CHAPTER
FIFTY-ONE
GLOBAL CREDIT BOND PORTFOLIO MANAGEMENT

JACK MALVEY, CFA

Chief Global Markets Strategist
BNY Mellon Asset Management

Corporate bonds are the most demanding and fascinating subset of the global debt capital markets. The label, corporate, understates the scope of this burgeoning asset class. As commonly traded and administered within the context of an overall debt portfolio, the “corporate asset class” actually encompasses much more than pure corporate entities. Instead of the title, corporate asset class, this segment of the global bond market really should be classified as the credit asset class, including non-agency mortgage-backed securities (MBS), commercial mortgage-backed securities (CMBS), and asset-backed securities (ABS). Sovereigns and government-controlled entities with foreign currency debt issues thought to have more credit risk than the national government also should be included. In keeping with conventional practice in the debt market, however, the application of the term credit asset class in this chapter will pertain only to corporate bonds, sovereigns, and government-controlled entities.

From six continents, thousands of organizations (corporations, government agencies, projects, and structured pools of debt securities) with different credit “stories” have sold debt to sustain their operations and to finance their expansion. These borrowers use dozens of different types of debt instruments (first mortgage bonds, debentures, equipment trust certificates, subordinated debentures, medium-term notes, floating-rate notes, private placements, preferred stock) in multiple currencies (dollars, yen, euros, sterling, Swiss francs, reals, renminbi) from maturities ranging from one year to even a thousand years. Sometimes, these debt structures carry embedded options, which may allow for full or partial redemption prior to maturity at the option of either the borrower or the investor. Sometimes, the coupon payment floats with short-term interest rates or resets to a higher rate after a fixed interval or a credit-rating change.

Parts of this chapter are adapted from “Relative-Value Methodologies for Global Credit Bond Portfolio Management,” Chapter 5 in Frank J. Fabozzi (ed.), Fixed Income Readings for the Chartered Financial Analyst Program (New Hope, PA: Frank J. Fabozzi Associates, 2004).

Investors buy credit assets because of the presumption of higher long-term returns despite the assumption of credit risk. Except immediately prior and during recessions, credit products usually outperform local government bond benchmarks like U.S. Treasury securities and other higher-quality “spread sectors” such as U.S. agency securities, mortgage-backed securities, and asset-backed securities. In the 38-year period since the beginning of the Barclays/Lehman indexes (1973–2010), U.S. investment-grade credit outperformed U.S. Treasuries by 29 basis points (bp) per year on average (8.31% versus 8.02%) as shown in Exhibit 51–1.1 As usual, an average masks the true daily, weekly, monthly, and annual volatility of credit assets relative performance. Looking at the rolling five-year excess returns of U.S. investment-grade credit from 1988 through 2010 in Exhibit 51–1, there have been extended periods of generous (1991–1995) and disappointing returns (2004–2008) for credit assets.

EXHIBIT 51–1
Rolling 5-year Excess Return of U.S Investment-Grade Credit: 1988–2010

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Global credit portfolio management presents a complex challenge. Each day hundreds of credit portfolio managers face thousands of choices in the primary (new issue) and secondary markets. In addition to tracking primary and secondary flows, investors have to keep tabs on ever-varying issuer fundamentals, creditworthiness, acquisitions, earnings, ratings, and prices cast in multiple gauges (bond price, nominal spread, interest-rate swap spread, and credit default swap spread). The task of global credit portfolio management is to process all this rapidly changing information about the credit markets (issuers, issues, dealers, and competing managers) and construct the portfolio with the best return for a given risk tolerance. This discipline combines the qualitative tools of equity analysis with the quantitative precision of debt analysis.

Exhibit 51–2 illustrates the magnitude of this information-processing challenge. From a set of 5,000 different issuers, investors can assemble 4 × 10 (55) different combinations of 20-bond portfolios. The number of potential portfolio combinations of 20 bonds expands to the infinity neighborhood with the inclusion of additional variables such as rating (20 choices), issues (100,000), and currencies (at least 20). Incredibly, the number of potential combinations of this 20 bond credit portfolio exceeds the neutrons in the known universe. In turn, this begs the question of whether credit portfolio “optimization” is truly achievable given the current state of technology. Although perfect optimization may prove elusive, the optimization goal remains a worthy pursuit for asset managers.

EXHIBIT 51–2
U.S. Investment-Grade Credit vs. U.S. Treasury: 1973–2010

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Despite this apparent limitation on the perfection of credit portfolio optimization, broad demand exists for credit debt. Investors in credit debt consist of individuals in the pursuit of incremental yields above government bonds, central banks aiming to extract a higher yield and return on their considerable holdings of fixed income assets, commercial banks arbitraging the difference between the greater yields on floating-rate notes and their lower cost of funding, mutual funds attempting to maximize both yield and total return, insurers and state pension funds seeking to fund their projected long-term liabilities, “pure” total-return maximizers competing against each other on a monthly, quarterly, and annual basis to satisfy their clients (public or private pension fund plan sponsors) or risk their loss, and hedge funds staking out leveraged long or short positions in credits with short-term potential for major price and spread movements. Portfolio investment choices are driven also by the existing security population of the credit market (sector, issuer, structure, and currency) and often the need to constrain tracking error deviations from broad corporate indices, by the psychology of portfolio managers (overall risk tolerance, shortfall risk aversion, and internal politics of the investment-management institution), and the state of market liquidity.

Borrowers and investors intersect mainly through dealers in both the classic telephone form and increasingly through “e-market techniques” such as websites and e-mails. Each day a few dozen credit bond dealers convey information about secondary positions and new issue offerings from any of the thousands of corporate borrowers to the hundreds of corporate bond portfolio managers. Through their investment banking and syndicate operations, dealers also advise issuers on when and how to sell new debt. Through their debt research, sales, and trading arms, dealers relay investment recommendations to portfolio managers.

As shown in Exhibit 51–3, the task of global credit bond portfolio management is to process rapidly changing information about the credit bond market (cash and derivative prices, industry and issuers’ fundamentals, ratings, issuance, demand, dealer market-making, and competing asset managers) and construct a portfolio with the best expected return for a given risk tolerance.

EXHIBIT 51–3
Global Credit Sector Asset Allocation Methodology

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CREDIT RELATIVE-VALUE ANALYSIS

Credit portfolio management represents a major subset of the multi-asset global portfolio management process illustrated in Exhibit 51–4. After setting the currency allocation (in this case, dollars were selected for illustration convenience) and distribution among key debt asset classes (Treasuries, agencies, asset-backed securities, commercial mortgage-backed securities, and mortgage-backed securities), bond managers are still left with a lengthy list of questions to construct an optimal credit portfolio. Some examples are:

EXHIBIT 51–4
Fixed Income Portfolio Management Process

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• What stages are the global and local business cycles (peak, descending to recession, recession, ascending to peak)?

