Systematic Fixed Income E-Book

Scott A. Richardson

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 490

Veröffentlichungsjahr: 2022

Beschreibung

Understand the role and potential of fixed income as an asset class Systematic Fixed Income: An Investor's Guide offers readers a powerful, practical, and robust framework for investors and asset managers to preserve the diversifying properties of a fixed income allocation, and add to that unique sources of excess returns via systematic security selection. In other words, this framework allows for efficient capture of fixed income beta and fixed income alpha. Celebrated finance professional Dr. Scott Richardson presents concrete strategies for identifying the relevant sources of risk and return in public fixed income markets and explains the tactical and strategic roles played by fixed income in typical portfolios. In the book, readers will explore: * The implementation challenges associated with a systematic fixed income portfolio, including liquidity and risk * The systematic return sources for rate and credit sensitive fixed income assets in both developed and emerging markets An essential read for asset managers and institutional investors with a professional interest in fixed income markets, Systematic Fixed Income: An Investor's Guide deserves a place in the libraries of advanced degree students of finance, business, and investment, as well as other investment professionals seeking to refine their understanding of the full potential of this foundational asset class.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Dedication

Preface

Acknowledgments

About the Author

CHAPTER 1: Setting the Stage

OVERVIEW

1.1 WHAT IS FIXED INCOME?

1.2 HOW BIG ARE FIXED INCOME MARKETS?

1.3 WHAT DOES IT MEAN TO BE SYSTEMATIC?

1.4 WHICH FIXED INCOME MARKETS WILL THIS BOOK FOCUS ON?

1.5 COMMONLY USED FIXED INCOME ANALYTICS

1.6 OTHER FIXED INCOME CONSIDERATIONS

REFERENCES

NOTES

CHAPTER 2: Fixed Income – Strategic Asset Allocation

Overview

2.1 WHAT ARE THE KEY DRIVERS OF FIXED INCOME SECURITY RETURNS?

2.2 WHAT TRADITIONAL RISK PREMIA CAN BE HARVESTED IN FIXED INCOME?

2.3 THE STRATEGIC DIVERSIFICATION BENEFIT OF FIXED INCOME

2.4 IS THE STRATEGIC DIVERSIFICATION BENEFIT OF FIXED INCOME THREATENED IN A LOW‐INTEREST‐RATE ENVIRONMENT?

REFERENCES

CHAPTER 3: Fixed Income – Tactical Asset Allocation

OVERVIEW

3.1 MARKET TIMING – TERM PREMIUM

3.2 MARKET TIMING – CREDIT PREMIUM

3.3 OTHER CONSIDERATIONS

REFERENCES

CHAPTER 4: Incumbent Active Fixed Income Managers

OVERVIEW

4.1 FRAMEWORK FOR ACTIVE FIXED INCOME MANAGEMENT

4.2 US AGGREGATE (CORE PLUS) BENCHMARKED FIXED INCOME MANAGERS

4.3 GLOBAL AGGREGATE BENCHMARKED FIXED INCOME MANAGERS

4.4 UNCONSTRAINED BOND FUNDS

4.5 EMERGING MARKET FIXED INCOME MANAGERS

4.6 CREDIT LONG/SHORT MANAGERS

REFERENCES

CHAPTER 5: Security Selection – Rate‐Sensitive Assets

OVERVIEW

5.1 WHAT IS THE INVESTMENT OPPORTUNITY SET FOR DEVELOPED MARKET GOVERNMENT BONDS?

5.2 REDUCING THE DIMENSIONALITY

5.3 A FRAMEWORK FOR SECURITY SELECTION OF GOVERNMENT BONDS (INVESTMENT THEMES)

5.4 A FRAMEWORK FOR SECURITY SELECTION OF GOVERNMENT BONDS (LEVEL, SLOPE, AND CURVATURE)

5.5 EXTENSIONS

REFERENCES

CHAPTER 6: Security Selection – Credit‐Sensitive Assets

OVERVIEW

6.1 WHAT IS THE INVESTMENT OPPORTUNITY SET FOR DEVELOPED MARKET CORPORATE BONDS?

6.2 DIMENSIONS OF ACTIVE RISK TAKING WITHIN CORPORATE BONDS

6.3 A FRAMEWORK FOR SECURITY SELECTION OF CORPORATE BONDS (INVESTMENT THEMES)

6.4 A FRAMEWORK FOR SECURITY SELECTION OF CORPORATE BONDS (PERFORMANCE)

6.5 EXTENSIONS

REFERENCES

CHAPTER 7: Security Selection – Emerging Markets (Hard Currency)

OVERVIEW

7.1 WHAT IS THE INVESTMENT OPPORTUNITY FOR EMERGING MARKET FIXED INCOME?

7.2 A FRAMEWORK FOR SECURITY SELECTION OF HARD CURRENCY EMERGING MARKET BONDS (INVESTMENT THEMES)

7.3 A FRAMEWORK FOR SECURITY SELECTION OF EMERGING MARKET HARD CURRENCY BONDS (PERFORMANCE)

7.4 EXTENSIONS

REFERENCES

CHAPTER 8: Portfolio Construction Considerations

OVERVIEW

8.1 CHOICES IN THE INVESTMENT PROCESS (DESIGN AND INVESTMENT UNIVERSE)

8.2 CHOICES IN THE INVESTMENT MODEL (EXPECTED RETURNS)

8.3 CHOICES IN THE PORTFOLIO CONSTRUCTION PROCESS (OPTIMIZATION, REBALANCING, TRADING)

8.4 OTHER TOPICS

REFERENCES

CHAPTER 9: Liquidity and Trading Considerations

OVERVIEW

9.1 SOME CONTEXT FOR THE LIQUIDITY CHALLENGES OF FIXED INCOME ASSETS

9.2 BASICS FOR TRADING CREDIT‐SENSITIVE ASSETS

9.3 ELECTRONIFICATION OF TRADING FOR CREDIT‐SENSITIVE ASSETS

9.4 PRIMARY MARKETS – LIQUIDITY PROVISION

9.5 SECONDARY MARKETS – LIQUIDITY PROVISION

9.6 ANCILLARY TOPICS

REFERENCES

NOTES

CHAPTER 10: Sustainability

OVERVIEW

10.1 INTEREST IN ESG/SUSTAINABILITY

10.2 SUSTAINABLE INVESTING WITH CREDIT‐SENSITIVE ASSETS

10.3 SUSTAINABLE INVESTING WITH RATE‐SENSITIVE ASSETS

REFERENCES

NOTE

CHAPTER 11: Putting It All Together

OVERVIEW

11.1 WHAT MIGHT A SUCCESSFUL SYSTEMATIC FIXED INCOME INVESTING PROCESS LOOK LIKE?

11.2 SOME FINAL THOUGHTS

REFERENCE

Index

End User License Agreement

List of Exhibits

Chapter 1

EXHIBIT 1.1 Market capitalization of global fixed income markets as of Decem...

