IN THIS LIST

How Smart Beta Strategies Work in the Chinese Market

Sector Effects in the S&P 500®

Blending Factors in Mexico: The S&P/BMV Quality, Value & Growth Index

Distinguishing Style From Pure Style

Constructing a Systematic Asset Allocation Strategy: The S&P Dynamic Tactical Allocation Index

How Smart Beta Strategies Work in the Chinese Market

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Priscilla Luk

Managing Director, Global Research & Design, APAC

S&P Dow Jones Indices

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Liyu Zeng

Director, Global Research & Design

S&P Dow Jones Indices

EXECUTIVE SUMMARY

In response to the increasing interest in smart beta strategies in the Chinese equity market, we examined the effectiveness of six well-known risk factors—size, value, low volatility, momentum, quality, and dividends— in that market from July 31, 2006, to Nov. 30, 2018.

  • All the risk factors delivered absolute and risk-adjusted quintile return spreads, with the low volatility, value, and high dividend portfolios generating the highest risk-adjusted return spreads.
  • All the Chinese factor indices offered by S&P DJI, except the momentum index, generated absolute and risk-adjusted excess returns in the long run. The low volatility and high dividend indices delivered the highest absolute and risk-adjusted returns, while only the low volatility index had reduced return volatility and drawdown compared with the S&P China A BMI.
  • S&P DJI’s various Chinese factor indices behaved differently during up and down markets. The momentum index tended to perform better in up markets, but the low volatility, value, quality, and dividend indices had better returns in down markets.
  • Our macro regime analysis showed that most factor portfolios in China were sensitive to local market cycles and investor sentiment regimes.
  • Factor strategies can be useful tools for the implementation of active views on the Chinese equity market due to distinct cyclicality in factor performance.


FACTOR-BASED INVESTING IN THE CHINESE EQUITY MARKET

Smart beta strategies are gaining significant attention in the asset management industry, and the exchange-traded products (ETPs) tracking factor indices have shown significant asset growth since the end of 2008. Factor-based strategies are a category of smart beta strategies that target specific risk factors. They have characteristics of passive investing, such as rules-based construction, transparency, and cost efficiency; they also share features of active investing in that they aim to enhance return and reduce risk compared with traditional market-cap-weighted indices.

Single-factor indices are constructed to capture a specific risk factor. They exhibit distinct cyclicality in response to a changing market environment, which also makes them ideal tools for the implementation of active views.

In China, we observe increasing interest in factor-based investing in the equity market, although it lags the U.S. and some other Asian markets (like Japan). Dividend products still dominate the Chinese factor-based ETP market.

In this paper, we examined the effectiveness of six well-known risk factors (size, value, low volatility, momentum, quality, and dividend) in the Chinese equity market and the behavior of these factors under different market regimes.

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Sector Effects in the S&P 500®

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Tim Edwards

Managing Director and Global 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

The Role of Sectors in Risk, Pricing, and Active Returns


Sometimes, the sector composition of an equity portfolio is of primary importance. At other times, single-stock risks are more prominent. In this paper, we shall:

  • Assess the relative importance of sectors in determining the performance of the S&P 500 and its constituents;
  • Compare the potential of active strategies based on sectors to those based on single stocks;
  • Discuss the role that sector-based products can play in generating active returns; and
  • Identify periods when sector selection was particularly important.

This perspective is particularly timely; Exhibit 1 illustrates the increasing strength of sector-level effects in the S&P 500 over the past five years.

1. INTRODUCTION

Consider an active manager who has identified a certain stock in the Utilities sector1 as relatively attractive. He anticipates an excess return from a concentrated position in that stock, compared to a diversified position in the sector. However, a concentrated position in any stock is exposed not only to the specific prospects of that company, but to a sector and to the market. Which exposure is more important?

To illustrate the relative importance of sectoral and stock-level return drivers, consider that the average annualized dispersion of constituent returns in the S&P 500 Utilities sector over the 10 years ending in December 2018 was 10%. Thus, a better-performing stock in the Utilities sector might be expected to offer a one-year excess return over its sector of around 10%. However, over the same 10-year period, the average difference between the one-year return of the S&P 500 Utilities and S&P 500 indices was also 10%. In other words, a stock being one of the best Utilities stocks may be less important than being a Utilities stock.

Of course, even if a chosen stock outperforms its sector, and even if that sector doesn’t significantly underperform the market, the risk of a loss remains. (The S&P 500 Utilities outperformed the S&P 500 by 18% in 2008, but even the best-performing Utilities stock still had a negative total return for the year.) A manager selecting which securities to avoid faces equal and opposite difficulties; an Energy stock with poor prospects relative to its competitors might soar in price if there were a sudden shortage of crude oil.

The extent to which sector-level effects can drive stock returns is the subject of Exhibit 2. It shows the average statistical coefficient of determination (R-squared) between the daily price changes in S&P 500 constituents and their respective sectoral index, based on capitalizationweighted averages of monthly calculations over the 15-year period from January 2004 to December 2018.

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Blending Factors in Mexico: The S&P/BMV Quality, Value & Growth Index

EXECUTIVE SUMMARY

As factor-based investing gains momentum, many market participants are increasingly moving beyond single factors and are constructing multi-factor portfolios.  This progression is not surprising given that combining factors that have low or negative correlation can potentially result in a more diversified portfolio. 

