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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
A guide to the popular and fast growing investment opportunities of smart beta Equity Smart Beta and Factor Investing for Practitioners offers a hands-on guide to the popular investment opportunities of smart beta, which is one of the fastest growing areas within the global equity asset class. This well-balanced book is written in accessible and understandable terms and contains an in-depth manual filled with analytical information and new ideas. The authors--noted experts in the field--include a definition of smart beta investing and detail its history. They also explore the distinguishing characteristics of smart beta strategies, offer an overview of factor investing, and reveal the implementation of smart beta approaches. Comprehensive in scope, the book contains helpful examples of applications, real-life illustrative case studies, and contributions from leading and respected practitioners that explain how they approach smart beta investing. This important book: * Contains an in-depth exploration of smart beta investing * Includes the information written in clear and accessible language * Presents helpful case studies, illustrative examples, and contributions from leading and respected experts * Offers a must have resource coauthored by the Head of Goldman Sachs' equity smart beta business Written for investors who want to tap into the opportunities that smart beta offers, Equity Smart Beta and Factor Investing for Practitioners is the comprehensive resource for learning how to create more efficient overall equity portfolios.
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EQUITY SMART BETA AND FACTOR INVESTING FOR PRACTITIONERS
Khalid Ghayur
Ronan Heaney
Stephen Platt
Cover image: © VectaRay / iStock.com
Cover design: Wiley
Copyright © 2019 by Goldman Sachs & Co. LLC. All rights reserved.
Republished with permission.
Chapter 16: Smart Beta from an Asset Owner's Perspective by James Price and Phil Tindall. Copyright © 2018 Willis Towers Watson. All rights reserved.
Chapter 17: Smart Beta: The Space Between Alpha and Beta by Andrew Junkin, Steven Foresti, and Michael Rush. Copyright © 2018 Wilshire Associates Incorporated. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data:
Names: Ghayur, Khalid, author. | Heaney, Ronan G., author. | Platt, Stephen C., author.
Title: Equity smart beta and factor investing for practitioners / Khalid Ghayur, Ronan Heaney, Stephen Platt.
Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2019] | Includes bibliographical references and index. |
Identifiers: LCCN 2019011593 (print) | LCCN 2019013697 (ebook) | ISBN 978-1-119-58345-5 (Adobe PDF) | ISBN 978-1-119-58344-8 (ePub) | ISBN 978-1-119-58322-6 (hardback)
Subjects: LCSH: Investments. | Portfolio management. | Investment analysis.
Classification: LCC HG4521 (ebook) | LCC HG4521 .G43 2019 (print) | DDC 332.6—dc23
LC record available at https://lccn.loc.gov/2019011593
CONTENTS
Cover
Acknowledgments
Disclaimer
Introduction
PART I OVERVIEW OF EQUITY SMART BETA SPACE
Chapter 1 Evolution and Composition of the Equity Smart Beta Space
Chapter Summary
I. Introduction
II. Evolution of Equity Smart Beta
III. Desired Characteristics of Smart Beta Strategies
IV. Composition and Definition of Equity Smart Beta
V. Typical Investor Questions
VI. Conclusion
Notes
PART II EQUITY COMMON FACTORS AND FACTOR INVESTING
Chapter 2 An Overview of Equity Common Factors and Factor Investing
Chapter Summary
I. Introduction: What Are Equity Common Factors?
II. Evolution of Equity Common Factors and Factor Investing
III. Typical Investor Questions
IV. Conclusion
Notes
Chapter 3 Explaining Smart Beta Factor Return Premia
Chapter Summary
I. Introduction
II. Data Mining
III. Risk-Based Explanations
IV. Behavioral Explanations
V. Structural Explanations
VI. Typical Investor Questions
VII. Conclusion
Notes
PART III CAPTURING SMART BETA FACTORS
Chapter 4 Weighting Schemes
Chapter Summary
I. Introduction
II. Weighting Schemes Used to Capture Factor Returns
III. Assessing the Investment Performance and Efficiency of Weighting Schemes Used to Capture Factor Returns
IV. Typical Investor Questions
V. Conclusion
Note
Chapter 5 Factor Specifications
Chapter Summary
I. Introduction
II. Value
III. Momentum
IV. Low Volatility
V. Quality
VI. Typical Investor Questions
VII. Conclusion
Chapter 6 Active Risk and Return Decomposition of Smart Beta and Active Strategies
Chapter Summary
I. Introduction
II. Risk Decomposition of Smart Beta Strategies
III. Risk Decomposition of Active Strategies
IV. Typical Investor Questions
V. Conclusion
PART IV PERFORMANCE CHARACTERISTICS OF SMART BETA FACTOR STRATEGIES
Chapter 7 Performance Characteristics of Individual Smart Beta Factors
Chapter Summary
I. Introduction
II. After-Cost Performance: Accounting for Implementation Costs
III. After-Cost Performance Characteristics
IV. Typical Investor Questions
V. Conclusion
Chapter 8 Performance Characteristics of Factor Diversification Strategies
Chapter Summary
I. Introduction
II. Active Return Correlations
III. Performance Characteristics of Factor Diversification Strategies
IV. Constructing Diversification Strategies: The Portfolio Blending versus Signal Blending Debate
V. Typical Investor Questions
VI. Conclusion
Chapter 9 The Low-Volatility Anomaly
I. Introduction
II. Historical Manifestation of the Low-Volatility Factor
III. How Is “Low Volatility” Defined?
IV. Secondary Factors of Low-Beta Portfolios
V. Building a Low-Volatility Portfolio
VI. Publicly Available Low-Volatility ETFs
VII. Summary and Conclusion
PART V SMART BETA IMPLEMENTATION
Chapter 10 Structuring Better Equity Portfolios: Combining Smart Beta with Smart Alpha