EXHIBIT 1.2 Market capitalization of global fixed income markets as of Decem...

EXHIBIT 1.3 Market capitalization of Bloomberg Global Aggregate Index as of ...

EXHIBIT 1.4 Composition of Bloomberg Global Aggregate Index as of December 3...

EXHIBIT 1.5 A comparison of systematic and discretionary investment approach...

EXHIBIT 1.6 The size of the systematic fixed income investing universe.

EXHIBIT 1.7 Cash flow profile of a $100 10‐year coupon bond issued on Januar...

EXHIBIT 1.8 Yield to worst of Bloomberg Global Aggregate Index and subindice...

EXHIBIT 1.9 Proxy government bond yields from 1304 to 2020.

EXHIBIT 1.10 Relation between price and yield for a $100 10‐year coupon bond...

EXHIBIT 1.11 Maturity profile of Bloomberg Global Aggregate Index and subind...

EXHIBIT 1.12 Duration profile of Bloomberg Global Aggregate Index and subind...

EXHIBIT 1.13 Domicile breakdown of institutional funds benchmarked to the US...

EXHIBIT 1.14 Domicile breakdown of institutional funds benchmarked to the Gl...

Chapter 2

EXHIBIT 2.1 Total returns (annualized averages and standard deviation) for G...

EXHIBIT 2.2 Total return decomposition into “rates” and “spread” components ...

EXHIBIT 2.3 Standard deviation contribution of “rates” and “spread” return c...

EXHIBIT 2.4 Total return variance decomposition into “rates” and “spread” co...

EXHIBIT 2.5 Correlations of USD monthly total returns across Global Treasury...

EXHIBIT 2.6 Correlations of USD monthly

excess

returns across Global Treasur...

EXHIBIT 2.7 Rolling 36‐month Sharpe ratios of USD monthly

excess

returns acr...

EXHIBIT 2.8 Cumulative and rolling 36‐month excess returns for US government...

EXHIBIT 2.9 Cumulative and rolling 36‐month excess returns for US corporate ...

EXHIBIT 2.10 Cumulative and rolling 36‐month excess returns for US mortgage‐...

EXHIBIT 2.11 Annualized returns and rolling 10‐year correlation for US bonds...

EXHIBIT 2.12 Drawdowns for US bonds and US stocks over the 1926–2020 period....

Chapter 3

EXHIBIT 3.1 Visual representation of the expected return for a risk‐free bon...

EXHIBIT 3.2 Value signals – term premium. Panel A shows the effect of cappin...

EXHIBIT 3.3 Momentum signals – term premium. Panel A shows the effect of cap...

EXHIBIT 3.4 Carry signals – term premium. Panel A shows the effect of cappin...

EXHIBIT 3.5 Active returns from tactically timing term premium.

EXHIBIT 3.6 Cumulative excess returns from long‐term government bonds and as...

EXHIBIT 3.7 Value signals – Credit Premium (US IG). Panel A shows the effect...

EXHIBIT 3.8 Momentum signals – Credit Premium (US IG). Panel A shows the eff...

EXHIBIT 3.9 Carry signals – Credit Premium (US IG). Panel A shows the effect...

EXHIBIT 3.10 Value signals – Credit Premium (US HY). Panel A shows the effec...

EXHIBIT 3.11 Momentum signals – Credit Premium (US HY). Panel A shows the ef...

EXHIBIT 3.12 Carry signals – Credit Premium (US HY). Panel A shows the effec...

EXHIBIT 3.13 Active returns from tactically timing credit premium (US IG Cor...

EXHIBIT 3.14 Active returns from tactically timing credit premium (US HY Cor...

EXHIBIT 3.15 Cumulative excess returns from US IG corporate bonds and associ...

EXHIBIT 3.16 Cumulative excess returns from US HY corporate bonds and associ...

EXHIBIT 3.17 Relation between credit and equity values and underlying asset ...

Chapter 4

EXHIBIT 4.1 Forecasting 10‐year US nominal yields.

EXHIBIT 4.2 Correlation of rolling three‐month returns across out‐of‐benchma...

EXHIBIT 4.3 Traditional risk premia proxies.

EXHIBIT 4.4 Regression analysis for US Core Plus funds.

EXHIBIT 4.5 Relative frequency histogram of correlation between excess of be...

EXHIBIT 4.6 Scatter plot of equally weighted average US Core Plus fund exces...

EXHIBIT 4.7 Relative frequency histograms of annualized active returns and a...

EXHIBIT 4.8 Regression analysis for Global Aggregate Funds.

EXHIBIT 4.9 Scatter plot of equally weighted (EW) average Global Aggregate f...

EXHIBIT 4.10 Relative frequency histograms of annualized active returns and ...

EXHIBIT 4.11 Regression analysis for Unconstrained Bond Funds.

EXHIBIT 4.12 Scatter plot of equally weighted average Global Unconstrained B...

EXHIBIT 4.13 Relative frequency histograms of annualized active returns and ...

EXHIBIT 4.14 Regression analysis for Global Aggregate Funds.

EXHIBIT 4.15 Relative frequency histograms of annualized active returns and ...

EXHIBIT 4.16 Regression analysis for credit long/short hedge funds.

EXHIBIT 4.17 Relative frequency distribution of credit hedge fund correlatio...

EXHIBIT 4.18 Relative frequency histograms of annualized active returns and ...

EXHIBIT 4.19 Scatter plot of (equal weighted) net‐of‐fee credit hedge fund r...

Chapter 5

EXHIBIT 5.1 Market capitalization of ICE/BAML Global Government Bond (W0G1) ...

EXHIBIT 5.2 Number of unique issuers and issues in ICE/BAML Global Governmen...

EXHIBIT 5.3 Number of unique issues per issuer in ICE/BAML Global Government...

EXHIBIT 5.4 Cash flow profile of a $100 10‐year zero‐coupon bond issued on J...