• How are overall capital market liquidity conditions and dealer receptiveness to trading?

• Will structural changes in broad market themes, geopolitical risk, regulation, rating agency philosophy, and portfolio management methodology affect valuations?

• Should U.S. investors add U.S. dollar–denominated bonds of non-U.S. issuers?

• Should global credit market investors in 2011 bank on ultimate fiscal recovery of key European sovereigns (Greece, Ireland, Portugal, Spain, Italy) or wait for more information?

• Should central banks and Sovereign Wealth Funds add high-quality euro-denominated corporate bonds or high-quality emerging market sovereigns to their reserve holdings?

• Should LIBOR-funded London-based portfolio managers buy fixed-rate U.S. industrial paper and swap into floating-rate notes?

• Should Japanese mutual funds own euro-denominated telecommunications debt swapped back into dollars or yen using currency swaps?

• Should U.S. insurers buy perpetual floaters (i.e., floaters without a maturity date) issued by British banks and swap back into fixed-rate coupons in dollars using a currency/interest rate swap?

• When should investors reduce their allocation to the credit sector and increase allocation to governments, pursue a “strategic upgrade trade” (sell Baa/BBBs and buy higher-rated Aa/AA credit debt), rotate from industrials into utilities and/or financial institutions, switch from consumer cyclicals to noncyclicals, overweight airlines and underweight telephones, or deploy a credit derivative (e.g., short the high-yield index or reduce a large exposure to a single issuer by selling an issuer-specific credit default swap) to hedge their portfolios?

To respond to such questions, managers need to begin with an analytical framework (relative-value analysis) and to develop a strategic outlook for the global credit markets.

Relative Value

Economists have long debated the concept and measurement of “value.” But fixed income practitioners, perhaps because of the daily pragmatism enforced by the markets, have developed a consensus about the definition of value. In the bond market, relative value refers to the ranking of fixed income investments by geographic regions, sectors, structures (i.e., fixed versus floating rate), issuers, and issues in terms of their expected performance over some future period of time (horizon).

For a day trader, relative value may carry a maximum horizon of a few minutes. For a dealer, relative value may extend from a few days to a few months. For a total-return investor, the relative-value horizon typically runs from one to three months. For a large insurer, plan sponsor, and sovereign wealth fund, relative value usually spans a multiyear horizon. Accordingly, relative-value analysis refers to the methodologies used to generate such rankings of expected returns.

Classic Relative-Value Analysis

There are two basic approaches to global credit bond portfolio management—top-down approach and bottom-up approach. The top-down approach focuses on high-level allocations among broadly defined credit asset classes. The goal of top-down research is to form views on large-scale secular economic and industry developments like geopolitical risk, demographics, climate change, energy, global monetary and fiscal policies, global trade and capital flow imbalances, and plan sponsor investment philosophy. These views then drive asset allocation decisions (overweight certain sectors, underweight others). The bottom-up approach seeks to identify individual issuers and issues that will outperform their peer groups. Managers follow this approach hoping to outperform their benchmark owing to superior security selection while maintaining mainly neutral weightings to the various sectors in the benchmark.

Classic relative-value analysis is a dialectic process combining the best of top-down and bottom-up approaches as shown in Exhibit 51–5. This process blends the macro input of chief investment officers, strategists, economists, and portfolio managers with the micro input of credit analysts, quantitative analysts, and portfolio managers. The goal of this methodology is to pick the sectors with the most potential upside, populate these favored sectors with the best representative issuers, and select the structures of the designated issuers at the yield-curve points that match the investor’s overall duration target and perspective on the Treasury benchmark yield-curve.

EXHIBIT 51–5
Credit-Sector Portfolio Management Process: Classic, Dialectic Relative-Value Analysis

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For many credit investors, the use of classic relative-value analysis usually leads to portfolio management success. Although sector, issuer, and structural analyses remain the core of superior relative-value analysis, the increased availability of information and technology has transformed the analytical process into a complex discipline. Credit portfolio managers have far more data than ever on the total returns of sectors, issuers, and structures; quantity and composition of new-issue flows; investor product demand; aggregate credit-quality movements; multiple sources of fundamental and quantitative credit analyses on individual issuers; and yield-spread data to assist them in their relative-value analysis.

Relative-Value Methodologies

The main methodologies for credit relative-value maximization are

• Total-return analysis

• Primary market analysis

• Liquidity and trading analysis

• Secondary trading rationales and constraints analysis

• Spread analysis

• Structure analysis

• Credit-curve analysis

• Credit analysis

• Asset allocation/sector analysis

In the sections that follow, we discuss each of these methodologies.

TOTAL-RETURN ANALYSIS

The goal of global credit portfolio management for most investors is to optimize the risk-adjusted total return of their credit portfolio. The best place to start is naturally total-return analysis. Accordingly, credit relative-value analysis begins with a detailed dissection of past returns and a projection of expected returns. For the entire asset class and major contributing subsectors (such as banks, utilities, natural gas pipelines, sovereigns, Baa/BBBs, etc.), how have returns been generated? How much is attributed to credit-spread movements, sharp changes in the fundamental fortunes of key issuers, and yield-curve dynamics? If there are macro determinants of credit returns (the total return of the credit asset class), then credit markets may display regular patterns. For instance, the macroeconomic cycle is the major driver of overall credit-spreads. With the approach of and during recessions, the escalation of default risk widens spreads (which are risk premiums over underlying, presumably default-free government securities—or swaps) and reduces credit returns relative to Treasuries. Conversely, economic prosperity reduces bankruptcies and enhances overall credit fundamentals of most issuers. Economic growth usually leads to tighter credit-spreads and boosts credit returns relative to Treasuries. For brief intervals, noncyclical technical factors can offset fundamentals. For example, the inversion of the U.S. Treasury yield-curve in 2000 actually led to wider credit-spreads and credit underperformance despite solid global economic growth and corporate profitability.

Thanks to the development of total-return indexes for credit debt (databases of prices, spreads, issuer, and structure composition), analyses of monthly, annual, and multiyear total returns have uncovered numerous high-frequency patterns (i.e., large issue versus small issue performance variation, seasonality, election-cycle effects, and government benchmark auction effects) in the global credit market. Although they do not always recur, an awareness and understanding of these regular patterns is essential to optimizing portfolio performance.