Chapter Summary
I. Introduction
II. Current Portfolio Structuring Practices
III. Portfolio Structuring: A Suggested Framework
IV. Typical Investor Questions
V. Conclusion
Chapter 11 Incorporating ESG with Smart Beta
Chapter Summary
I. Introduction
II. ESG Data
III. Incorporating ESG Strategies
IV. Incorporating ESG with Smart Beta
V. Typical Investor Questions
VI. Conclusion
Chapter 12 An Alternative to Hedge Fund Investing: A Risk-Based Approach
I. Introduction
II. Benefits of a Diversified Portfolio of Hedge Funds
III. Systematic Drivers of Hedge Fund Performance
IV. Liquid Tracking Portfolio Simulated Performance
V. Developments in the Hedge Fund Industry
VI. Conclusion
Notes
PART VI ASSET OWNER PERSPECTIVES
Chapter 13 Implementing Smart Beta at CalPERS, A Conversation with Steve Carden
Chapter 14 A Pension Fund’s Journey to Factor Investing: A Case Study
I. Introduction
II. The Case for Passive Market Cap–Weighted Strategies
III. Are Smart Beta Strategies the Better Alternative?
IV. Practical Considerations
V. Conclusion
Chapter 15 Using Smart Beta for Efficient Portfolio Management
I. Introduction
II. Motivation and Strategy Selection
III. Challenges
IV. Product Selection
V. Smart Beta Allocation
VI. Governance, Monitoring, and Performance Benchmarking
VII. Conclusion
PART VII CONSULTANT PERSPECTIVES
Chapter 16 Smart Beta from an Asset Owner’s Perspective
I. The Smart Beta Revolution or Evolution?
II. Smart Beta from the Asset Owner Perspective
III. Asset Owners Face New Challenges When Using Smart Beta Strategies
IV. Future Developments
V. Concluding Thoughts
Notes
Chapter 17 Smart Beta: The Space Between Alpha and Beta
Chapter Summary
I. Factors: The Building Blocks of Portfolios
II. Alpha or Beta?
III. Equity Factor Investing: An Example
IV. Performance of Key Equity Factors
V. Implementation of Smart Beta
VI. Smart Beta Case Study: A Potential Complement to Traditional Active Management
VII. The Pros and Cons of Smart Beta
VIII. Conclusion
Appendix: Valuation Exhibits
Notes
PART VIII RETAIL PERSPECTIVES
Chapter 18 Smart Beta Investing for the Masses: The Case for a Retail Offering
I. Introduction to Factor Investing and Smart Beta
II. Why Provide a Smart Beta Strategy in Today's Retail Market?
III. Challenges in Developing a Smart Beta Portfolio Strategy for Retail Investors
IV. Implementing a Smart Beta Portfolio Strategy as a Fiduciary Advisor
V. A Look into the Future
VI. Conclusion
Notes
Chapter 19 Positioning Smart Beta with Retail Investors, a Conversation with
PART IX CONCLUDING REMARKS
Chapter 20 Addressing Potential Skepticism Regarding Smart Beta
I. Skepticism Regarding Factor Existence
II. Skepticism Regarding Implementation
III. Skepticism Regarding Factor Persistence
IV. Conclusion
Chapter 21 Conclusion
About the Authors
Bibliography
Additional Disclaimers
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1
Table 2.2
Chapter 4
Table 4.1
Table 4.2
Table 4.3
Table 4.4
Table 4.5
Table 4.6
Table 4.7
Table 4.8
Table 4.9
Table 4.10
Table 4.11
Table 4.12
Table 4.13
Table 4.14
Table 4.15
Table 4.16
Table 4.17
Table 4.18
Chapter 5
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 5.6
Table 5.7
Chapter 6
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Table 6.5
Table 6.6
Table 6.7
Table 6.8
Table 6.9
Table 6.10
Table 6.11
Chapter 7
Table 7.1
Table 7.2
Table 7.3
Table 7.4
Table 7.5
Table 7.6
Table 7.7
Chapter 8
Table 8.1
Table 8.2
Table 8.3
Table 8.4
Table 8.5
Table 8.6
Table 8.7
Table 8.8
Table 8.9
Table 8.10
Table 8.11
Table 8.12
Table 8.13
Table 8.14
Chapter 10
Table 10.1
Table 10.2
Chapter 11
Table 11.1
Table 11.2
Table 11.3
Table 11.4
Table 11.5
Table 11.6
Table 11.7
Table 11.8
Table 11.9
Chapter 16
Table 16.1
Table 16.2
Table 16.3
Table 16.4
Chapter 18
Table 18.1
List of Illustrations
Chapter 1
Figure 1.1 Drawbacks of Capitalization Weighting and Suggested Solutions
Figure 1.2 Implementation Perspectives
Figure 1.3 Timeline of Various Smart Beta Offerings
Figure 1.4 Desired Characteristics of Smart Beta Offerings
Chapter 4
Figure 4.1 Smart Beta Weighting Schemes: Two Broad Categories
Figure 4.2 Smart Beta Weighting Schemes: Total and Active Weight Characteristics
Figure 4.3 Weighting Schemes and Examples of Smart Beta Offerings
Figure 4.4 Total Weight Profile of SW Factor Portfolios
Figure 4.5 Total Weight Profile of EW Factor Portfolios
Figure 4.6 Active Weight Profiles of Illustrative Portfolios
Chapter 7
Figure 7.1 ST Size Portfolio: Cumulative Active Return (Russell 1000 Universe, January 197...
Figure 7.4 ST Low-Volatility Portfolio: Cumulative Active Return (Russell 1000 Universe, J...
Figure 7.5 ST Quality Portfolio: Cumulative Active Return (Russell 1000 Universe, January ...
Chapter 8
Figure 8.1 ST MFP Portfolio: Cumulative Active Return (Russell 1000 Universe, January 1979...
Figure 8.2 ST MFP Portfolio: Cumulative Active Return (MSCI World ex. USA Universe, Januar...
Figure 8.3 ST MFP Portfolio: Cumulative Active Return (MSCI EM Universe, January 1998–June...
Figure 8.5 ST MFP Portfolio: Cumulative Active Return (MSCI EM Universe, January 1998–June...