EXHIBIT 5.5 Cash flows of three risk‐free government bonds used to create a ...

EXHIBIT 5.6 Synthetic zero‐coupon bond prices and yields for our case study ...

EXHIBIT 5.7 First three principal components (PC1, PC2, PC3) for US zero‐cou...

EXHIBIT 5.8 Visualizing value opportunities along the yield curve.

EXHIBIT 5.9 Properties of systematic investment themes (V for value, M for m...

EXHIBIT 5.10 Properties of country “level” systematic investment themes (V f...

EXHIBIT 5.11 Properties of country “slope” systematic investment themes (V f...

EXHIBIT 5.12 Properties of country “curvature” systematic investment themes ...

Chapter 6

EXHIBIT 6.1 Market capitalization (USD) of developed‐market corporate bond i...

EXHIBIT 6.2 Number of corporate issuers across developed market corporate bo...

EXHIBIT 6.3 Number of corporate bonds (issues) across developed‐market corpo...

EXHIBIT 6.4 Duration of corporate bonds (issues) across developed‐market cor...

EXHIBIT 6.5 Option adjusted spreads (OAS) of corporate bonds (issues) across...

EXHIBIT 6.6 Fraction of private issuers across developed market corporate bo...

EXHIBIT 6.7 Breakdown of credit spread changes into level and slope componen...

EXHIBIT 6.8 Receiver operating characteristic (ROC) curve for evaluation of ...

EXHIBIT 6.9 Graphical representation of

(distance to default).

EXHIBIT 6.10 Properties of corporate bond security selection investment them...

EXHIBIT 6.11 Properties of corporate bond security selection investment them...

EXHIBIT 6.12 Properties of corporate bond security selection investment them...

EXHIBIT 6.13 Effect of liquidity on corporate bond spreads for US IG (left) ...

EXHIBIT 6.14 Effect of liquidity on corporate bond credit‐excess returns for...

EXHIBIT 6.15 Example of a pruned tree.

Chapter 7

EXHIBIT 7.1 Country representation in emerging markets fixed income.

EXHIBIT 7.2 Number of sovereign and quasi‐sovereign issuers of emerging mark...

EXHIBIT 7.3 Credit rating breakdown of emerging market hard currency bonds....

EXHIBIT 7.4 Cross‐sectional distribution of credit spreads for emerging mark...

EXHIBIT 7.5 Cross‐sectional distribution of spread duration for emerging mar...

EXHIBIT 7.6 Properties of emerging market hard currency bond security select...

Chapter 8

EXHIBIT 8.1 Visualization of the systematic investment process.

EXHIBIT 8.2 Visualization of the investment model (the investment cube).

EXHIBIT 8.3 Z‐scored (entire cross‐section) momentum signal for a liquid cor...

EXHIBIT 8.4 Z‐scored ranks (within sector) momentum signal for a liquid corp...

EXHIBIT 8.5 Beta‐neutralized Z‐scored ranks (within sector) momentum signal ...

EXHIBIT 8.6 Scatter plot of optimized portfolio weights and the beta‐neutral...

Chapter 9

EXHIBIT 9.1 Overview of relative liquidity and trading challenges across sto...

EXHIBIT 9.2 Visualization of the corporate bond market structure with dealer...

EXHIBIT 9.3 Quoting and trading conventions for IG corporate bonds. Hypothet...

EXHIBIT 9.4 Example of liquid (GE) and illiquid (CENT) IG corporate bonds. H...

EXHIBIT 9.5 Quoting and trading conventions for HY corporate bonds. Hypothet...

EXHIBIT 9.6 Example of liquid (CHTR) and illiquid (HCC) HY corporate bonds. ...

EXHIBIT 9.7 Example of market liquidity for CDX.IG and CDX.HY contracts. Hyp...

EXHIBIT 9.8 Example of market liquidity for JNK ETF contract. Hypothetical B...

EXHIBIT 9.9 Differences in electronic trading across asset classes.

EXHIBIT 9.10 Cumulative returns of corporate bonds in the first month after ...

EXHIBIT 9.11 Timeline for primary markets.

EXHIBIT 9.12 Hypothetical Bloomberg 1→many axes.

EXHIBIT 9.13 Growth in green bond issuance.

EXHIBIT 9.14 Visualizing of transaction cost analysis (TCA).

Chapter 10

EXHIBIT 10.1 Histogram showing the fraction of variation in credit spreads e...

EXHIBIT 10.2 Temporal variation in the relation between measures of sustaina...

EXHIBIT 10.3 Histogram showing the statistical significance of measures of s...

EXHIBIT 10.4 Regression results of Equation (10.4). The four corporate bond ...

EXHIBIT 10.5 Histogram showing the fraction of variation in credit spreads e...

EXHIBIT 10.6 Reduction in portfolio level expected returns from increasing s...

EXHIBIT 10.7 Cumulative performance of sustainable systematic global high yi...

EXHIBIT 10.8 Measures of governance and their relation to corporate bond ret...

EXHIBIT 10.9 A framework for measuring country‐level sustainability.

EXHIBIT 10.10 Global map showing country‐level sustainability scores.

EXHIBIT 10.11 United Nations sustainable development goals.

EXHIBIT 10.12 Measures of governance and their relation to government bond r...

Chapter 11

EXHIBIT 11.1 Scatter plot of excess of benchmark returns for a systematic IG...

EXHIBIT 11.2 Scatter plot of excess of benchmark returns for a systematic lo...

EXHIBIT 11.3 Scatter plot of excess of benchmark returns for a systematic US...

EXHIBIT 11.4 Scatter plot of excess of cash returns for a systematic credit ...

EXHIBIT 11.5 Scatter plot of excess of benchmark returns for a systematic em...

EXHIBIT 11.6 Regression analysis for systematic emerging market bond fund. R...

EXHIBIT 11.7 Scatter plot of excess of benchmark returns for a systematic Gl...

EXHIBIT 11.8 Regression analysis for systematic Global Aggregate strategy. R...

EXHIBIT 11.9 Scatter plot of excess of cash returns for a systematic Unconst...

EXHIBIT 11.10 Regression analysis for systematic Unconstrained Bond strategy...

EXHIBIT 11.11 Holdings analysis of discretionary and systematic HY corporate...