PRIMARY MARKET ANALYSIS

The analysis of primary markets centers on new-issue supply and demand. Supply is often a misunderstood factor in tactical relative-value analysis. Prospective new supply induces many traders, analysts, and investors to advocate a defensive stance toward the overall credit market as well as toward individual sectors and issuers. Yet the premise, “supply will hurt spreads,” which may apply to an individual issuer, does not generally hold up for the entire credit market. Credit-spreads are governed by many factors; supply, although important, represents one of many determinants of spreads. During most years, increases in issuance (most notably during the first quarter of each year) are associated with market-spread contraction and strong relative returns for credit debt. In contrast, sharp supply declines are accompanied frequently by spread expansion and a major fall in both relative and absolute returns for credit securities. For example, this counterintuitive effect was most noticeable during August–October 1998 (Russian devaluation/default and Long-Term Capital Management implosion) and August–December 2008 (fall of Lehman Brothers and onset of global financial panic) when new issuance nearly disappeared in the face of a substantial increase in credit-spreads.

In the investment-grade credit market, heavy supply is often associated with spread compression and boosts relative returns for credit assets as new primary valuations validate and enhance secondary valuations. When primary origination declines sharply, secondary traders lose reinforcement from the primary market and tend to reduce their bid spreads. Contrary to the normal supply–price relationship, relative credit returns often perform best during periods of heavy supply. For example, 2009–2010 will be recalled for both the then all-time record for new credit origination and the best relative performance for global credit securities in decades.

The Effect of Market-Structure Dynamics

Given their immediate focus on the deals of the day and week, portfolio managers often overlook short- and long-term market-structure dynamics in making portfolio decisions. Because the pace of change in market structure is often gradual, market dynamics have less effect on short-term tactical investment decision making than on long-term strategy.

The composition of the global credit bond market shifted markedly over the last third of the twentieth century and continues in the twenty-first century. For example, medium-term notes (MTNs) dominated issuance in the front end of the credit yield-curve (1–7 year maturities) since the 1980s. Structured notes and swap products heralded the introduction of derivative instruments into the mainstream of the credit market in the 1990s. The high-yield corporate sector became a widely accepted asset class in the 1980s, with emerging market debt following in the 1990s. Global origination became more popular since the early 1990s for U.S. government agencies Supranationals (e.g., World Bank), sovereigns, and large corporate borrowers.

Although the ascent of derivatives and high-yield instruments stood out during the 1990s, the quickening march toward full credit market globalization was the most important structural trend. The rapid development of the Eurobond market since 1975, the introduction of many non-U.S. issuers into the dollar markets during the 1990s, and the birth of the euro on January 1, 1999 have led to the proliferation of truly transnational credit portfolios. The accelerating expansion of local-currency denominated debt origination, especially from Brazil, Russia, India, and China (the so-called BRICs), likely will be recalled as the most prominent evolutionary feature of early twenty-first century global credit markets.

These long-term structural changes in the composition of the global credit asset class arise owing to the desire of issuers to minimize funding costs under different yield-curves and yield spreads, as well as the needs of both active and asset/liability bond managers to satisfy their risk and return objectives. Portfolio managers will adapt their portfolios either in anticipation of or in reaction to these structural changes across the global credit markets.

The Effect of Product Structure

Partially offsetting this proliferation of issuers since the mid 1990s, the global credit market has become structurally more homogeneous. Specifically, bullet and intermediate-maturity structures have come to dominate the credit market. A bullet maturity means that the issue is not callable, putable, or sinkable prior to its scheduled final maturity. The trend toward bullet securities does not pertain to the high-yield market, in which callables remain the structure of choice. With the hope of credit-quality improvement, many high-yield issuers expect to refinance prior to maturity at lower rates.

There are three strategic portfolio implications for this structural evolution. First, the dominance of bullet structures translates into scarcity value for structures with embedded call and put features. That is, credit securities with embedded options have become rare and therefore demand a premium price. Typically, this premium (price) is not captured by option-valuation models. Yet, this “scarcity value” should be considered by managers in relative-value analysis of credit bonds.

Second, bonds with maturities beyond 20 years are a small share of outstanding credit debt. This shift reduced the effective duration of the credit asset class and cut aggregate sensitivity to interest-rate risk. For asset/liability managers with long-time horizons, this shift of the maturity distribution suggests a rise in the value of long-credit debt and helps to explain the warm reception afforded, initially at least, to the handful of new offerings of issues with 100-year maturities in the early and mid 1990s as well as 2010.

Third, the use of credit derivatives has skyrocketed since the early 1990s. The rapid maturation of the credit derivative market has led investors and issuers to develop new strategies to match desired exposures to credit sectors, issuers, and structures. In particular, many high-frequency traders (dealer desks, hedge funds, and active total return managers) prefer to execute their long and short credit positions in highly liquid portions of the CDS market rather than in conventional cash credit securities.

LIQUIDITY AND TRADING ANALYSIS

Short- and long-term liquidity needs influence portfolio management decisions. Citing lower expected liquidity, some investors are reluctant to purchase certain types of issues such as small-sized issues (less than $1 billion), private placements, MTNs, and nonlocal corporate issuers. Other investors gladly exchange a potential liquidity disadvantage for incremental yield. For investment-grade investors with even a medium-term horizon of more than six months, these liquidity concerns often are exaggerated.

The liquidity of credit debt changes over time. Specifically, liquidity varies with the economic cycle, credit cycle, shape of the yield-curve, supply, and the season. As in all markets, unknown shocks, such as a surprise wave of defaults or an eruption of geopolitical risk as in the immediate wake of 9/11, can reduce credit debt liquidity as investors become unwilling to purchase new issues at any spread and dealers become reluctant to position secondary issues except at very wide spreads. In reality, these transitory bouts of illiquidity mask an underlying trend toward heightened liquidity across the global credit asset class. With a gentle push from regulators, the global credit asset class is well along in converting from its historic “over-the-counter” domain to a fully transparent, equity/U.S. Treasury–style marketplace. In the late 1990s, new technology led to creating ECNs (electronic communication networks), essentially electronic trading exchanges. And in July 2002, the U.S.-based regulatory agency, FINRA, introduced the Trade Reporting and Compliance Engine (TRACE) to track publicly all institutional-sized trades of credit securities. In turn, credit bid/ask spreads generally have shifted lower for very large, well-known credit issues. This powerful combination of technological innovation and competition promises the rapid development of an even more liquid and efficient global credit market during the twenty-first century.