Chapter 10
Figure 10.1 Alpha-Beta Portfolio Structure
Figure 10.2 Size and Style Decomposition
Figure 10.3 Potential Key Questions in Portfolio Structuring
Figure 10.4 Decomposition of Active Return
Figure 10.5 Strategy Selection and Diversification Example
Figure 10.6 Manager Selection and Diversification Example
Figure 10.7 Weighting Strategy Buckets by Active Risk Example
Figure 10.8 Allocating between Smart Beta and Smart Alpha Examples
Figure 10.9 Summary of Proposed Structure
Figure 10.10 Core and Satellite Components of Smart Beta
Chapter 11
Figure 11.1 Process Overview: Constructing ESG Portfolios
Figure 11.2 Process Overview: Incorporating ESG with Smart Beta
Chapter 16
Figure 16.1 Traditional Investment Review Process
Figure 16.2 New Portfolio Building Blocks
Chapter 18
Figure 18.1 Growth of One Dollar Invested in Value, Size, and Momentum Factors over the Per...
Figure 18.2 Hypothetical Performance Computed from the Fama-French-Carhart Factors
Figure 18.3 The Number of Years That a Given Factor Has Underperformed the Market in This H...
Guide
Cover
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Introduction
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Acknowledgments
First of all, we wish to thank the investment practitioners who have contributed to this book. We are grateful that they took time out of their busy schedules to share their experiences relating to smart beta investing. We hope that readers will find their contributions insightful and useful.
We wish to recognize the valuable contributions and assistance provided by the following reviewers: Andrew Alford, Stephan Kessler, and Joseph Kushner.
We also wish to thank the following individuals for their insightful comments and guidance during the review process: Leyla Marrouk, Prafulla Saboo, Katie Souza, and Aicha Ziba.
Finally, this book would not have been possible without the editorial and organizational assistance of Patricia Berman and Ingrid Hanson.
Disclaimer
The views and opinions expressed in this book are those of the authors and do not necessarily reflect the views or position of any asset manager or entity the authors may be affiliated with. They are provided for informational and educational purposes only and do not constitute (and should not be relied upon as) any investment advice or recommendation. Views and opinions are current as of the date of publishing and may be subject to change. All investors are strongly urged to consult with their legal, tax, or financial advisors regarding any potential transactions or investments.
Introduction
Equity smart beta and factor investing has become a highly discussed and debated topic within the industry over the last few years. Indeed, investor surveys consistently highlight not only the increasing popularity but also adoption of smart beta investing. As an example, in the FTSE Russell 2018 Global Survey Findings from Asset Owners, which surveyed asset owners representing an estimated $3.5 trillion in assets across North America, Europe, Asia Pacific, and other regions, 77% of asset owners responded that they have already implemented, are currently evaluating implementation, or plan to evaluate smart beta strategies in the near future. The survey also found that smart beta adoption rates increased from 26% in 2015 to 48% in 2018. More interestingly perhaps, while FTSE Russell surveys in previous years showed a significantly higher adoption rate for large asset owners with more than $10 billion in assets, in this most recent survey, the adoption rates were more evenly distributed across small (39%), medium (43%), and large (56%) asset owners. In terms of adopted smart beta strategies, multifactor offerings showed the highest adoption rate (49%), followed by single factor low volatility (35%) and value strategies (28%). The growth in the adoption rate of multifactor offerings, most likely driven by a better understanding of the diversification benefits offered by a combination of lowly correlated equity common factors, appears to come at the expense of other smart beta strategies that have concentrated exposures to certain factors, such as fundamentally weighted strategies, which have seen adoption rates decline from 41% in 2014 to 19% in 2018.
In our opinion, smart beta is an important innovation in the field of investments, and its growing adoption across the industry is driven by many considerations. First, in our experience, many public and private pension plans have a 6% to 8% return requirement from their investment portfolio (actuarial rate of return) in order to meet their expected liabilities. In a low expected return environment, such return targets may become difficult to achieve without significantly increasing the allocation to equities. At the same time, some asset owners also have a desire to lower the volatility of the overall investment portfolio as well as the volatility in funding contributions and earnings, while retaining the equity allocation. Asset owners, therefore, appear to be confronted with conflicting objectives: (1) improve portfolio returns, but without increasing the equity allocation and/or (2) reduce portfolio volatility, but without lowering the allocation to equities. Smart beta investing may provide potential solutions to meet these goals. Certain smart beta offerings, such as multifactor strategies, offer the potential to improve expected returns, while keeping portfolio volatility at a level similar to that of the market. Certain other smart beta offerings, such as low-volatility strategies, provide the potential to lower overall portfolio risk, while seeking to generate market-like returns. As such, smart beta investing may allow investors to meet the objectives of return enhancement and/or risk reduction, without meaningfully altering the equity allocation.
Second, the introduction of smart beta investing, alongside active management, offers the potential to significantly improve the diversification benefits in a portfolio. Indeed, in combining smart beta with true alpha, investors can introduce multiple layers of diversification, which drive important efficiency gains (i.e. higher relative risk adjusted returns) in the overall portfolio.
Third, in our interactions with large asset owners, we find that, as the portfolio size grows, it may become progressively more difficult for these asset owners to find additional skilled active managers and/or increase the allocations to the best performing managers, as manager concentration may lead to capacity and/or manager risk constraints. Such asset owners are confronted with the problem of delivering a reasonable alpha on a large and growing asset base. In our experience, these asset owners have tended to look at certain smart beta strategies, mainly low tracking error multifactor offerings, as transparent and systematic strategies capable of delivering alpha (excess returns relative to the market portfolio) with high capacity and cost-efficiency.
Fourth, from an investment process point of view, the increasing popularity of smart beta investing can also be attributed to the fact that it seeks to combine the most attractive features of both active and index investing. Smart beta offerings often seek to capture the same sources of excess returns (i.e. factors) that active managers commonly emphasize, and that have depicted persistent market outperformance. But unlike active management, these sources of excess returns are now delivered in index-like approaches, which aim to mitigate investment process and transparency risks and provide meaningful implementation cost and management fee savings.
Fifth, as product structurers have more or less exhausted offerings based on capitalization-weighted indexes, their focus has shifted to smart beta indexes and associated products. According to Morningstar Research (2017), “A Global Guide to Strategic-Beta Exchange-Traded Products,” strategic-beta (Morningstar's terminology for smart beta) exchange-traded products (ETPs) were introduced in the United States in May 2000. As of June 2017, strategic-beta ETPs had grown to 1,320, with aggregate assets under management of US$707 billion worldwide. In fact, the rate of growth in strategic-beta ETPs and associated assets has accelerated in the recent past. For instance, from June 2016 to June 2017, strategic-beta ETPs recorded an increase in inflows of 28.3%.