Guide

Cover Page

Title Page

Copyright

Dedication

Preface

Acknowledgments

About the Author

Table of Contents

Begin Reading

Index

End User License Agreement

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Additional Praise for Systematic Fixed Income: An Investor's Guide

“Systematic investing – driven by models and data – has made major inroads in equity investment, but not in fixed income. This book is long overdue, and Scott Richardson is the ideal author. Combining his clear grasp of theory, coupled with his long practitioner experience, it is a ‘must read’ for fixed‐income investors.”

— Stephen Schaefer, Professor, London Business School

“This book is a must read for any serious investment professional or aspiring student interested in systematic fixed income. Scott Richardson is one of the most experienced hands‐on global professionals in this space bringing a unique combination of insights that span both academia and industry.”

— Andrew Jackson, Head of Research, Vinva Investment Management

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For a list of available titles, visit our Web site at www.WileyFinance.com.

Systematic Fixed Income

An Investor's Guide

SCOTT A. RICHARDSON, PHD

Copyright © 2022 by Scott Richardson. 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: Richardson, Scott (Accounting professor), author.

Title: Systematic fixed income : an investor's guide / Scott Richardson, Ph.D.

Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2022] |

Series: Wiley finance series | Includes bibliographical references.

Identifiers: LCCN 2022010444 (print) | LCCN 2022010445 (ebook) | ISBN 9781119900139 (cloth) | ISBN 9781119900238 (adobe pdf) | ISBN 9781119900191 (epub)

Subjects: LCSH: Fixed-income securities.

Classification: LCC HG4650 .R53 2022 (print) | LCC HG4650 (ebook) | DDC 332.63/2044—dc23/eng/20220302

LC record available at https://lccn.loc.gov/2022010444

LC ebook record available at https://lccn.loc.gov/2022010445

Cover Design: Wiley

Cover Image: © KTSDESIGN/Getty Images

To İrem

and

Sean

Preface

I wrote this book to help fill a void between theoretical fixed income asset pricing and the practicalities of investing in fixed income securities. Fixed income is a ubiquitous component of asset owner portfolios. Fixed income markets are enormous (well in excess of $100 trillion USD) and have traditionally been seen as a powerful diversifier alongside equity market allocations. To date, incumbent investment approaches for active risk taking in fixed income are typically discretionary. These discretionary approaches tend to be dominated by reaching for yield (spread) behavior that dampens the strategic diversification benefit of a fixed income allocation. With the advent of improved data sources (from pre‐ and post‐trade price transparency to enhanced fundamental data insights for issuers of debt) systematic investment approaches can now be feasibly applied to fixed income markets. The potential for asset owners is enormous: a way to preserve the diversifying potential of fixed income as an asset class and add excess returns via security selection.

This book lays out a framework for identifying the relevant sources of risk and return in public fixed income markets. After a comprehensive analysis of the strategic and tactical roles that fixed income can play in asset allocation, the book covers the systematic return sources for rate and credit sensitive fixed income assets across developed and emerging markets. Armed with an understanding of return drivers, the book then explores the implementation challenges (e.g., liquidity, risk) that need to, and can, be overcome to successfully build a systematic fixed income portfolio. Putting it all together, the reader will appreciate the powerful diversifying potential of a well‐implemented systematic fixed income allocation.

Although the book is primarily targeted to institutional asset owners and investors with an interest/responsibility for the fixed income asset class, the content is also suitable for advanced‐degree students and other investment professionals looking to expand their knowledge of fixed income investment approaches.

Acknowledgments

There are many people to thank for the content of this book. It is the result of a dual career spanning academia and the investment community. Without excellent mentors and peers in both spheres I would not be where I am today.

On the academic side, my advisor, Richard Sloan, has always been and continues to be a source of inspiration and sound guidance. Over the years academic colleagues from University of Sydney (undergraduate days), University of Michigan (formative PhD years), University of Pennsylvania (assistant professor days) and London Business School (tenured professor life) have continued to support and challenge all my research. I thank them all.

On the investment side, there are too many individuals to thank individually. My formative investment years at Barclays Global Investors (BGI) were amazing. I am thankful for the opportunity to have been part of the original large‐scale effort for systematic fixed income investing at BGI. The breadth of research talent across asset classes (stocks, bonds, currencies, and commodities) was truly breathtaking. Internal research seminars were on par with the quality of seminar attendees and critical discussion at the top business schools of the day. Over the past decade, I was fortunate to work with an excellent set of colleagues at AQR, where, again, the breadth of research talent across asset classes and the willingness to collaborate across asset classes was fantastic. After BGI, I thought I would never find a similar group of smart, engaged people to work with, but I was wrong. The founders, Cliff Asness, David Kabiller, and John Liew, helped cultivate that curiosity and collaboration. A special thanks for the time and resources provided to me at the end of 2021 and early 2022 to complete work on this book.

Thank you to all my co‐authors over the years on the many academic and practitioner‐oriented research papers focused on fixed income. That material, and the associated discussions/experiences, is the collective knowledge base of this book. I thank you all, especially Navneet Arora, Attakrit Asvanunt, Jordan Brooks, Maria Correia, Peter Diep, Andrea Eisfelt, Tony Gould, Johnny Kang, Ronen Israel, Stephen Lok, Diogo Palhares, Lukasz Pomorski, İrem Tuna, and Zhikai Xu.

At both BGI and AQR I had the opportunity to develop systematic fixed income businesses and do so in an environment that appreciated the diversification benefit of such an investment approach. If I can communicate that opportunity and create a new set of believers in this opportunity through this book I will be happy.

I also thank the entire publication team at Wiley, with special thanks to Bill Falloon, Samantha Enders, Purvi Patel, and Samantha Wu for their efforts in streamlining the publication process and making this process enjoyable. And to William Allen, Alfie Brixton, Michael Doros, Atif Ellahie, Antti Ilmanen, Thom Maloney, Jon Peress, and Kevin Rauseo, thank you for the discussions and feedback over the past few months in helping this book come to life.

About the Author

Scott Richardson is a senior advisor (former principal) at AQR Capital Management, where he was the co‐head of fixed income and a senior member of the Research and Portfolio Management team. He was also involved with the equity research team for the firm's Global Stock Selection group. Prior to AQR, Scott held senior positions at BlackRock (Barclays Global Investors), including head of Europe equity research and head of global credit research, where he oversaw research and investment decisions at BGI for both total return and absolute return products across credit and equity markets. Scott is a professor of practice at London Business School, where he teaches graduate‐level classes, including systematic investing in fixed income (an elective whose materials this book is based on). He began his career as an assistant professor at the University of Pennsylvania. He is an editor of the Review of Accounting Studies and has published extensively in leading academic and practitioner journals. In 2009 he won the Notable Contribution to Accounting award for his work on earnings quality and accruals. Scott earned a BEc with first‐class honors from the University of Sydney and a PhD in business administration from the University of Michigan.