SECONDARY TRADE RATIONALES

Capital market expectations constantly change. Recessions may arrive sooner rather than later. The yield-curve may steepen rather than flatten in anticipation of monetary policy adjustments. The auto and paper cycles may be moving down from their peaks. Higher oil and natural gas prices may enhance the credit quality of the energy sector. An industrial may have announced a large debt-financed acquisition, earning an immediate ratings rebuke from the rating agencies. A major firm may plan to repurchase 15% of its outstanding common stock (great for shareholders but leading to higher financial leverage for debt holders). In response to such daily information flows, portfolio managers amend their holdings. To understand trading flows and the real dynamics of the credit market, investors should consider the most common rationales of whether to trade and not to trade.

Popular Reasons for Trading

There are dozens of rationales to execute secondary trades when pursuing portfolio optimization. Several of the most popular are discussed below.

Yield-Spread Pickup Trades

Yield-spread pickup trades represent the most common secondary transactions across all sectors of the global credit market. Historically, at least half of all secondary swaps reflect investor intentions to add additional yield within the overall duration and credit-quality constraints of a portfolio. If five-year Aa2/AA+ GE Capital paper trades at 114 basis points, 79 basis points less than three-year A2/A Bank of America, some investors will determine the rating and maturity differential irrelevant and purchase the Bank of America bond and sell the GE Capital (an issue swap) for a spread gain of 79 basis points per annum.

This “yield-first psychology” reflects the institutional yield need of long-term asset/liability managers (plan sponsors and insurers). Despite the passage of more than three decades, this investor bias toward yield maximization also may be a methodological relic left over from the era prior to the introduction and market acceptance of total-return indexes in the early 1970s.

Credit-Upside Trades

Credit-upside trades take place when the debt asset manager expects an upgrade in an issuer’s credit quality that is not already reflected in the current market yield spread. In the illustration of the GE Capital and Bank of America trade described above, some investors may swap based on their view of potential credit-quality improvement for Bank of America. Obviously, such trades rely on the credit analysis skills of the investment management team. Moreover, the manager must be able to identify a potential upgrade before the market; otherwise, the spread for the upgrade candidate will already exhibit the benefits of a credit upgrade.

Credit-upside trades are particularly popular in the crossover sector—securities with ratings between Ba2/BB and Baa3/BBB– by two major rating agencies. In this case, the portfolio manager is expressing an expectation that an issue of the highest speculative grade rating (Ba1/BB+) has sufficiently positive credit fundamentals to be upgraded to investment-grade (i.e., Baa3/BBB–). If this upgrade occurs, then not only would the issue’s spread narrow based on the credit improvement (with an accompanying increase in total return, all else equal), but the issue also would benefit from improved liquidity because managers prohibited from buying high-yield bonds could then purchase that issue. Further, the manager would expect an improvement in the portfolio’s overall risk profile.

Credit-Defense Trades

Credit-defense trades become more popular as geopolitical and economic uncertainty increase. Secular sector changes also often generate uncertainties and induce defensive positioning by investors. In anticipating greater competition in the mid 1990s, some investors reduced their portfolio exposures to sectors such as electric utilities and telecommunications. As some Asian currencies and equities swooned in mid 1997, many portfolio managers cut their allocation to the Asian debt market. Unfortunately, because of yield-maximization needs and a general reluctance to realize losses by some institutions (i.e., insurers), many investors reacted more slowly to credit-defensive positioning. However, after a record number of “fallen angels” in 2002, which included such major credit bellwether issuers as WorldCom, and the misfortunes of many financial institutions in 2007–2008, investors became quicker to jettison potential problem credits from their portfolios. Ironically, once a credit is downgraded by the rating agencies, internal portfolio guidelines often dictate security liquidation immediately after the loss of single-A or investment-grade status. This is usually the worst possible time to sell a security and maximizes losses incurred by the portfolio.

New-Issue Swaps

New-issue swaps contribute to secondary turnover. Because of perceived superior liquidity, many portfolio managers prefer to rotate their portfolios gradually into more current and usually larger sized on-the-run issues. This disposition, reinforced by the usually superior market behavior of newer issues in the U.S. Treasury market (i.e., the on-the-run issues), has become a self-fulfilling prophecy for many credit issues. In addition, some managers use new-issue swaps to add exposure to a new issuer or a new structure.

Sector-Rotation Trades

Sector-rotation trades, within credit and among fixed income asset classes, have become more popular since the early 1990s. In this strategy, the manager shifts the portfolio from a sector or industry that is expected to underperform to a sector or industry that is believed will outperform on a total-return basis. With the general development of enhanced liquidity and lower trading transaction costs during non-crisis periods across the global bond market in the early twenty-first century, sector-rotation trades have become more prevalent in the credit asset class.

Such intra-asset class trading has played a major role in differentiating performance among credit portfolio managers. For example, as soon as the Fed launched its preemptive strike against inflation in February 1994, some investors correctly exchanged fixed-rate corporates for floating-rate corporates. In 1995, the specter of U.S. economic weakness prompted some investors in high-yield corporates to rotate from consumer-cyclical sectors such as autos and retailing into consumer noncyclical sectors such as food, beverage, and health care. Anticipating slower U.S. economic growth in 1998 induced a defensive tilt by some portfolio managers away from other cyclical groups such as paper and energy. The resurrection of Asian and European economic growth in 1999 stimulated increased portfolio interest in cyclicals, financial institutions, and energy debt. Credit portfolio managers could have avoided a great deal of portfolio performance disappointment in 2002 by underweighting utilities and many industrial sectors and again in 2007 by underweighting financial firms.

Curve-Adjustment Trades

Yield-curve-adjustment trades, or simply, curve-adjustment trades, are taken to reposition a portfolio’s duration. For most credit investors, their portfolio duration is typically within a range from 20% below to 20% above the duration of the benchmark index. If credit investors could have predicted U.S., euro, and yen yield-curve movements perfectly in 2002 and in 2008, then they would have increased their credit portfolio duration at the beginning of 2002 and 2008 in anticipation of a decrease in interest rates. Although most fixed income investors prefer to alter the duration of their aggregate portfolios in the more-liquid Treasury market, strategic portfolio duration tilts also can be implemented in the credit market.

Such trades also are executed in anticipation of changes in the credit term structure or credit curve. For example, if a portfolio manager believes that credit-spreads will tighten (either overall or in a particular sector), with rates in general remaining relatively stable, then she might shift the portfolio’s exposure to longer-spread-duration credit issues in her preferred sectors.

Structure Trades

Within the set of investment-grade, fixed-rate credit securities, structure trades have become rarer as the global credit markets have become more homogeneous (intermediate bullets). Structure trades are swaps into structures (e.g., callable structures, bullet structures, and putable structures) that are expected to have better performance given expected movements in volatility and the shape of the yield-curve. Structure trades also encompass swaps from less stringent issue indentures into tighter indentures that may afford bond investors greater protection should the issuer encounter financial difficulties. By expanding the choice set from fixed credit-only debt to include floating rate, preferred, preference, and even convertible securities, structural trades remain very common. The following examples illustrate the portfolio benefits of sound structure trades.