Moving forward, based on our discussions and experiences with clients, we expect growth in smart beta investing to continue. For retail investors, structured smart beta products, priced significantly below traditional active and close to traditional passive, in our opinion, are likely to attract the majority of allocations. For institutional investors, although the starting allocations to smart beta are small, we expect a typical equity portfolio structure to comprise 50% capitalization-weighted passive, 25% smart beta, and 25% active in the long run. At the same time, we also note that many investors have not yet adopted smart beta investing. According to various surveys, such as the FTSE Russell 2018 Global Survey, the need for better education on topics such as how to approach and position smart beta, how to analyze and conduct due diligence, on the large number of smart beta offerings, and how to determine the best strategy or combination of strategies for a given portfolio structure, remains the most important barrier for investors to implement smart beta investing.
The need for continued investor education provides the motivation for this book. Our hope is that investment practitioners will find the content of this book relevant and useful in understanding the theoretical underpinnings of smart beta investing, analyzing and selecting appropriate smart beta strategies that meet their specific objectives, structuring more efficient portfolios by incorporating smart beta with true alpha, and, perhaps most importantly, gaining insights from other practitioners who have successfully implemented smart beta investing in their portfolios.
Overview of Book Chapters
In Chapter 1, we start by reviewing the evolution of the equity smart beta space as well as some desired characteristics of smart beta offerings. This review of the evolution of smart beta investing provides useful insights in understanding the definition and current composition of the smart beta space.
Since smart beta has over time become closely aligned with factor investing, in Chapters 2 and 3 we provide an overview of equity common factors and factor investing. Chapter 2 briefly reviews the origins and theory of factor investing. We also address topics such as why investors should care about equity factors and which specific factors have become the focus of various smart beta offerings. Chapter 3 focuses on explaining smart beta factor return premia. We discuss the risk-based, behavioral, and structural explanations for why factor premia exist, why they have persisted historically, and why they can be expected to persist going forward.
The wide variety of smart beta products available in the marketplace can sometimes be overwhelming for investors, who often struggle with how to analyze and select such products. Differences in smart beta offerings can arise from many sources, such as factor specifications, weighting schemes, and methodologies used to control turnover, diversification, or capacity. The various considerations involved in capturing smart beta factors and selecting smart beta strategies are discussed in Chapters 4, 5, and 6. In Chapter 4, we propose a simple framework for understanding some of the various weighting schemes employed to capture smart beta factor returns. We also analyze the efficiency in factor capture achieved by these weighting schemes. In Chapter 5, we discuss some of the various factor signal specifications that are commonly used in the design of smart beta products. In addition to the choice of the weighting scheme, factor signal specifications can also drive differences among the various smart beta offerings. And in Chapter 6, we analyze a large number of publicly available smart beta strategies, using the factor portfolios we construct in Chapter 4. Although our focus is on smart beta strategies, we also use these factor portfolios to conduct a risk decomposition of certain active strategies. The analysis conducted in this chapter provides useful insights in understanding the drivers of performance for smart beta and active strategies as well as assessing the efficiency of factor capture or the existence of manager skill more generally.
In Chapters 7, 8, and 9, our focus shifts to understanding the performance characteristics of smart beta factor strategies. We start by analyzing the historical performance of individual smart beta factor portfolios in Chapter 7. We discuss performance across three regions, namely, US, Developed Markets ex. US, and Emerging Markets. We adjust performance for implementation costs in order to make historical simulations potentially more representative of “live” implementation. This chapter seeks to provide insights into how factors differ in terms of their total and relative risk and return attributes as well as their performance in different market regimes. In Chapter 8, we move from individual factors to factor diversification strategies. We discuss the attractive correlation attributes of smart beta factors and show how combining factors results in improved relative risk-adjusted performance, while also potentially mitigating market underperformance risk. It is often said that diversification is the only free lunch in finance. Multifactor smart beta strategies may well represent an example of the significant benefits that can be achieved through diversification. In Chapter 9, Roger Clarke, Harindra de Silva, and Steven Thorley provide an insightful discussion relating to low-volatility investing. The authors review (1) the historical performance of the low volatility factor and explanations advanced to explain it, (2) whether the anomaly is driven by systematic or idiosyncratic risk, (3) the characteristics of the low volatility factor, such as correlation with other factors, and (4) techniques commonly used for building low volatility portfolios.
With regard to smart beta implementation and portfolio structuring, Chapter 10 analyzes various potential challenges that investors face in designing multistrategy, multimanager portfolios. These challenges partially arise from current portfolio structuring practices, which, in our opinion, do not provide adequate guidance on how to implement efficient style and manager diversification. Therefore, we propose an alternative portfolio structuring framework that seeks to improve on current practices by facilitating the building of potentially more efficient overall portfolio structures that incorporate smart beta strategies alongside active management.
Investors have an increasing desire to reflect environmental, social, and governance (ESG) values and perspectives in their overall equity portfolios. In Chapter 11, we propose a framework for incorporating ESG factors as well as combining ESG factors with smart beta factor investing. The framework emphasizes customization and transparency in performance attribution, while maintaining some degree of benchmark-awareness.
Chapter 12 provides an example of the application of factor investing beyond equities. In this chapter, Oliver Bunn outlines a factor-based approach to identifying the systematic risk exposures taken by hedge funds. These economically intuitive factors based on academic research are well-defined, liquid, and can be implemented at relatively low cost. A portfolio of these systematic factors can provide investors with access to a hedge fund-like return profile.