CHAPTER 1Setting the Stage

OVERVIEW

This chapter defines key terms that will be used throughout the book. I start by describing fixed income securities and the size of the global fixed income markets. I introduce the term systematic and distinguish it from quantitative. All fixed income market participants are quantitatively aware; after all commonly used analytics like duration and convexity require a little more than elementary school mathematics. However, not all fixed income investors are systematic in how they translate their investment narratives into portfolios. That is what it means to be a systematic investor: prespecifying your investment hypotheses (narrative) and then converting that to an algorithm that generates trades and ultimate portfolio positions. We will explore the key ingredients of that algorithm as we proceed through the book. Finally, while this book is designed for fixed income investors and not financial engineers, resulting in minimal mathematical proofs, it is still important that commonly used analytics like yields, durations, and convexity are well understood. We will cover the intuition of these concepts, and their limitations, in detail.

1.1 WHAT IS FIXED INCOME?

This book is focused on understanding the investment opportunities available to asset owners from the fixed income markets. We need to define what makes a financial asset a fixed income security. But let's first start with a brief discussion of what a financial asset is to help set the stage for what is to come later in this chapter. All financial assets provide the owner a right to share in the cash flows generated from ownership. The price of a financial asset today will reflect expected cash flows for today, tomorrow, and all future time periods until the security ceases to exist. You don't need to hold the security to maturity to receive the actual cash flows: the price of the security will capture expectations (albeit noisily) of all future cash flows. Implicit in this last statement is an equivalence of cash flows that accrue over different time periods. Of course, there is a complicated discounting that is applied to expected future cash flows to arrive at a price. These statements are true for all financial assets, whether they be common stocks or bonds or any other contractual claim.

We can start with a simple general equation linking the price of a financial asset to its expected cash flow participation rights:

(1.1)

Equation 1.1 is a discrete time pricing formula generalizable to all financial assets. E[] captures expectations based on information today with respect to future cash flows, CF. These cash flows are discounted back to today, reflecting not just time value of money considerations but also perceived risk of associated cash flows. (We will have more to say on discount rates and its components throughout this book.) Fixed income securities are relatively unique, relative to equity securities, in two key respects. The key is in the name of the asset class: “fixed” income. First, the numerator is less important from a security valuation perspective (cash flows are “fixed”). Expected future cash flows for fixed income securities (the numerator) are typically known in advance, with almost complete certainty for truly risk‐free securities. Uncertainty in the numerator (one‐sided for fixed income and more two‐sided for equity) is increasing in the risk that the issuer will be unable to deliver those future cash flows (e.g., a risky corporate issuer). Generally, fixed income security pricing is dominated by the denominator. In contrast, equity securities require detailed forecasting of both the numerator and denominator for any meaningful security valuation. Some might be tempted to say fixed income investing is easier as a result. Alas, it is not; it is just that your focus is shifted to the denominator. Second, fixed income securities have limited lives, and the life of the fixed income security is typically also “fixed.” Of course, there are complexities with embedded options that can alter (usually shorten) the life of a fixed income security, but fixed income securities generally have a prespecified time to pay cash flows. This has very important implications for valuation of the cash flows. As time passes the value of the claim will change, absent any changing views of the expected cash flows. This gives rise to unique investment opportunities and challenges for fixed income securities (e.g., the importance of carry for identifying expected returns and the complications of modeling the deterministic time‐varying risk profile of fixed income securities), all of which we will cover in detail later in this book.

So where do the fixed income securities come from? Entities of various forms require capital to finance their operating and investing activities. The most common entities that issue fixed income securities are (i) governments and quasi‐government entities, and (ii) corporations. We will focus on fixed income securities from these two issuers in this book. Governments and corporations from all countries engage in debt financing across both developed and emerging markets. Our focus will be on developed markets, but there will be some discussion of the unique risks and investment opportunities in emerging market debt in Chapter 7. There is also a large set of asset‐backed fixed income securities. These are largely repackaging of other fixed income securities into pools where the cash flow rights are reassigned to new fixed income securities. Perhaps the largest securitized market is the government sponsored mortgage‐backed securities in the United States. But there are other large pools of securitized assets globally such as covered bonds issued by financial institutions (largely in Europe) and nonagency mortgages. We will have less to say on securitized assets in this book, outside of the prepayment risk premium to be discussed in Chapter 2. However, the security valuation frameworks and portfolio construction considerations we cover for the more common government and corporate fixed income securities can be tailored across the full breadth of fixed income securities.

1.2 HOW BIG ARE FIXED INCOME MARKETS?

Let's start broad when assessing the size of the potential investment opportunity set for the fixed income asset class. Although there are no universally accepted statistics for the true size of fixed income (or indeed equity) markets, the Bank for International Settlements is a commonly used reference for this purpose. Exhibit 1.1 shows the size of global fixed income markets as of December 31, 2020. Clearly, the global fixed income market is enormous, accounting for a little over $123 trillion USD. This estimate is an attempt to capture broadly investible fixed income markets. Ilmanen (2022) notes that the size of global fixed income markets could be closer to $200 trillion if all money market securities and all bank loans were included. Global equity markets, in contrast, were valued at $106 trillion USD as of December 31, 2020.1 There is clearly a concentration in debt securities in developed markets, but there is an increasing presence of Chinese domiciled issuers over the past decade.

An alternative way to understand global fixed income markets is to assess the relative importance of issuer type. Exhibit 1.2 shows that government entities (includes supranationals and quasi‐sovereign issuers) account for a little more than half of total fixed income securities outstanding. Financial institutions (inclusive of asset‐backed securities and regular bonds) account for around a third of global fixed income securities, and nonfinancial corporations account for the remainder. This explains our focus on government and corporate fixed income securities, as these account for most global fixed income securities.

EXHIBIT 1.1 Market capitalization of global fixed income markets as of December 31, 2020. All securities are valued in USD. Fixed income securities are grouped into countries based on the domicile of the issuer and are dependent on source data availability at the country level.

Source: Data from Bank for International Settlements (www.bis.org/statistics).

