IN THIS LIST

Commodities Index Innovation: The Next 30 Years

Simplicity Is Also Beautiful in Brazil: The S&P/B3 Low Volatility High Dividend Index

Style Bias and Active Performance

Exploring Techniques in Multi-Factor Index Construction

The Hidden Costs of Retail Purchases in Municipal Bonds

Commodities Index Innovation: The Next 30 Years

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Jim Wiederhold

Associate Director, Commodities and Real Assets

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Fiona Boal

Head of Commodities and Real Assets

Simplicity Is Also Beautiful in Brazil: The S&P/B3 Low Volatility High Dividend Index

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Smita Chirputkar

Director, Global Research & Design

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María Sánchez

Associate Director, Global Research & Design

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Gaurav Sinha

Managing Director, Head of Americas Global Research & Design

EXECUTIVE SUMMARY

For investors who seek higher dividend yield and lower volatility for better risk-adjusted returns, S&P Dow Jones Indices has proposed a two-step constituent screening method. In this paper, we discuss how this analysis can be applied to Brazilian equity markets using the S&P/B3 Low Volatility High Dividend Index.

  • The low volatility screen acts as a quality measure to avoid high-yield stocks with sharp price drops and seeks to capture the low volatility factor for the S&P/B3 Low Volatility High Dividend Index.
  • The S&P/B3 Low Volatility High Dividend Index delivered a higher absolute and risk-adjusted return than the benchmark, the S&P Brazil BMI, from May 31, 2007, to March 31, 2020 (see Exhibit 1).
  • The index outperformed the S&P Brazil BMI 83% of the time in down markets and underperformed 68% of the time in up markets. However, the outperformance in down markets was more pronounced than the underperformance in up markets.
  • Compared with its benchmark, the S&P/B3 Low Volatility High Dividend Index historically delivered higher dividend yield.

Exhibit 1: Absolute and Risk-Adjusted Returns

1. INTRODUCTION

Almost one year after launching the S&P/B3 Low Volatility High Dividend Index, we examine the potential advantage of incorporating a low volatility screen into a high-dividend-yield portfolio. We also compare the S&P/B3 Low Volatility High Dividend Index to other S&P Dividend Indices in the Brazilian equity market across various aspects such as sector composition, dividend yield, and historical return, among others.

Historically, the percentage of dividend payers in Brazil has ranged between 71% and 92%, making it a favorable environment for implementing dividend-focused strategies. In Brazil, S&P Dow Jones Indices has three different dividend-focused strategies, using different constructions and targeting different objectives:

The highest-yielding stocks in high-yield strategies often come with greater portfolio volatility, and Brazil is no exception. Therefore, an income strategy may require some form of volatility management for portfolio construction.

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Style Bias and Active Performance

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Craig Lazzara

Managing Director, Global Head of Index Investment Strategy

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Anu R. Ganti

Senior Director, Index Investment Strategy

EXECUTIVE SUMMARY

  • Style bias plays a major role in explaining active manager outperformance across the capitalization spectrum.
  • Active managers of large-capitalization portfolios tend to tilt down the cap scale, while mid- and small-cap managers tend to tilt up. Consequently, large-cap managers are most challenged when large-cap stocks beat mid- and small-caps.  Mid- and small-cap managers have the opposite tendency.
  • As Exhibit 1 illustrates, the likelihood that a majority of managers in a given capitalization tranche will outperform is importantly dependent on style favorability.
  • Similar results apply for fixed income managers.

Style Bias and Active Performance - Exhibit 1

A SIMPLE QUESTION

The evidence that most active portfolio managers typically underperform passive benchmarks appropriate to their investment style is extensive—both historically and geographically. Exhibit 2, for example, summarizes data from our firm’s SPIVA® Scorecards, which have documented the performance of U.S. managers since 2001 (with shorter histories for other markets). Of the 19 full calendar years for which we have U.S. SPIVA results, the majority of large-cap active managers outperformed the S&P 500® in only three.

This paper asks a simple question: what (if anything) distinguishes the three years when most active managers outperformed from the 16 years when the majority failed?

Style Bias and Active Performance - Exhibit 2

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Exploring Techniques in Multi-Factor Index Construction

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Akash Jain

Associate Director, Global Research & Design

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Akash Jain

Associate Director, Global Research & Design

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Andrew Innes

Head of EMEA, Global Research & Design

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Lalit Ponnala

Director, Global Research & Design

In multi-factor equity index construction, the decision-making and practical implementation can be complex and challenging. This paper examines the range of portfolio construction choices available to those seeking rank-based, multi-factor approaches, and the relative advantages of each.