However, one should note that different factors have varying performance patterns depending on market conditions, economic cycles, or investor sentiment.[1]  While every factor strategy aims to earn higher risk-adjusted returns than the broad market over a long-term investment horizon, factors can go through long periods of underperformance. 

Therefore, factor-based investing involves the potential for relative underperformance.  At the same time, timing factors dynamically is difficult to implement and can be costly.[1]  Therefore, the appeal of a multi-factor strategy lies in its ability to provide potentially smoother risk/return patterns than single-factor strategies, while addressing the issue of choosing between factors.

In light of this rationale, S&P Dow Jones Indices (S&P DJI) launched the S&P/BMV Quality, Value & Growth Index in August 2017.  The index is designed to measure the performance of securities in the S&P/BMV IPC that exhibit high quality, value, and growth characteristics.  In this paper, we introduce the performance of those factors, our rationale for combining them, the index construction, and the methodology behind the index.  

Before diving deeper into the multi-factor strategy, it is important to understand the evolution of the implementation of factor strategies in passive investing.  Factor strategies such as value, quality, and growth have existed for decades and have been utilized by active management as part of the security selection and the investment processes.  Passive offerings of factor strategies began with the introduction of growth and value investment styles and later extended to factors such as quality, momentum, and low volatility.

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Distinguishing Style From Pure Style

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Phillip Brzenk

Managing Director, Global Head of Multi-Asset Indices

S&P Dow Jones Indices

EXECUTIVE SUMMARY

  • The first-generation S&P Style Indices cover broad market segments, grouped into value and growth categories using style metrics commonly used in the investment community. This makes the indices relevant benchmarks for evaluating the skill of active Director managers, as well as making them suitable for those seeking a traditional “buy-and-hold” index-linked investment implementation with a tilt toward a particular style.
  • In contrast, the S&P Pure Style Indices have a stricter definition of value and growth style factors, leading each to have concentrated exposures.  Unlike the standard style indices, there are no overlapping securities between pure growth and pure value, potentially presenting them as better candidates for market participants looking to have precise tools in their investment process.
  • Driven by methodological differences, the indices have distinct risk/return characteristics and behave differently in different style cycles. Over the long-term investment horizon, the pure style indices have exhibited greater returns and volatility, lower cross correlations, and wider return spreads than the standard style indices.

INTRODUCTION

Launched in 1992, the first-generation S&P U.S. Style Indices brought broad style benchmarks for large-, mid-, and small-cap equities.  The indices group the investment universe into value and growth categories, based on relevant fundamental ratios for each style.  Certain securities may exhibit both growth and value characteristics; in this scenario, the company’s market capitalization is distributed between growth and value.

As a result, there are overlapping securities that fall into both growth and value indices.  Our analysis shows that over the past 10 years, on average, 166 securities in the S&P 500®, 131 securities in the S&P Midcap 400®, and 188 securities in the S&P SmallCap 600® fell into both the growth and value indices (see Exhibit 1).

Hence, roughly one-third of each size segment exhibits neither strong growth nor value characteristics.  Therefore, even though traditional style indices serve as investment universes and define the broad opportunity set for style equity managers, the overlapping nature of the indices may not appeal to market participants that desire more precise and focused measurements tools.

In 2005, S&P Dow Jones Indices introduced a second generation of style indices, the S&P Pure Style Indices, which require higher style scores for inclusion, resulting in clearer differentiation between growth and value.  The pure style indices include only securities that exhibit either pure growth or pure value characteristics.  Due to this, there are no overlapping securities between the pure style indices (see Exhibit 2).

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Constructing a Systematic Asset Allocation Strategy: The S&P Dynamic Tactical Allocation Index

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Phillip Brzenk

Managing Director, Global Head of Multi-Asset Indices

S&P Dow Jones Indices

SUMMARY

A typical long-term investor may seek exposure to riskier asset classes in their portfolios with the hopes of higher returns and better outcomes.  While the long-term historical returns for higher risk asset classes (such as equities, real estate, and commodities) have been higher relative to safer assets (like short-term U.S. Treasuries), losses can be substantial in downturns.  In times of distress, market participants may tactically allocate to safe haven investments, such as cash or government bonds.Nevertheless, knowing when to be fully “risk on” and when to move to safety is not an easy undertaking.

The capital asset pricing model (CAPM) assumes that investors are rational and risk averse.  However, in reality, behavior biases affect investor decision-making.  In fact, research has shown that when investor performance lags the market, it is often attributable to these biases (Elan, 2010 and Feldman, 2011).  

Behavioral biases, such as loss aversion, overconfidence, anchoring, or impulse, can lead to ill-timed or ill-advised investment decisions, resulting in less desirable outcomes (Kahneman and Ripe, 1998 and Pompian, 2018).  Investors can be hardwired to want to take action in times of volatility, whether warranted or not.  Although it can be challenging to overcome these behavioral tendencies, a systematic and dynamic allocation approach to control portfolio volatility can help prevent an unnecessary “anxious exit” from the market.

In this paper, we introduce the S&P Dynamic Tactical Allocation Index (DTAQ), which uses a systematic approach to asset allocation by incorporating dynamic and tactical investment strategies into the index design.  We first review the portfolio construction methodology, providing empirically driven rationale for the asset class building blocks and overall ruleset.  In part two of the paper, we review the historical index performance.  We compare the strategy with hypothetical static allocation versions and the classic 60/40 equity/bond portfolio.

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