The debt securities included in Exhibits 1.1 and 1.2 include domestic debt securities (DDSs) and international debt securities (IDSs). The Bank for International Settlements (BIS) defines DDS as those instruments issued in the local market of the country where the borrower resides, regardless of the currency in which the security is denominated, and IDSs as those instruments outside the local market of the country where the borrower resides. Approximately 80 (20) percent of fixed income securities are DDSs (IDSs) as of December 31, 2020. Correspondingly, our focus will be on domestic fixed income securities, as this reflects the bulk of the fixed income investment opportunity set. This is not to say that international securities are not relevant. They are, and in Chapter 7 we will discuss hard currency emerging market debt specifically. It is important to remember that although the issuance of debt securities by an issuer in multiple countries (and multiple currencies) does expand the investment opportunity set, these additional securities, while not totally redundant, are usually highly correlated with the domestic security.

EXHIBIT 1.2 Market capitalization of global fixed income markets as of December 31, 2020. All securities are valued in US dollars. Fixed income securities are grouped into categories based on the nature of the issuing entity.

Source: Data from Bank for International Settlements (www.bis.org/statistics).

A final point to note about the size of the global fixed income market is that the BIS data is based on cash securities (i.e., bonds issued by governments and corporations). In addition to these cash securities, there is an extensive derivatives market covering fixed income. These include (i) interest rate derivatives (futures, options, and swaps) linked to the underlying cash bonds issued by governments (both developed and emerging markets), (ii) derivatives linked to specific securitized assets (e.g., the to be announced [TBA] market for mortgage pass‐through securities in the United States), (iii) single‐name credit derivatives (credit default swaps), and (iv) index‐level credit derivatives (e.g., Markit and iTraxx indices). These markets are also very large, and a key benefit of these derivative markets is the concentration of liquidity into specific issuers and specific issues for a given issuer (although a company typically has only common equity claim outstanding, it may have many bonds outstanding). As we will see in greater detail later in this book (especially Chapter 9 on liquidity), there is oftentimes difficulty in sourcing inventory for a specific fixed income security.

A driver of the limited liquidity in fixed income, especially relative to equity markets, is the breadth of securities to select from for a given issuer. For example, as of November 30, 2021, there were 1,744 bonds in the Global Treasury subindex within the Bloomberg Global Aggregate Index. Of these 1,744 bonds, 267 were issued by the United States and 554 were issued by what is collectively referred to as the G‐7 (excluding the United States): Japan (274), Italy (82), Germany (57), the UK (55), France (47), and Canada (39). There are many securities to choose from for each government issuer. A similar concentration in issuance is seen for corporate bonds where there are roughly seven bonds outstanding for each investment‐grade‐rated corporate issuer. As we'll see in detail in Chapters 5 (6) for government (corporate) fixed income securities, there is considerable redundancy across the multiple issues for a given issuer. Stated differently, bonds issued by the same issuer share a considerable amount of common variation. Investors are spoiled by breadth, and this leads to a bifurcation in liquidity across many redundant securities. Blackrock (2014) has noted the lack of standardization in the corporate bond market as a key driver of liquidity challenges. Although there are institutional reasons for the lack of standardization (e.g., issuance fees for intermediaries, cash flow maturity management from corporate treasury departments), there is limited need for so many securities from an investor perspective. A small number of securities (as few as two or three) can capture most of the return variation that is available to the investor. And this is where the derivative market can be very useful for the fixed income investor: liquidity can be concentrated in a small number of tenors for each issuer. Unfortunately, derivative markets are liquid for many but not all issuers and many but not all key tenors.

We can undertake a more detailed analysis of the investment opportunity set for fixed income investors by looking at the Bloomberg Global Aggregate Index. This is a very broad index commonly used as a policy benchmark by many large institutional asset owners. It contains investment‐grade‐rated bonds issued in multiple currencies that meet specific liquidity requirements (e.g., par value more than $300 million USD for USD‐denominated debt). As of December 31, 2020, the Global Aggregate Index included 26,514 individual bonds amounting to $67.5 trillion USD outstanding. Exhibit 1.3 shows the breakdown of the market capitalization of the Global Aggregate Index. The $67.5 trillion USD is broken down into (i) $35.9 trillion USD for Global Treasury (TSY) securities (includes all investment‐grade‐rated debt issued by developed market sovereign entities), (ii) $10.0 trillion USD issued by government‐related entities (GREL), (iii) $12.7 trillion USD issued by corporate (CORP) entities (includes all investment‐grade‐rated debt issued by corporations domiciled in developed markets), and (iv) $8.9 trillion USD of securitized (SEC) debt (the majority of which is mortgage‐backed securities from the main US government agencies).

EXHIBIT 1.3 Market capitalization of Bloomberg Global Aggregate Index as of December 31, 2020.

Source: Data from Bloomberg Indices.

Exhibit 1.4 shows the breakdown of the number of issues contained within the Global Aggregate Index as at December 31, 2020. The 26,514 issues are comprised of (i) 1,681 bonds issued by developed sovereign entities, (ii) 5,828 bonds issued by government related entities, (iii) 13,831 bonds issued by corporate entities, and (iv) 5,174 bonds issued across agency and nonagency asset‐backed securities, commercial mortgage‐backed securities, and covered bonds. The proportional composition of the Global Aggregate Index looks very different when viewed through the lens of number of instruments; there are fewer government bonds outstanding, but they have a much larger market value per bond, and there are far more corporate bonds outstanding, but they have a much smaller market value per bond. The smaller issue size of corporate bonds is related to the liquidity challenges discussed earlier that we will return to in Chapter 9.

EXHIBIT 1.4 Composition of Bloomberg Global Aggregate Index as of December 31, 2020.

Source: Data from Bloomberg Indices.

1.3 WHAT DOES IT MEAN TO BE SYSTEMATIC?

We are talking about systematic active investing in fixed income markets. Now that we have a handle on fixed income markets, we need to agree on what it means to be systematic in your investment approach. This is challenging. A systematic investor cannot be defined as an investor who has a quantitative approach. After all, as we will see later in this chapter, all investors in fixed income markets need to be able to understand (if not compute) derivatives (I mean duration and convexity, which we will cover in detail later in this chapter), which would seem to qualify most fixed income investors as quantitatively able. So, what then distinguishes a systematic investor? And if you are not a systematic investor, then what are you? These are related questions, and to help answer these questions I will make use of a simple visual (Exhibit 1.5) that is adapted from an AQR Alternative Thinking (2017) publication titled “Systematic vs Discretionary.”