The remainder of the book chapters comprise contributions from practitioners who have successfully implemented or are considering implementing smart beta investing in their equity portfolios. Asset owner perspectives are provided in Chapters 13 through 15. The implementation of smart beta at California Public Employees' Retirement System (CalPERS) is discussed by Steve Carden in Chapter 13. The evolution of the smart beta program at CalPERS constitutes an interesting case study because it closely mirrors the evolution of smart beta investing in the industry, in general, from an alternative beta strategy to multifactor investing. In 2006, CalPERS adopted fundamental indexation as a mean-reversion strategy that could potentially address the perceived shortcomings of a trend-following market capitalization-weighted portfolio. As fundamental indexation was implemented and monitored over the next four or five years, an understanding was gained that the excess returns of this strategy were driven by a high exposure to the value factor. This exposed the portfolio to the significant cyclicality of value returns. As a result, over time, the focus shifted toward diversifying the value exposure with other factors, such as momentum, quality, and low volatility, which have a low or negative correlation with value but independently deliver positive excess returns in the long run. CalPERS was also an early adopter of a hybrid implementation model, which combines active and index management for implementing systematic smart beta and factor strategies. In this model, external strategies are sourced from smart beta managers as a custom index through a licensing agreement and replicated in-house by CalPERS. The hybrid implementation model has resulted in meaningful trading cost and management fee savings for CalPERS. In the next case study in Chapter 14, Hans de Ruiter discusses the design and implementation of a smart beta program at the Pensionfund TNO. Historically, TNO had allocated to equities in a passive fashion using traditional index funds. The advent of smart beta offerings provided an opportunity to include additional sources of excess returns in order to potentially improve the risk-adjusted performance of the portfolio. As such, Pensionfund TNO approached smart beta as a form of enhanced indexing that would allow the fund to partially transition the portfolio from a single-beta to a multiple-beta passive strategy. In considering smart beta, Pensionfund TNO laid out the important questions that needed addressing, such as: Which smart beta factors to focus on and why? Which smart beta strategies to consider if mitigating short-term market underperformance risk is an important objective? How to address persistence of smart beta factor premiums at a practical level? How to construct multifactor smart beta strategies? How to assess and mitigate the impact of implementation costs? And which benchmark to use for the implemented smart beta strategies? This case study provides useful insights into how Pensionfund TNO addressed these questions. Another early adopter of smart beta factor investing is the Barclays Bank UK Retirement Fund (BUKRF). In Chapter 15, Ilian Dimitrov explains how smart beta over the years has contributed meaningfully to improve the risk-adjusted returns of the overall equity allocation. Initially, at BUKRF, smart beta was used for portfolio completion and exposure management purposes with the goal of achieving a diversified and balanced exposure to certain targeted factors. In recent years, the use of smart beta has broadened to include strategies that capture a specific risk premium at low cost as well as multifactor strategies that serve as an alternative to active management in highly efficient segments of global equity markets. This case study also discusses the various challenges faced by BUKRF in the implementation of their smart beta program, the criteria used in selecting appropriate smart beta strategies, the process used to determine an allocation to smart beta, and the various considerations relating to governance, monitoring, and performance benchmarking of smart beta strategies.
Chapters 16 and 17 provide investment consultants' perspectives on smart beta. Although some investment consultants have not formed a formal, public view on smart beta investing, others, such as Willis Towers Watson (WTW), have been early advocates of such strategies. In Chapter 16, James Price and Phil Tindall from WTW discuss smart beta from an asset owner's perspective. The authors argue that smart beta has resulted in a meaningful change in the investment landscape for asset owners as it shifts the emphasis from manager selection to investment strategy selection and, hence, requires a different set of skills. Smart beta requires increased up-front governance, which also means that asset owners need to form beliefs regarding smart beta, distinguish between absolute and relative return worlds, and avoid short-termism in strategy evaluation and monitoring. In this new world, asset owners also face some challenges, such as potential crowding of smart beta factors and timing allocations to strategies, which they will need to address. In the US, Wilshire Consulting have also been one of the early advocates of smart beta investing. In Chapter 17, Andrew Junkin, Steven Foresti, and Michael Rush discuss the perspectives of Wilshire Consulting with regard to smart beta. They argue that investors consider adopting smart beta as a replacement for or complement to active management, as smart beta captures many of the systematic sources of returns that active managers also implement, but does so in a systematic, transparent, and less expensive manner. Smart beta may also be appropriate as a replacement for traditional passive for those investors who are looking to improve risk-adjusted returns of their portfolios but wish to achieve that at a reasonable cost. In the end, Wilshire Consulting believes that smart beta strategies potentially represent an effective solution for those asset owners wrestling with the current low expected return environment.
Chapters 18 and 19 focus on the potential motivations for retail investors to consider smart beta investing. In Chapter 18, Lisa Huang and Petter Kolm at Fidelity Investments and Betterment, respectively, lay out the case for retail advisors to offer a complete smart beta solution to their clients. Supported by academic evidence and declining costs of implementation via exchange traded products, the authors argue that smart beta strategies represent an interesting vehicle for building more efficient and cost-effective portfolios in the retail space. In Chapter 19, Jerry Chafkin from AssetMark addresses the potential positioning of smart beta with retail investors. In his opinion, smart beta is a disciplined and systematic approach to alpha generation, which facilitates the basic objective of active management, but with greater reliability and transparency. Smart beta is a compelling proposition for retail investors because it combines the advantages of both passive (low-cost, disciplined, and transparent) and active (potential for market outperformance) investing. One of the most important appeals of smart beta investing is that as systematic strategies they help both investors and managers set appropriate expectations and maintain discipline during difficult times. This potentially significantly improves the ability to achieve investor objectives in the long run.
Finally, Chapters 20 and 21 provide some concluding remarks, including addressing some skepticisms regarding smart beta investing.
PART IOVERVIEW OF EQUITY SMART BETA SPACE
Chapter 1Evolution and Composition of the Equity Smart Beta Space
This chapter reviews the evolution of the equity smart beta space as well as some desired characteristics of smart beta offerings. This review of the evolution of smart beta investing provides useful insights in understanding the definition and current composition of the smart beta space.
Chapter Summary
The origins of smart beta investing can potentially be traced back to research investigating the shortcomings of the capitalization-weighted market indexes. These efforts and the identified shortcomings led researchers to investigate alternative non-capitalization-weighted methodologies, such as equal-weighting, minimum variance, and fundamental weighting.
Empirical analysis of products based on these alternative weighting methodologies depicted higher risk-adjusted returns (Sharpe ratios) compared to the market index, thereby suggesting that the capitalization-weighted market index may not be as efficient as theory (the Capital Asset Pricing Model, or CAPM) would suggest.
