted market cap weighting allows the indices to retain many of the core features of the benchmark. In summary, the key historical performance characteristics of the S&P QVM Top 90% Indices include:

  • Moderate outperformance versus the benchmark;
  • Low tracking error;
  • Low turnover;
  • Low active share; and
  • Sector weights consistent with the benchmark.

INTRODUCTION

With the rising adoption of factor indices, the traditional boundaries between passive and active investing have become increasingly blurred. For decades, institutional investors constructed portfolios from a combination of market-cap-weighted index funds and active funds. Now, factor-based investing straddles these two approaches and enables institutional and retail investors alike to implement active strategies through passive vehicles.

Single-factor equity strategies (quality, value, or momentum) have been widely adopted to harvest each factor risk premium that could reward market participants over the long term. However, each factor is susceptible to periods of underperformance dependent on the market environment and economic cycle. This induces some market participants to attempt the notoriously difficult task of timing factors through tactical allocation strategies.

Harnessing Multi-Factor Strategies Close to the Core: Exhibit 1

An alternative solution is to employ a transparent multi-factor strategy that aims to capture exposures across all targeted factors simultaneously. Such a strategy exploits the potential diversification benefits by combining factor returns that have relatively low correlation to one another (see Exhibit 2). Subsequently, the diversified factor exposures may provide more stable excess returns over shorter time horizons, while still capturing their average long-term risk premia. Importantly, this approach avoids the need to subjectively time factor exposures.

Harnessing Multi-Factor Strategies Close to the Core: Exhibit 2

Exhibit 3 demonstrates the key differences between single-factor strategies and the multi-factor strategy in these indices. Here the full-year return of three single factor indices (S&P Quality, S&P Enhanced Value, and S&P Momentum), the S&P QVM Top 90% Multi-factor Index, and their respective benchmarks are ranked each year. Across all three universes (S&P 500, S&P MidCap 400, and S&P SmallCap 600), the wide variability in the calendar year performance rank of each single-factor strategy is evident. Conversely, the multi-factor strategy more consistently exhibits stable excess returns (higher performance rank) across most calendar years with respect to its benchmark.

Harnessing Multi-Factor Strategies Close to the Core: Exhibit 3

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