Exhibit 1.5 identifies both differences and similarities between systematic and discretionary approaches. While it can be natural to think of investment approaches as mutually exclusive, this would be a disservice to both approaches. So, let's first emphasize what is, and must be, common across the investment approaches. Both systematic and discretionary investors are active investors. That means they are both willing to entertain that the market is not completely efficient with respect to certain value‐relevant information, and that they have the investment acumen to identify that information and trade upon it profitably. It may also mean that they believe that markets are efficient but that there are opportunities to provide liquidity to the market to take advantage of price‐taker traders (e.g., avoiding bad selling practices from rating downgrades or other index exclusions, and/or actively seeking out new issue concessions).

EXHIBIT 1.5 A comparison of systematic and discretionary investment approaches.

Source: Data from AQR (2017). Systematic vs Discretionary. Alternative Thinking Quarterly, Q3 2017.

Both systematic and discretionary active investors must have a sound understanding of the sources of returns and risks within the fixed income market. Again, almost by definition, systematic and discretionary investors must share some common beliefs about the determinants of fixed income security returns. Indeed, I have often been told over the years that what we say and do, as systematic fixed investors, at a high level, sounds very familiar to a discretionary investment approach. A few years ago, I gave a presentation in front of a large internal team of fixed investment professionals at a large sovereign wealth fund, and I followed a senior investment professional from a large traditional (discretionary) fixed income asset manager. The head of the internal fixed income team spoke to me after the event and commented, “You sound very similar to the traditional (discretionary) manager, yet your final portfolios are quite different.” It is that difference that this book seeks to identify and promote the diversifying potential of. A good systematic process should be trying to capture the best bits of a fundamentally driven discretionary approach and apply that in a highly diversified manner.

As we will see in Chapter 2, understanding risk‐free yields and spreads is at the heart of fixed income investing. This is true for both the systematic and discretionary investor. A systematic investor does not have access to a secret sauce allowing them privileged access to the data‐generating process that gives rise to asset returns. If only that were true! This book will lay out a fundamental framework to understand the risks and returns for the most common fixed income securities. Both discretionary and systematic fixed investors are likely to share common beliefs on this fundamental framework. The intersection of the Venn diagram in Exhibit 1.5 is a nod to this similarity in core investment beliefs, noting that the Venn diagram is not drawn to scale (if it were, the common area would be much larger, as I think the investment approaches are more similar to each other than they are different).

So, what then is the difference between systematic and discretionary investment approaches? I believe the primary differences are the reliance on individuals (discretionary) rather than a repeatable process (systematic), and the reliance on an investment narrative/story (discretionary), rather than an investment model (systematic). These differences may sound superficial at first glance, but they are what distinguishes a systematic process from a discretionary approach. The “narrative” in a systematic investment process is a set of measurable characteristics capturing fixed income assets that have the potential to generate higher excess returns. For fixed income this will include measures of value, quality, carry, and momentum (and other measures). These characteristics will be measured across a wide set of fixed income securities (the hundreds of government bonds and thousands of corporate bonds discussed earlier in this chapter). The “narrative” in a discretionary investment process will typically reflect a combination of top‐down macro views (on inflation, growth, etc.) and detailed bottom‐up credit analysis on specific issuers. The implicit belief is that this deeper contextual analysis is the source of investment value add. Both approaches have their merits (breadth for systematic and depth for discretionary).

The systematic approach does not rely on any one individual on a day‐to‐day basis to make trading decisions. Trade lists are generated as the result of a systematic process. That is not to say individuals do not look at the trade list. They do, just that the role of the individual is to confirm the integrity of the data and ensure that there are not any unmodeled news or risks that would negate the purpose of the intended trade (e.g., your systematically generated buy list includes a bond from a corporate issuer for which overnight, there was news of a leveraged buyout or some other corporate action). In those cases, there is a clear role for human intervention to take risk off the table in a controlled manner. Indeed, these interventions can also be built in a somewhat systematic manner. In contrast, the discretionary approach leans heavily and regularly on the lead portfolio manager. That individual is the holder of investment decision rights, and their narrative is inherently less objective than a prespecified systematic process. That approach has the potential benefit of responsiveness to changing market conditions and changing drivers of fixed income returns. However, it comes with the risk of individual biases that may be the root source of investment opportunities to start with. The investment approaches are different in their implementation of what I think are reasonably similar core investment beliefs/narratives.

The final portfolios are very different in expected ways. A more diversified portfolio typically means (i) more line items in the final portfolio (this characteristic is common for systematic portfolios), and (ii) lower tracking error as a direct consequence of the more diversified portfolio. This does mean that a discretionary fixed manager has the potential to generate higher excess returns, but that comes with additional risk. What ultimately matters for asset owners is the excess return generated per unit of active risk (a ratio of return to risk is called a Sharpe ratio or an information ratio, depending on the return that is examined: excess of cash returns is a Sharpe ratio, and excess of beta/benchmark returns is an information ratio). There is minimal evidence to suggest the information ratios are different across systematic or discretionary managers (for equity markets, AQR 2017, note that while the excess of benchmark returns are similar for systematic and discretionary managers, discretionary managers have a higher tracking error leading to slightly lower information ratios). We will discuss the Sharpe ratios and information ratios of incumbent active fixed income managers in detail in Chapter 4.

It is also important to remember that not all systematic fixed income managers will be the same. Just as not all discretionary managers are expected to hold similar portfolios, there is no reason that all systematic managers will hold the same portfolio. Indeed, research in equity markets has shown that the average pairwise correlation between excess returns for systematic active equity managers is as low as the average pairwise correlation between excess returns for discretionary active equity managers (see e.g., Lakonishok and Swaminathan 2010, and AQR 2017). As we work through the systematic fixed income framework developed in this book, hopefully the breadth of choices that must be made to build a final portfolio will become clear. Variations in those choices across asset managers will lead to meaningful differences in portfolio outcomes. At the end of the day, what matters most to an asset owner is a well‐implemented (read liquidity and risk aware) portfolio that targets exposure to your investment hypothesis (read narrative for the discretionary manager or investment model for the systematic manager). It is those signals and implementation details that we will cover in detail in later chapters.