These products were initially referred to as alternative equity betas in the academic literature. Today, the term “smart beta” is commonly used in reference to such strategies.
Risk decomposition analyses of alternative equity beta strategies revealed that these strategies derive much of their market outperformance through high exposures to equity common factors, such as size, low volatility, or value, which have been well documented in the academic literature over several decades.
As such, in our experience, investor focus shifted to capturing the equity common factors more directly and/or in a more customizable benchmark-aware implementation (an active beta perspective), which does not require replacing the capitalization-weighted market index as the policy benchmark (an alternative beta perspective).
The composition of the smart beta space, therefore, evolved from alternative equity beta strategies to a combination of alternative beta and various factor offerings.
The risk decomposition of alternative equity beta strategies also shows that, at least in terms of investment outcome, smart beta can be defined as mostly factor investing, as the continued success of alternative equity beta strategies critically depends on the persistence of various factor premia.
Factor investing is not new both from a passive as well as an active implementation perspective. What is new with regard to smart beta strategies, however, is a value-adding repackaging of factor investing. Smart beta strategies create a hybrid solution that retains the attractive features of both passive and active management. Such strategies offer characteristics that emphasize efficiency, transparency, low turnover, improved diversification and capacity, and low fees.
I. Introduction
When asked about smart beta, William Sharpe’s response was that the term makes him “definitionally sick.”1 Indeed, in the CAPM, Sharpe (1964) and others (Treynor (1961), Lintner (1965), and Mossin (1966)) provided a definition of the terms “beta” and “alpha.” Beta is the sensitivity (regression coefficient) of an asset to the capitalization-weighted market portfolio (the factor). A stock with a beta of one behaves just like the market. A stock with a beta above (below) one is more (less) risky than the market. Alpha is the return in excess of the beta-adjusted market return. Today, however, practitioners commonly use the term beta to refer to the market portfolio or some other benchmark index. That is, for practitioners beta refers to the factor itself, rather than the exposure to the factor. A beta capture typically means a passive approach, which seeks to replicate the performance of the factor or benchmark index. Returns in excess of the benchmark are referred to as “alpha,” based on the (implicit) assumption that the portfolio has a beta of one to the benchmark index.2 But, what are practitioners referring to with regard to smart beta investing?
The definition and composition of the equity smart beta space is a source of confusion in the industry. Smart beta goes by many names, such as alternative beta, systematic beta, advanced beta, exotic beta, beta prime, or active beta, and many investment strategies with seemingly quite different characteristics are lumped into the smart beta category. At the outset, smart beta strategies were designed to address the potential shortcomings of capitalization-weighted market indexes and, as such, were positioned as a more efficient non-capitalization-weighted alternative. Over time, however, the term smart beta has become closely linked with factor investing. A review of the evolution of smart beta investing provides useful background and insights into the changing perspectives and the current composition of the smart beta space.
II. Evolution of Equity Smart Beta
A. Benefits of Capitalization Weighting
The CAPM (further detailed in the next chapter) demonstrates that under some simplifying assumptions the capitalization-weighted market portfolio is the most efficient portfolio on the efficient frontier, on an ex ante basis. In other words, the capitalization-weighted market portfolio is mean-variance optimal. Under the assumption of market efficiency, investors cannot do better than this portfolio. The CAPM clearly provided the theoretical motivation for the creation of capitalization-weighted equity market indexes and their widespread use in performance benchmarking and portfolio implementation. For investors, capitalization-weighted equity market indexes also offer other practical benefits, such as high capacity, high liquidity, low turnover, easy replicability, and low fees. It is no surprise, then, that capitalization-weighted equity market indexes have gained tremendous popularity with investors. Given the widespread use of such indexes around the globe, a reasonable question is whether such indexes are as efficient as theory (CAPM) would suggest. Therefore, analyzing the potential shortcomings of capitalization weighting became an important topic of research within the industry.3
B. Potential Drawbacks of Capitalization Weighting
Criticisms of capitalization weighting tend to center on three areas: concentration, volatility, and propensity to invest in expensive stocks.
At the individual stock level, concentration refers to a few companies having a large weight in the index, which exposes investors to significant stock-specific risk.4In many countries, in fact, just a handful of names may account for a large proportion of the weight of the market index. At the end of 2017, as an example, the three largest Belgian companies trading on Euronext Brussels had an aggregate market capitalization larger than that of the remaining 130 companies combined. Capitalization-weighted market indexes can also become heavily concentrated in individual industries/sectors (e.g. the technology sector in the S&P 500 Index during the technology bubble) or even countries (e.g. Japan in the MSCI EAFE Index in the mid- to late 1980s).
Another potential drawback of capitalization weighting is that it may expose passive investors to high levels of volatility. Higher volatility may be caused by the noisy nature of market prices as well as the interaction between the speculative behavior of investors and concentration. For instance, overenthusiasm of investors may lead to overpricing in individual stocks and/or industries. Rising prices for these stocks and/or industries increases their capitalization and weights in the market index, thus causing concentration. Concentration, in turn, may force passive investors, who closely replicate the market indexes, to hold more of the overpriced stocks and/or industries. As mispricing eventually corrects, investors experience significant volatility and suffer significant losses by virtue of being overly concentrated in the most overpriced stocks and/or industries of the market. The formation of bubbles, and their subsequent bursting, may imply that investors replicating the capitalization-weighted market indexes end up taking more risk than would otherwise be needed to capture the equity risk premium. One well-known example of such dynamics is the price-to-earnings (PE) ratio as well as the weight of technology stocks in the S&P 500 Index during the technology bubble. Between 1998 and 2000, as the overenthusiasm of investors led to the doubling of valuation ratios for technology stocks, their weight in the S&P 500 Index increased from 13% in 1998 to more than 30% at the start of 2000. As the technology bubble burst, the valuations and weight of technology stocks shrunk considerably, causing passive investors to experience significant portfolio volatility and losses.
Arnott et al. (2005) identified the performance drag as another potential drawback of capitalization-weighted market portfolios. Under the assumption that market prices tend to revert to underlying fundamental values, capitalization weighting tends to overweight overvalued stocks and underweight undervalued stocks, thus introducing a potential performance drag as the mispricing inevitably corrects. The performance drag may be another reason why the capitalization-weighted market portfolio may not be optimal.
