Introduction

A systematic approach to investing in corporate bond portfolios is becoming more widely used by investors as a result of the increased availability of fixed income data, improved price transparency, and the influence of established quantitative insights from the equity markets. This book is focused on new research in this area covering a broad spectrum of algorithmic credit investing: exploiting inefficiencies of benchmark indices, investing based on factors constructed using a combination of fundamental and market data as well as extending quantitative equity methodologies and signals to credit.

The authors are long‐term members of the Quantitative Portfolio Strategy (QPS) group, which has been a part of Barclays (and, previously, Lehman Brothers) Research for nearly three decades. The group has a unique focus on working with major institutional investors across the globe on all issues of portfolio management that are quantitative in nature. As a result of this focus, research results produced by the group are practical and implementable. The group's publications target portfolio managers and other investment practitioners as well as research analysts and academics. Past involvement by the QPS group in the creation and replication of Bloomberg Barclays Fixed Income Indices and its expertise in quantitative research in both equities and bonds further help it to produce innovative portfolio construction methodologies and timing signals.

This is the fourth book published by the QPS team. The group's prior books—Quantitative Credit Portfolio Management (Wiley, 2012) and Quantitative Management of Bond Portfolios (Princeton University Press, 2007)—were focused on QPS original risk measures, benchmark customization and replication, and other aspects of the investment process. The 2012 book was dedicated to credit investing while the 2007 book also included our research related to mortgage‐backed securities and rates portfolio management. One of our risk measures for credit securities—Duration Times Spread (DTS)—was broadly adopted by institutional investors since its introduction in 2005 and is the sole subject of another QPS book, A Decade of Duration Times Spread (DTS) (Barclays, 2015). Given the broad use of DTS, we continue to monitor its validity in different market regimes and credit asset classes.

In this book, we focus on our original research into systematic strategies—fully rules‐based algorithmic methodologies aimed at improving credit portfolio performance by generating alpha. Some of the strategies fall into “smart beta” category and take advantage of inefficiencies in conventional market‐value‐weighted benchmarks. Others harvest risk premia associated with risk factors, both traditional and new, and are formulated as scorecards—ranking methodologies for credit securities, issuers, and industry sectors by measures that are informative of future performance. Most of these scorecards are produced by Barclays Research on a periodic basis and are shared with clients.

All the materials included in the book reflect QPS research as it was originally published for Barclays clients. We decided against going back and updating individual chapters to avoid any possibility of hindsight tainting the results.

Credit portfolio management was originally, and still mostly is, discretionary in nature. Managers form views on issuers, industry sectors, and credit spread curves based on fundamental bottom‐up analysis and seek to implement those views using securities available in the market subject to liquidity constraints. However, there is a growing trend toward incorporating systematic (algorithmic) approaches into this process, either as additional filters of the eligible investment universe or as checks on the discretionary choices made by the fundamental manager or even—in some cases – as stand‐alone strategies. This trend is helped by the increasing availability of bond‐level index data and of large datasets that require a quantitative approach to be useful in the investment process as well as by the migration of rich, highly developed systematic equity methodologies into credit management.

Over the years, we have often heard investors question whether credit is an independent asset class or can be replaced in a portfolio by an appropriately weighted combination of Treasury bonds and equities. Part I addresses this fundamental question head‐on with a thorough empirical analysis of the role of credit in a Treasury/equities portfolio. We analyze the underlying sources of the performance difference between a credit portfolio and a risk‐matched and issuer‐matched portfolio of equities and Treasuries. To ensure that the corporate bond portfolios and the Treasury/equity portfolios in this study are exposed to the same corporate entities, we rely on an issuer‐level historical mapping between bond issuers and the associated equity tickers built by our team over time. This very detailed mapping process required that we correctly reflect all corporate events that can cause this mapping to change as well as address several technical challenges of the differences between the two markets. We rely on this mapping throughout this book for all studies and models that analyze corporate bonds using stock market data or fundamental issuer information.

Credit investing is often an index‐centric process subjecting managers to index rules and constraints. The continued popularity of low‐fee passive management, coupled with the need for pension consultants to have a basis for comparison among different managers, ensures that it will remain this way going forward. In Part II we discuss ways of exploiting index inefficiencies to generate alpha. Empirical evidence of a particular methodology generating outperformance is never sufficient for us to call it “smart beta” or systematic alpha. We always insist on economic intuition explaining which market inefficiency allows for the outperformance and whether there is a reason to expect it to persist or to mean‐revert. In this sense, index inefficiencies are among the most reliable sources of outperformance, as they stem from the rules of inclusion and elimination of securities built into the index definition. These rules, which are predefined and independent of market pricing, lead to strong demand for debt being added to the index increasing allocations to large borrowers and strong selling pressure on issuers being dropped from the index. These dynamics can often cause bonds to trade at levels that diverge from their financial fair value. We further look at the performance impact of other liquidation constraints based on rating downgrades in a credit portfolio beyond the traditional index constraints. We demonstrate their impact on portfolio performance and on the optimal allocation in a portfolio to different rating categories. This forced liquidation is one of the reasons long‐horizon investors do not always significantly overweight credit during various crises and wait for the spreads to mean revert to generate alpha. The liquidation rule may trigger a realized loss before spreads recover.

In Part III we proceed with research on the performance implications of bond portfolio characteristics: both traditional ones, such as coupon level and maturity distribution, and those that came into focus more recently such as environmental, social, and governance (ESG) rankings. We show that low‐coupon bonds offer a performance advantage over their high‐coupon peers at the time of significant changes in Treasury yields due to better price protection provided by the recovery value. We also explain the causes of the outperformance of short‐dated corporates over long‐dated peers and attribute it to market factors. Our study of the impact of an ESG tilt on credit portfolio performance was first undertaken to see whether it leads to reduced returns. Like so many of our studies, it was prompted by a large US asset manager seeking to understand whether such tilt is justified in its pension mandate, given the return maximization objective. The concern was that high‐ESG bonds might be overbought, which could lead to lower returns. We were surprised to find in a series of studies that, all else equal, an ESG tilt led to improved performance in both investment‐grade and high‐yield markets in both the United States and Europe. This finding held true using ESG rankings from different providers and over different time periods. We analyze the reasons for this outcome, both for the markets overall and for specific industry sectors.

The traditional approach to building a credit portfolio is based on allocations to industry sectors, credit ratings, spread duration buckets, and, of course, issuer selection based on fundamental bottom‐up analysis. Most institutional investors measure these allocations in terms of contribution to Duration Times Spread (DTS)—a new measure of credit risk introduced by our group in 2005. These allocations may or may not reflect priced factors in credit markets: categories of risk that is compensated by corresponding return. Also, these allocations may contain biases, such as issue size or coupon level, which may affect performance. Finally, they can be correlated. In Part IV of this book we present two priced factors in credit markets that have risk premia associated with them—value and momentum—and analyze the role of issuer size as a factor. Again, we use our proprietary mapping between bonds and equity of a given issuer to access...