A word of caution is useful for the term discretionary. In a pure sense, all active investors are discretionary. A systematic investment approach requires many decisions to be made from everything to selecting data vendors to signal construction, signal weighting choices, various liquidity, and risk management choices, and so forth. All these decisions are discretionary in that a human (or group of humans) must make them. What is different is the stage of the investment process at which those decisions are made. A systematic investor is trying to make discretionary decisions at the start of the investment process. Think of these as strategic decisions that are then codified into a repeatable and scalable investment process.

A recipe for a systematic investment approach might look like the list that follows. I first heard this described when working at Barclays Global Investors (Richard Grinold was a key proponent) and I used this framework to summarize a broad literature on accounting‐based anomalies in the equity market back in 2010 (see Richardson, Tuna, and Wysocki 2010):

Credible hypothesis: Does your investment idea pass a “sniff test”? What is it about your idea that would lead to future excess returns? Why does the market price not already reflect this information? What informational inefficiency or liquidity provision are you targeting? This is a subjective conceptual discussion. To the extent that there is discretion in the systematic investment process, this is the stage where it is most important.

Robust predictive power: Take your investment idea to the data and assess whether it robustly predicts future returns. This empirical exercise should look for as many possible markets to test the idea (developed and emerging markets, different time periods, other related asset classes, etc.).

Test the mechanism: Flesh out the implications of the investment hypothesis. If your measure is related to mispricing, then look for evidence beyond simple return correlations. If the investment idea is attributable to market participants failing to fully utilize information that is useful for forecasting fundamentals (e.g., inflation or growth for government bonds or free cash flows for corporate bonds) then test whether your “signal” can forecast future fundamentals in addition to future returns. Go one step further and assess whether capital market participants who are in the business of forecasting these fundamentals make systematic forecasting errors with respect to your information. These non‐return‐based tests add substantial conviction to the efficacy of your investment idea. In‐sample fitting of returns (data mining) is a serious risk. Although many have criticized the lack of out‐of‐sample results in finance and advocate for multiple‐testing adjusted test statistics, a simple protection for this risk is robust testing of the underlying economic mechanism.

Implementation: Test whether your investment idea survives the various implementation challenges. This would include expected transaction costs, potential market impact, inability to source inventory at time of trade, risk‐based position limits, and so forth. Many ideas that look good on paper are too costly to implement in practice.

Additivity: Is your investment idea additive to what is already in the portfolio? Although there is a path dependency here to be handled (i.e., the first signal would seem to get a free pass by this criteria) this is critically important, as what matters is the active risk and return at the final portfolio level. If you keep adding highly correlated signals, you will end up with a very unbalanced portfolio.

There are clearly some necessary ingredients for a systematic investment approach to be adopted. Most notable is the need for reliable data. All the aforementioned testing procedures require data. That data needs to exist and needs to be point‐in‐time (i.e., available historically at the same time that market participants had access to it) and of reliable quality, so any hypothetical portfolio (back‐test) is at least partially informative of what you may expect to experience in the future. One other key aspect for the success of systematic investing is liquidity in the markets in which the assets trade (both primary and secondary markets). There are fixed income markets (e.g., municipal bond markets and cash mortgage‐backed securities) that are inherently ill suited for standard systematic investment processes. There is little point building an investment infrastructure to generate trade lists for assets that do not trade after issuance or that trade very infrequently. Trading as a liquidity taker in fixed income markets can be very costly, especially relative to the excess return potential, so liquidity in the market is almost a necessary condition to utilize a systematic investment approach. We will discuss the importance of liquidity provision for the successful implantation of systematic fixed income portfolios in Chapter 9.

How common are systematic investment approaches within the fixed income asset class? It is very difficult, if not impossible, to answer this question precisely. We can look at regular databases that store information about investment vehicles. Exhibit 1.6 shows the size and breakdown of institutional systematic fixed income investment vehicles as at December 31, 2020. To construct this chart various choices are made. Investment vehicles are long‐only and fixed income benchmarked and the definition of systematic is made subjectively based on an understanding of which asset managers have described themselves as having a systematic approach to their investment philosophy. Using this subjective criteria and fund data from eVestment, the total size of the systematic fixed income investing universe is about $120 billion USD. That is tiny relative to the size of the overall fixed income universe. But this total has grown significantly over recent years, adding more than $50 billion in the past five years. Exhibit 1.6 obviously misses a considerable portion of actively managed systematic fixed income assets. For example, there could be internal pools of capital run systematically by asset owners, and there could be internal systematic tools used to complement traditional discretionary approaches. However, even including conservative estimates for the potential additional systematic fixed income approaches, systematic approaches are in the minority. Exhibit 1.6 notes that corporate strategies (both investment‐grade and high‐yield) account for roughly half of systematic fixed income assets and government bond or aggregate index mandates account for the rest. As we will see throughout this book, government and corporate bonds are the two areas in which a systematic approach is most likely to succeed: there is sufficient data access and market liquidity.

EXHIBIT 1.6 The size of the systematic fixed income investing universe.

Source: Data from eVestment.

What does the small size of actively managed systematic fixed income funds mean? I am a firm believer in the notion of equilibrium. The fact that something does not exist, or exists only at small scale, may speak to the inefficiency of that practice. That is certainly a possible interpretation of the small scale of active systematic fixed income investing. However, there is another, more optimistic interpretation. It is difficult to successfully implement a systematic approach in fixed income, and it is challenging to convince asset owners of the efficacy of this alternative investment approach. I am a firm believer in this optimistic interpretation. Indeed, one can view this book as a continued attempt to beat the drum of the legitimacy of systematic fixed income investing. This is a challenge to be embraced by asset managers and asset owners alike. Asset managers, and to some extent asset owners assessing whether, and how, to develop internal systematic capabilities, need to overcome inertia and cultural frictions. There is natural skepticism of new approaches, and incumbents are naturally averse to new things that threaten their existence. Of course, just because something is new does not mean it is better. Fixed income, and especially credit sensitive assets like corporate bonds, face considerable liquidity challenges and security idiosyncrasies that may make it too difficult to be successful as a systematic investor. So, a healthy dose of self‐doubt is necessary, and we will cover these data and liquidity challenges in detail in Chapters 6 and 9. But, this introspection on investment challenges should be applied uniformly across all active fixed income investors. After all, concerns about liquidity, the ability to trade into and out of assets, and confidence in your ability to measure expected returns for idiosyncratic assets are ultimately a data challenge. And data is central to any investment process, whether it is systematic or discretionary.

1.4 WHICH FIXED INCOME MARKETS WILL THIS BOOK FOCUS ON?