C. Suggested Solutions
To address the concentration issue, weighting schemes that provide more diversification were explored. These efforts led to the development of equal-weighted indexes, capped indexes (which limit the weight of individual stocks at a certain level, such as 5% or 10%), diversity indexes (e.g. Fernholz (1998)) and maximum diversification indexes (e.g. Choueifaty and Coignard (2008)). Empirically, these portfolios were shown to outperform capitalization-weighted market indexes, on a risk-adjusted basis, thereby suggesting that concentration risk is not rewarded over time and makes capitalization-weighted indexes less efficient than those employing weighting schemes that realize more diversification. In relation to the higher volatility of capitalization-weighted market portfolios, Haugen and Baker (1991) investigated the characteristics of the minimum-variance portfolio on the efficient frontier for US stocks and found that such portfolios realized approximately 25% total risk reduction compared to the market portfolio, without sacrificing returns. Clarke et al. (2006) corroborated these results. With significant total risk reduction and market-like returns, minimum-variance portfolios realized much higher risk-adjusted returns (Sharpe ratio) relative to the market. On an empirical basis, minimum-variance portfolios, as well as other low-risk strategies, such as risk-weighting, provided another challenge to the notion that the capitalization-weighted market portfolio is mean-variance optimal. With regard to the potential performance drag embedded in capitalization weighting, Arnott et al. (2005) showed that portfolios weighted by fundamental variables of size, such as sales or cash flows, as opposed to market capitalization, outperformed the market by about 2% per annum at similar levels of risk.5
Figure 1.1 summarizes the challenges posed by capitalization weighting and some of the solutions that have been proposed to address them.
FIGURE 1.1 Drawbacks of Capitalization Weighting and Suggested Solutions
The non-capitalization-weighted strategies mentioned earlier, along with some others, such as EDHEC’s Risk Efficient Index (Amenc et al. 2010), were positioned by their providers as an alternative for the less-efficient capitalization-weighted market portfolio. As such, these strategies were initially referred to as “alternative equity betas” (AEB) or simply “alternative betas.” Over time, however, the term “smart beta” became more commonly used.
With the emergence of AEB, another important question arose: What explains the outperformance of these strategies compared to the capitalization-weighted market indexes?
D. Risk Decomposition of Alternative Equity Betas
The outperformance of AEB seemed to challenge the basic conclusions of the CAPM, but they weren’t the only strategies to do so. Over multiple decades, the academic literature has also documented a number of “factors,” or common characteristics of companies that were shown to explain relative risk and return differences for stocks much better than CAPM beta. For instance, Fama and French (1992) provided evidence that size and value explained the cross-section of average returns better than market beta. Given the extensive evidence on the existence and performance of certain factors, a reasonable area of enquiry was to investigate whether the market outperformance of AEB could also be explained by these factors.
To answer this question, researchers conducted a risk decomposition analysis of various AEB. Typically, this analysis entails analyzing the exposures and efficiency of a strategy against the Fama-French 3-factor model (1992), which comprises of the market, size, and value factors, or the Carhart 4-factor model (1997), which also includes momentum. These analyses revealed that the market outperformance of various analyzed AEB strategies was explained by high and significant exposure to the considered factors, with no meaningful alpha being generated by the analyzed strategies against the 3-factor and 4-factor models (e.g. Chow et al. 2011). As an example, equal-weighted, diversity, and maximum diversification indexes outperformed the market because they had a high exposure to size (small cap). Minimum-variance and risk-weighted portfolios outperformed because they had a high exposure to low-beta, low-volatility stocks. And fundamentally weighted portfolios outperformed because they had a high exposure to value.
Although AEB strategies analyzed by Chow et al. (2011) as a group generated no meaningful alpha relative to the 3-factor and 4-factor models, another important topic to address was whether these strategies at least provided a higher efficiency capture of factor returns compared to the existing size and style indexes offered by index providers. Capitalization-weighted size (large/mid/small) and style (value/growth) indexes constitute the first attempt at capturing equity common factors in an indexing framework. Chow et al. (2011) showed that AEB, in general, represented an improvement over the existing capitalization-weighted size and style indexes as they delivered higher efficiency in factor capture. Specifically, in a risk decomposition analysis against the Fama-French 3-factor model, AEB did not generate negative alphas, while capitalization-weighted size and style factor indexes did.
If AEB derive their market outperformance through exposures to well-known equity common factors, then why wouldn’t investors capture these factors more directly through methodologies that deliver higher efficiency compared to capitalization-weighted size and style indexes and more flexibility compared to AEB?
E. Renewed Focus on Factor Investing: Potential Advantages of Factor Offerings
In recent years, in our experience, investor focus has clearly shifted toward new factor products that seek to deliver the following additional value-adding features compared to capitalization-weighted size and style indexes as well as AEB.
a. Enhanced Efficiency in Factor Capture
Compared to the existing capitalization-weighted size and style indexes, many new factor offerings seek to deliver higher efficiency in factor capture through the use of non-capitalization-weighted methodologies. These include capitalization-scaled weighting, signal weighting, optimized, and other weighting schemes that seek higher levels of efficiency in factor capture. The improved efficiency may be demonstrated either in the form of higher risk-adjusted returns or, perhaps more appropriately, in the form of statistically significant factor-adjusted alphas.6
b. Customizable Benchmark-Aware Portfolio Construction
The alternative beta implementation perspective adopted by most AEB, which may involve replacing the capitalization-weighted policy benchmark, poses some challenges for certain investors. Some investors may not consider capitalization weighting as inefficient but may believe in the existence of extra-market factor premia. Other investors may find replacing the capitalization-weighted policy benchmark difficult for various implementation and governance reasons, even when they have reasonable doubts about the efficiency of capitalization-weighted equity benchmarks. Such investors generally would prefer to implement desired factor tilts in a benchmark-aware fashion, which does not require a respecification of the policy benchmark. Benchmark-aware means that investors can implement specific factor tilts relative to their existing policy benchmarks, whether they are commonly used capitalization-weighted benchmarks or client-specific custom benchmarks, and at desired levels of tracking error relative to the policy benchmark. We refer to such implementations as an “active beta,” as opposed to an alternative beta, perspective. As such, benchmark-aware factor strategies offer a much higher degree of “customization” and potential risk control compared to most AEB. The main features of alternative beta and active beta perspectives are summarized in Figure 1.2.