Through back-testing hypothetical portfolios based on the S&P 500®, this paper evaluates the following construction choices: top-down versus bottom-up; sector-neutral versus sector-agnostic; portfolio concentration; weighting scheme; and rebalancing frequency. To measure the effectiveness of each portfolio, a factor efficiency ratio (FER) is proposed, which allows investors to gauge their factor purity without having to invoke the complexity of a risk model.

Our paper concludes with key findings, including the following.

  • Sector-neutral portfolios may be more efficient than sector-agnostic.
  • Top-down approaches may dilute exposures but are still efficient.
  • Factor score-based weighting schemes may improve efficiency.

INTRODUCTION

The benefits of diversifying across a multitude of smart beta equity factors have been supported and explained in a host of research and literature. Single-factor indices (quality, value, momentum, low volatility, and small size) may reward market participants over the long term, but can be notoriously difficult to time over the shorter term. Multi-factor indices, on the other hand, generally forgo the need to time each factor and instead, through deliberate diversification, may provide more stable excess return outcomes.

Multi-factor equity investing may be well justified in theory; however, there are numerous practical portfolio construction choices to consider, each with its own advantages and implications. Without a consensus on the most effective multi-factor technique, indices offered in the market have fractured into a variety of vastly different methodologies. Some employ optimization and risk models to determine the most effective portfolio based on the strategy’s objectives. Others dismiss the complexity and lack of transparency of optimized solutions, instead favoring the relative simplicity of rank-based selection rules. Yet even within this latter realm, the choices may appear countless and overwhelming.

In this paper, we attempt to demystify the range of choices available to market participants seeking rank-based, multi-factor approaches. In doing so, we compare the relative advantages of each approach and discover the trade-offs between each decision. Critically, the approaches should be measured both on their effectiveness and efficiency in terms of risk.

Importantly, we do not advise investors which strategic factor allocation decisions are the more successful. Also, testing the robustness of multifactor performance across markets and time periods was outside the scope of this paper.

Instead, by testing only one multi-factor combination on the S&P 500, the paper’s purpose is only to demonstrate relationships among portfolio construction choices. Our goal is to arm market participants with the necessary knowledge to help determine which multi-factor portfolio construction techniques are most appropriate for their own investment objectives.

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The Hidden Costs of Retail Purchases in Municipal Bonds

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Jason Giordano

Director of Fixed Income

EXECUTIVE SUMMARY

  • Despite recent innovations providing greater access to bond markets, the tax-exempt municipal bond investor base is still dominated by retail buyers.
  • Independent research on retail transactions has shown an average loss in income of 0.55%. In a low-rate environment, this presents a substantial disadvantage to retail bond buying.
  • Retail investors can avoid these punitive charges by buying mutual funds or ETFs. ETFs have a distinct advantage in that shares of the fund can be exchanged without the need to incur any transactions in the institutional market.
  • In a low-yield environment, retail transaction costs can be a significant cause of erosion of potential returns.

INDIVIDUAL INVESTORS ARE AT A DISADVANTAGE

Owning individual bonds has its risks and rewards.  However, buying a bond entails an unseen transaction cost, which may not always be clear to retail investors.  This transaction cost exists because bonds are not typically sold with a commission.  Instead, a markup is built into the bond price.

This report offers a transparent look at these hidden transaction costs for U.S. municipal bonds.  To determine these costs, we used large, recently issued investment-grade bonds tracked by the S&P National AMT-Free Municipal Bond Index and the S&P AMT-Free Municipal Index Series, and high-yield municipal bonds tracked by the S&P Municipal Bond High Yield Index, in conjunction with bond transaction data provided by the Municipal Securities Rulemaking Board (MSRB).  This information can help market participants compare the cost of buying individual bonds to the cost of investing in bond alternatives, such as mutual funds and ETFs.

From Jan. 1, 2020, through Sept. 30, 2020, the average implied transaction cost of buying an individual municipal bond of investment-grade quality was 0.72% for retail investors, compared with the average implied transaction cost of 0.17% for institutional trades.  With yields on investment-grade municipal bonds averaging less than 2% during this timeframe, the difference of 0.55% in trade costs represents a significant, and immediate, loss of potential income.

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