• The plunge in U.S. interest rates and escalation of yield-curve volatility during the second half of 1998 restrained the performance of callable structures compared with bullet structures.

• The upward rebound in U.S. interest rates and the fall in interest-rate volatility during 1999 contributed to the relative outperformance of callable structures versus bullet structures.

• A swap from floating-rate debt to fixed-rate debt would have aided credit portfolios at the beginning of global central bank easing cycles in 2001 and 2007.

• A rotation from conventional investment-grade credit debt into riskier subordinated structures, preferred stock, and convertibles in March 2009 would have markedly boosted portfolio performance.

Basis Trades

Traders and asset managers regularly prowl the global credit asset class for discrepancies, even slight, among bond, nominal spread, OAS, interest-rate swap spread, and credit default swap spread values for the same and similar issues. These discrepancies may give rise to advantageous basis trade swaps.

Cash-Flow Reinvestment

Cash-flow reinvestment forces investors into the secondary market on a near-weekly basis. In some years, the sum of all coupon, maturity, and partial redemptions (via tenders, sinking funds, and other issuer prepayments) equals approximately 100% of all new gross issuance across the dollar bond market. Before the allocation of any net new investment in the bond market, investors have sufficient cash-flow reinvestment to absorb nearly all new bond supply. Some portfolio cash inflows occur during interludes in the primary market, or the composition of recent primary supply may not be compatible with portfolio objectives. In these periods, credit portfolio managers must shop the secondary market for investment opportunities to remain fully invested or temporarily replicate the corporate index by using CDS and financial futures. Portfolio managers who incorporate analysis of cash-flow reinvestment into their valuation of the credit market can position their portfolios to take advantage of this cash-flow reinvestment effect on spreads.

Trading Constraints

Portfolio managers also should review their main rationales for not trading. Some of the best investment decisions are not to trade. Conversely, some of the worst investment decisions emanate from stale views based on dated and anachronistic constraints (e.g., avoid investing in bonds rated below Aa/AA). The best portfolio managers retain very open minds, constantly self-critiquing both their successful and unsuccessful methodologies.

Portfolio Constraints

Collectively, portfolio constraints are the single biggest contributor to the persistence of market inefficiency across the global credit market. Here are some examples:

• Because many asset managers are limited to holding securities with investment-grade ratings, they are forced to sell immediately the debt of issuers who are downgraded to speculative ratings (Ba1/BB+ and below). In turn, this selling at the time of downgrade provides an opportunity for investors with more flexible constraints to buy such newly downgraded securities at a temporary discount (provided, of course, that the issuer’s creditworthiness stabilizes after downgrade).

• Some Sovereign Wealth Funds and U.S. state employee pension funds cannot purchase credit securities with ratings below A3/A– owing to administrative and legislative guidelines.

• Some U.S. pension funds also have limitations on their ownership of MTNs and non-U.S. corporate issues.

• Regulators have limited U.S. insurance companies’ investment in high-yield corporates.

• Some European investors are restricted to issues rated at least single-A and sometimes Aa3/AA− and above, created originally in annual-pay Eurobond form.

• Some investors are confined to their local currency market—yen, sterling, euro, U.S. dollar. Often the same issuer, such as GE Capital, will trade at different spreads in diverse local markets.

• Globally, many commercial banks must operate exclusively in the floating-rate realm; all fixed-rate securities, unless converted into floating-rate cash-flows via an interest-rate swap, are prohibited.

“Story” Disagreement

“Story” disagreement can work to the advantage or disadvantage of a portfolio manager. Traders, salespersons, sell-side analysts and strategists, and buy-side credit researchers have dozens of potential trade rationales that supposedly will enhance portfolio performance. The proponents of a secondary trade may make a persuasive argument, but the portfolio manager may be unwilling to accept the “shortfall risk” if the investment recommendation does not provide its expected return. For example, in early 1998, analysts and investors alike were divided equally on short-term prospects for better valuations of Asian sovereign debt. After a very disappointing 1997 for Asian debt performance, Asia enthusiasts had little chance to persuade pessimists to buy Asian debt at the beginning of 1998. Technically, such lack of consensus in the credit market signals an investment with great outperformance potential. Indeed, most Asian debt issues recorded exceptional outperformance over the full course of 1998 and 1999. After a difficult 2002, the same “rebound effect” was observed in electric utilities during 2003. Of course, “story” disagreement also can work in the other direction. For example, Enron and Lehman Brothers were long viewed as very solid credits before their sudden bankruptcies in late 2001 and September 2008, respectively. An asset manager wedded to this long-term view might have been reluctant to act on the emergence of less favorable information about Enron in the summer of 2001 and about Lehman during early 2008.

Buy-and-Hold

Although many long-term asset/liability managers claim to have become more total-return-focused in the 1990s, accounting constraints (cannot sell positions at a loss compared with book cost or take too extravagant a gain compared with book cost) often limit the ability of these investors to trade. Effectively, these investors (mainly insurers) remain traditional-buy-and-hold investors. Some active bond managers have converged to quasi-buy-and-hold investment programs at the behest of consultants to curb portfolio turnover. In the aftermath of the “Asian Contagion” in 1997–1998 and the Great Recession–induced financial panic of September 2008–March 2009, this disposition toward lower trading turnover was reinforced by the temporary reduction in market liquidity provided by more wary bond dealers. As shown in 2000–2002 and over 2007–2010, however, a buy-and-hold strategy can gravely damage the performance of a credit portfolio. At the first signs of a system-wide credit event (systemic risk) or credit trouble for an issuer (idiosyncratic risk), many credit portfolios would have improved returns by reducing their exposure to the overall credit asset class or to a deteriorating credit. And in the case of systemic-risk events, subsequent relative portfolio performance would have been greatly aided by adding solid issues temporarily caught up in the credit market’s transitory general dislocation.

Seasonality

Lower-quality credits (Baa/BBB’s) tend to be more susceptible to underperformance during August–October as dealers and investors become more defensive with the approach of year-end. Conversely, lower-quality credits frequently outperform during the first quarter on optimistic hopes for strong economic, industry, issuer fundamentals for the full calendar year. Secondary trading slows at month end, more so at quarter end, and the most at the conclusion of calendar years. Dealers often prefer to reduce their balance sheets at fiscal year-end (December 31 or March 31 [Japan]). Also, portfolio managers take time to mark their portfolios, prepare reports for their clients, and chart strategy for the next investment period. During these intervals, even the most compelling secondary offerings can languish.