FIGURE 1.2 Implementation Perspectives
Most AEB either are not conditioned on or derived from commonly used benchmarks and/or do not provide the ability to target specific levels of tracking error relative to client-selected policy benchmarks. AEB may be used in an active beta implementation, but they are not ideal solutions. For example, the FTSE RAFI 1000 index (a fundamentally weighted index) tends to have an average long-term tracking error of about 4% to the Russell 1000 Index (e.g. Arnott et al. 2005). So, it could be implemented as a 4% tracking error active value strategy relative to the Russell 1000 Index. However, the FTSE RAFI 1000 Index is not conditioned on the Russell 1000 Index. Its starting universe is determined from a ranking based on fundamental variables of size as opposed to market capitalization. As such, the FTSE RAFI 1000 Index has a different set of constituents than the Russell 1000 Index, which raises potential benchmark mis-fit issues for investors using the Russell 1000 Index as the policy benchmark for US Large Cap. Furthermore, the tracking error of the FTSE RAFI 1000 Index to the Russell 1000 Index is not explicitly targeted. It is a by-product of the methodology used and turned out to be an average of about 4% in the long run.
c. Factor Diversification
In our experience, the benefits of factor diversification are also now well-understood by investors. Individual factors depict tremendous cyclicality in returns (e.g. value can go in and out of favor), which exposes investors to pronounced and prolonged periods of market underperformance. At the same time, factors also depict low or negative pair-wise active return (return in excess of the benchmark) correlation, which tends to deliver significant gains from diversification. As a result, factor diversification strategies tend to dominate individual factors, as documented by many studies (e.g. Asness et al. 2009, Hjalmarsson 2009, and Ghayur et al. 2013). That is, they generate higher relative risk-adjusted returns (IR), while potentially significantly mitigating market underperformance risk. Although AEB deliver exposure to multiple common factors, they are not explicitly designed for implementing balanced factor diversification strategies. Most AEBS tend to have concentrated factor exposures. As an illustration, RAFI has a much higher exposure to value than to other factors (e.g. Chow et al. 2011), while minimum-variance portfolios are composed primarily of low-volatility, low-beta stocks (e.g. Clarke et al. 2011).
As a result of the above considerations, we believe investor interest in recent years has shifted toward more efficient, customizable, and benchmark-aware single factor and factor diversification strategies. Smart beta has become closely linked with factor investing, and factor offerings now form an important component of the smart beta landscape, in addition to the various AEB.
Figure 1.3 depicts the timeline relating to the launch of some of the smart beta offerings.
FIGURE 1.3 Timeline of Various Smart Beta Offerings
III. Desired Characteristics of Smart Beta Strategies
If smart beta has become closely linked with factor investing, then what is new about smart beta? After all, factor investing has been implemented both from passive and active perspectives for a long time. Capitalization-weighted size and style indexes were introduced in 1989, and active managers, for decades, have attempted to beat the market by gaining exposure to equity common factors.
From the perspective of the source of excess return (i.e. factors), there may not be anything new in smart beta factor investing. From the perspective of how these excess return sources are captured and delivered, smart beta is creating a value-adding repackaging of factor investing. Smart beta factor strategies are a hybrid solution that seeks to harness the attractive features of both passive and active management in capturing factor returns. Compared to traditional capitalization-weighted size and style indexes, smart beta offerings deliver a more efficient exposure to factors through alternative weighting schemes, and in many cases in a benchmark-aware, tracking error-targeted fashion. Compared to traditional active management, smart beta factor strategies differ in terms of product design, product structure, and product delivery. From the perspective of product design, smart beta strategies tend to focus on factors and factor specifications that have been researched, scrutinized, and vetted in the academic literature over multiple decades. These factors, also known as rewarded factors, have been shown to retain statistical significance in multiple testing approaches that account for the problems associated with data mining and have been demonstrably linked with persistence on an out-of-sample basis.7 In terms of product structure, smart beta investment processes tend to be characterized by simplicity and transparency, such that the sources of risk and return embedded in the investment process are well understood by investors. Smart beta offerings seek portfolio construction and implementation methodologies that are rules-based and incorporate additional features designed to mitigate turnover and improve diversification and capacity. Finally, in terms of product delivery, smart beta strategies are offered at lower fees than traditional active management and seek to provide implementation flexibility to investors. Implementation flexibility means that the strategies are offered either in fully managed separate accounts or in various forms of index-like licensing arrangements.
IV. Composition and Definition of Equity Smart Beta
In our opinion, equity smart beta could be defined in terms of investment objectives and desired characteristics of smart beta offerings. In terms of investment objectives, equity smart beta strategies seek to (1) address the potential shortcomings of capitalization weighting through alternative weighting methodologies, and/or (2) gain efficient exposure to well-documented and rewarded equity common factors.8 Therefore, the smart beta space comprises two types of offerings: AEB and factor investing. Additionally, smart beta offerings would tend to emphasize characteristics, such as transparency, low turnover, high diversification and capacity, implementation flexibility, and lower fees.
V. Typical Investor Questions
1.1 Does Smart Beta Imply That the Capitalization-Weighted Market Index Is Dumb?
The smart beta terminology lends to the implication that capitalization-weighted market indexes are dumb, and some smart beta product providers have encouraged such implications. This is, of course, quite unreasonable. Smart beta products may provide improved investment efficiency, but capitalization-weighted market indexes remain the highest-capacity, highest-investability, lowest turnover, and cheapest option to capture the equity market return. That is why many practitioners, such as the consultant Willis Towers Watson, refer to the market beta as “bulk beta.” Decades of academic research and practical experience of investors have provided support to the argument that minimizing implementation costs is an intelligent way of maximizing after-cost returns in the long run. There is nothing dumb about bulk beta and capitalization-weighted market indexes.
1.2 Should Smart Beta Be Defined More Narrowly?
Some product providers have attempted to provide narrower definitions of smart beta arguing that the term needs a more precise meaning. As an example, Arnott and Kose (2014)