SPREAD ANALYSIS

By custom, some segments of the high-yield and emerging (EMG) debt markets still prefer to measure value by bond price or bond yield rather than spread. But for the rest of the global credit market, nominal spread (the yield difference between corporate and government bonds of similar maturities) has been the basic unit of both price and relative-value analysis for more than two centuries.

Alternative Spread Measures

Many U.S. practitioners prefer to value investment-grade credit securities in terms of option-adjusted spreads (OAS) so that they can be compared more easily to the volatility (“vol”) sectors (mortgage-backed securities and U.S. agencies).2 But given the rapid reduction of credit structures with embedded options since 1990 (see structural discussion above), the use of OAS in primary and secondary pricing has diminished within the investment-grade credit asset class. Moreover, the standard one-factor binomial models3 do not account for credit-spread volatility. And given the exclusion of default risk in OAS option-valuation models, OAS valuation has seen only limited extension into the higher-risk markets of the quasi-equity, high-yield corporate, and EMG-debt asset classes.

Starting in Europe during the early 1990s and gaining momentum during the late 1990s, interest-rate swap spreads emerged as the common denominator to measure relative value across fixed- and floating-rate note credit structures. The U.S. investment-grade and high-yield markets eventually may rely more heavily on such swap spreads to be consistent with Europe and Asia. But with the exponential growth of the credit-default swap (CDS) market since approximately 2000, CDS spreads have emerged as the standard gauge of relative pricing for large credit issuers.

Other U.S. credit-spread calculations have been proposed from time-to-time, most notably using the U.S. agency benchmark curve. This short-lived suggestion in 2000 sprang from the fallacious assumption of a persistent U.S. budgetary surplus and significant liquidation of outstanding U.S. Treasury securities during the first decade of the twenty-first century. As again demonstrated by 2002, history teaches that long-term fiscal assumptions unfortunately often prove to be faulty. Although some practitioners may choose to derive credit-agency spreads for analytical purposes, this practice had little chance of becoming standard market convention.

Credit-default swap spreads have emerged as the latest valuation tool during the great stresses in the credit markets of 2000–2002 and 2007–2010. Most likely, credit-default swap (CDS) spreads will be used as a companion valuation reference to nominal spreads, OAS, and interest rate-swap spreads. The market, therefore, has an ability to price any credit instrument using multiple spread references. Historically, the basic spread measures used the Treasury yield-curve or Treasury spot-rate curve as the underlying benchmark. Given the potential that swap spreads will become the new benchmark, these same measures can be performed relative to swaps rather than relative to U.S. Treasuries.

Here is an illustration of how a bond manager can use the interest-rate swap-spread framework. Suppose that a hypothetical Ford Motor 4½s of 2016 traded at a bid price (i.e., the price at which a dealer is willing to buy the issue) of 113 basis point over the five-year U.S. Treasury yield of 2.75%. This equates to a yield-to-maturity of 3.88% (2.75% + 113 basis points). On that date, five-year swap spreads were 83 basis points (to the five-year U.S. Treasury). Recall that swaps are quoted where the fixed-rate payer pays the yield on a Treasury with a maturity equal to the initial term of the swap plus the swap spread. The fixed-rate payer receives LIBOR flat; that is, no increment over LIBOR. Thus, if the bond manager invests in the Ford Motor issue and simultaneously enters into this five-year swap, the following would result:

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Thus, a bond manager could exchange this Ford Motor bond’s fixed coupon flow for LIBOR + 30 basis points. A total-return manager would want to take advantage of this swap by paying fixed and receiving floating if he expects interest rates to increase in the future.

The swaps framework allows managers (as well as issuers) to more easily compare securities across fixed-rate and floating-rate markets. The extension of the swap-spread framework may be less relevant for speculative-grade securities, in which default risk becomes more important. In contrast to professional money managers, individual investors are not comfortable using bond valuation couched in terms of swap spreads. The traditional nominal spread framework is well understood by individual investors, has the advantages of long-term market convention, and works well across the entire credit-quality spectrum from Aaas to Bs. However, this nominal spread framework does not work very well for investors and issuers when comparing the relative attractiveness between the fixed-rate and floating-rate markets.

Spread Tools

Investors also should understand how best to evaluate spread levels in their decision making. Spread valuation includes mean-reversion analysis, quality-spread analysis, and percent yield spread analysis.

Mean-Reversion Analysis

The most common technique for analyzing spreads among individual securities and across industry sectors is mean-reversion analysis. The mean is the average value of some variable over a defined interval (usually one economic cycle for the credit market). The term mean reversion refers to the tendency for some variable’s value to revert (i.e., move toward) its average value. Mean-reversion analysis is a form of relative-value analysis based on the assumption that the spread between two sectors or two issuers will revert back to its historical average. This would lead investors to buy a sector or issuer identified as “cheap” because historically the spread has been tighter and will eventually revert back to that tighter spread. Also, this would lead investors to sell a sector or issuer identified as “rich” because the spread has been wider and is expected to widen in the future.

Mean-reversion analysis involves the use of statistical analysis to assess whether the current deviation from the mean spread is significant. For example, suppose that the mean spread for an issuer is 80 basis points over the past six months and the standard deviation is 12 basis points. Suppose that the current spread of the issuer is 98 basis points. The spread is 18 basis points over the mean spread or, equivalently, 1.5 standard deviations above the mean spread. The manager can use that information to determine whether or not the spread deviation is sufficient to purchase the issue. The same type of analysis can be used to rank a group of issuers in a sector.

Mean-reversion analysis can be instructive as well as misleading. The mean is highly dependent on the interval selected. There is no market consensus on the appropriate interval, and “persistence” frequents the credit market, meaning that cheap securities, mainly a function of credit uncertainty, often tend to become cheaper. Rich securities, usually high-quality issues, tend to remain rich.

Quality-Spread Analysis

Quality-spread analysis examines the spread differentials between low- and high-quality credits. For example, portfolio managers would be well advised to consider the “credit upgrade trade” when quality spreads collapse to cyclical troughs. The incremental yield advantage of lower-quality products may not compensate investors for lower-quality spread expansion under deteriorating economic conditions. Alternatively, credit portfolio managers have long profited from overweighting lower-quality debt at the outset of an upward turn in the economic cycle.

Percent Yield-Spread Analysis

Dating from the early twentieth century, percent yield-spread analysis (the ratio of credit yields to government yields for similar-duration securities) is another popular technical tool used by some investors. This methodology has serious drawbacks that undermine its usefulness. Percent yield spread is more a derivative than an explanatory or predictive variable. The usual expansion of credit percent yield spreads during low-rate periods such as 1997, 1998, 2002, and 2008–2010 overstates the risk as well as the comparative attractiveness of credit debt. And the typical contraction of credit percent yield-spreads during upward shifts of the benchmark yield-curve does not necessarily signal an imminent bout of underperformance for the credit asset class. Effectively, the absolute level of the underlying benchmark yield is merely a single factor among many factors (demand, supply, profitability, defaults, etc.) that determine the relative value of the credit asset class. These other factors can offset or reinforce any insights derived from percent yield-spread analysis.

STRUCTURAL ANALYSIS

As explained earlier in this chapter, there are bullet, callable, putable, and sinking fund structures. Structural analysis is simply analyzing the performance of the different structures discussed throughout this chapter. While evaluating bond structures was extremely important in the 1980s, it has become less influential in the credit bond market since the mid 1990s for several reasons. First, the European credit bond market almost exclusively features intermediate bullets. Second, as can be seen in Exhibit 51–6, the U.S. credit and the global bond markets have moved to embrace this structurally homogeneous European bullet standard. Plenty of structural diversity still resides within the U.S. high-yield and EMG debt markets, but portfolio decisions in these speculative-grade sectors understandably hinge more on pure credit differentiation than the structural diversity of the issue-choice set.

EXHIBIT 51–6
Changing Composition of the U.S. Investment-Grade Credit Markets*

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Still, structural analysis can enhance risk-adjusted returns of credit portfolios. Leaving credit aside, issue-structure analysis and structural allocation decisions usually hinge on yield-curve and volatility forecasts, as well as interpretation of option-valuation model outputs (see the discussion below). This is also a key input in making relative-value decisions among structured credit issues, mortgage-backed securities, and asset-backed securities. In the short run and assuming no change in the perceived creditworthiness of the issuer, yield-curve and volatility movements largely will influence structural performance. Investors also should take into account long-run market dynamics that affect the composition of the market and, in turn, credit index benchmarks.

Specifically, callable structures have become rarer in the U.S. investment-grade credit bond market with the exception of the 2000 inversion. This is due to an almost continuously positively sloped U.S. term structure since 1990 and the yield-curve’s intermittent declines to approximately multi-decade lows in 1993, 1997, 1998, 2002, and 2008–2010. As a result, the composition of the public U.S. corporate bond market converged toward the intermediate-bullet Eurobond and euro-denominated bond market. To see this, we need only look at the structure composition of Lehman’s U.S. Investment-Grade Credit Bond Index. Bullets increased from 24% of this index at the start of 1990 to 96.0% (principal-value basis) by the end of 2010. Over this interval, callables declined at a remarkable rate from 72% to just a 3.5% index share. Sinking-fund structures, once the structural mainstay of natural-gas pipelines and many industrial sectors, are on the “structural endangered species list” with a drop from 32% of the public bond market in 1990 to only 0.1% in 2010. Despite several brief flurries of origination in the mid 1990s and the late 1990s introduction of callable/putable structures, putable structure market share fell from 5% in 1990 to 0.4% by 2010. Pure corporate zeros are in danger of extinction with a fall from 4% market share in 1990 to negligible by 2010.

Bullets

Here is a review of how different types of investors are using bullet structures with different maturities.

Front-end bullets (i.e., bullet structures with one- to five-year maturities) have great appeal for investors who pursue a “barbell strategy.” There are “barbellers” who use credit securities at the front or short end of the curve and Treasuries at the long end of the yield-curve. There are non-U.S. institutions who convert short bullets into floating-rate products by using interest-rate swaps. The transactions are referred to as “asset swaps,” and the investors who employ this transaction are referred to as “asset swappers.”

Intermediate credit bullets (5- to 12-year maturities), especially the 10-year maturity sector, have become the most popular segment of the U.S. and European investment-grade and high-yield credit markets. Fifteen-year maturities, benchmarked off the 10-year bellwether Treasury, are comparatively rare and have been favored by banks that occasionally use them for certain types of swaps. Because new 15-year structures take five years to descend along a positively sloped yield-curve to their underlying 10-year bellwether, 15-year maturities hold less appeal for many investors in search of return through price appreciation emanating from benchmark rolldown. In contrast, rare 20-year structures have been favored by many investors. Spreads for these structures are benched off the 30-year Treasury. With a positively sloped yield-curve, the 20-year structure provides higher yield than a 10- or 15-year security and less vulnerability (lower duration) than a 30-year security.

The 30-year maturity is the most popular form of long-dated security in the global credit market. In 1992, 1993, late 1995, 1997, and 2010, there was a minor rush to issue 50-year (half-centuries) and 100-year (centuries) securities in the U.S. credit bond market. These longer-dated securities provide investors with extra positive convexity for only a modest increase in effective (or modified-adjusted) duration. In the wake of the “Asian contagion” and especially the “great spread-sector crash” of August 1998 as well as the Great Recession of 2007–2009, the cyclic increases in risk aversion and liquidity premiums greatly reduced both issuer and investor interest in these ultralong maturities.

Callables

Typically after a 5- or 10-year wait (longer for some rare issues), credit structures are callable at the option of the issuer at any time. Call prices usually are set at a premium above par (par + the initial coupon) and decline linearly on an annual basis to par by 5 to 10 years prior to final scheduled maturity. The ability to refinance debt in a potentially lower-interest-rate environment is extremely valuable to issuers. Conversely, the risk of earlier-than-expected retirement of an above-current market coupon is bothersome to investors.

In issuing callables, issuers pay investors an annual spread premium (about 20–40 basis points for high-quality issuers) for being long (from an issuer’s perspective) the call option. Like all security valuations, this call premium varies through time with capital market conditions. Given the higher chance of exercise, this call option becomes much more expensive during low-rate and high-volatility periods. Since 1990, this call premium has ranged from approximately 15 to 50 basis points for investment-grade issuers. Callables significantly underperform bullets when interest rates decline because of their negative convexity. When the bond market rallies, callable structures do not fully participate given the upper boundary imposed by call prices. Conversely, callable structures outperform bullets in bear bond markets as the probability of early call diminishes.

Sinking Funds

A sinking-fund structure allows an issuer to execute a series of partial calls (annually or semiannually) prior to maturity. Issuers also usually have an option to retire an additional portion of the issue on the sinking-fund date, typically ranging from one to two times the mandatory sinking-fund obligation. Historically, especially during the early 1980s, total-return investors favored the collection of sinking-fund structures at subpar prices. These discounted sinking funds retained price upside during interest-rate rallies (provided the indicated bond price remained below par), and given the issuers’ requirement to retire at least annually some portion of the issue at par, the price of these sinking-fund structures did not fall as much compared with callables and bullets when interest rates rose. It should be noted that astute issuers with strong liability management skills sometimes can satisfy such annual sinking-fund obligations in whole or in part through prior open-market purchases at prices below par. Nonetheless, this annual sinking-fund purchase obligation by issuers limits bond price depreciation during periods of rising rates.

Putables

Conventional put structures are simpler than callables. Yet, in trading circles, put bond valuations often are the subject of debate. American-option callables grant issuers the right to call an issue at any time at the designated call price after expiration of the noncallable or nonredemption period. Put bonds typically provide investors with a onetime, one-date put option (European option) to demand full repayment at par. Less frequently, put bonds include a second or third put option date. A very limited number of put issues afford investors the privilege to put such structures back to the issuers at par in the case of rating downgrades (typically to below investment-grade status).

Thanks to falling interest rates, issuers shied away from new put structures as the 1990s progressed. Rather than incur the risk of refunding the put bond in 5 or 10 years at a higher cost, many issuers would prefer to pay an extra 10 to 20 basis points in order to issue a longer-term liability.

Put structures provide investors with a partial defense against sharp increases in interest rates. Assuming that the issuer still has the capability to meet its sudden obligation, put structures triggered by a credit event enable investors to escape from a deteriorating credit. Perhaps because of its comparative scarcity, the performance and valuation of put structures have been a challenge for many portfolio managers. Unlike callable structures, put prices have not conformed to expectations formed in a general volatility–valuation framework. Specifically, the implied yield volatility of an option can be computed from the option’s price and a valuation model. In the case of a putable bond, the implied volatility can be obtained using a valuation model such as the binomial model. The implied volatility should be the same for both puts and calls, all factors constant. Yet, for putable structures, implied volatility has ranged 4% to 9% since 1990, well below the 10% to 20% volatility range associated with callable structures for the same time period. This divergence in implied volatility between callables (high) and putables (low) suggests that asset managers, often driven by a desire to boost portfolio yield, underpay issuers for the right to put a debt security back to the issuer under specified circumstances. In other words, the typical put bond should trade at a lower yield in the market than is commonly the case.

Unless put origination increases sharply, allowing for greater liquidity and the creation of more standardized trading conventions for this rarer structural issue, this asymmetry in implied volatility between putable and corporate structures will persist. Meanwhile, this structure should be favored as an outperformance vehicle only by investors with a decidedly bearish outlook for interest rates.

CREDIT-CURVE ANALYSIS

The rapid growth of credit derivatives since the mid 1990s has inspired a ground-swell of academic and practitioner interest in the development of more rigorous techniques to analyze the term structure (1–100 years) and credit structure (Aaa/AAA through B2/Bs) of credit-spread curves (higher-risk, higher-yield securities trade on a price rather than a spread basis).

Credit curves, both term structure and credit structure, are almost always positively sloped. In an effort to moderate portfolio risk, many portfolio managers take credit risk in short and intermediate maturities and substitute less-risky government securities in long-duration portfolio buckets. This strategy is called a credit barbell strategy. Accordingly, the application of this strategy diminishes demand for longer-dated credit risk debt instruments by many total-return, mutual fund, and bank portfolio bond managers. Fortunately for credit issuers who desire to issue long maturities, insurers, and pension plan sponsors often meet long-term liability needs through the purchase of credit debt with maturities that range beyond 20 years.

Default risk increases nonlinearly as creditworthiness declines. The absolute risk of issuer default in any one year remains quite low through the investment-grade rating categories (Aaa/AAA to Baa3/BBB−). But investors constrained to high-quality investments often treat downgrades like quasi-defaults. In some cases, such as a downgrade from single-A to the Baa/BBB category, investors may be forced to sell securities under rigid portfolio guidelines. In turn, investors justifiably demand a spread premium for the increased likelihood of potential credit difficulty as rating quality descends through the investment-grade categories.

Credit-spreads increase sharply in the high-yield rating categories (Ba1/BB+ through D). Default, especially for weak single-Bs and CCCs, becomes a major possibility. The credit market naturally assigns higher and higher risk premia (spreads) as credit and rating risk escalate. Exhibit 51–7 shows the credit curve for two credit sectors (Baa and single-A industrials) and also illustrates that a higher spread is required as maturity lengthens.

EXHIBIT 51–7
Illustration of Two Typical U.S. Investment-Grade Credit Curves

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In particular, the investment-grade credit market has a fascination with the slope of issuer credit curves between 10- and 30-year maturities. Like the underlying Treasury benchmark curve, credit-spread curves change shape over the course of economic cycles. Typically, spread curves steepen when the bond market becomes more wary of interest-rate and general credit risk. Spread curves also have displayed a minor propensity to steepen when the underlying benchmark curve flattens or inverts. This loose spread-curve/yield-curve linkage reflects the diminished appetite for investors to assume both curve and credit risk at the long end of the yield-curve when higher total yields may be available in shor and intermediate-credit products.

CREDIT ANALYSIS

In the continuous quest to seek credit upgrades and contraction in issuer/issue spread resulting from possible upgrades and, more important, to avoid credit downgrades resulting in an increase in issuer/issue spread, superior credit analysis has been and will remain the most important determinant of credit bond portfolio relative performance. Credit screening tools tied to equity valuations, relative spread movements, and the Internet (information available tracking all related news on portfolio holdings) can provide helpful supplements to classic credit research and rating agency opinions. But self-characterized credit models, relying exclusively on variables such as interest-rate volatility and binomial processes imported from option-valuation techniques, are not especially helpful in ranking the expected credit performance of individual credits such as IBM, British Gas, Texas Utilities, Pohang Iron & Steel, Sumitomo, and Brazil.

Credit analysis is both nonglamorous and arduous for many top-down portfolio managers and strategists, who focus primarily on macro variables. Genuine credit analysis encompasses actually studying issuers’ financial statements and accounting techniques, interviewing issuers’ managements, evaluating industry issues, reading indentures and charters, and developing an awareness of (not necessarily concurrence with) the views of the rating agencies about various industries and issuers.

Unfortunately, the advantages of such analytical rigor may clash with the rapid expansion of the universe of issuers of credit bonds. There are approximately 5,000 different credit issuers scattered across the global bond market. With continued privatization of state enterprises, new entrants to the high-yield market, and expected long-term growth of the emerging-debt markets, the global roster of issuers could swell to 7,500 by 2025. The sorting of this expanding roster of global credit issues into outperformers, market performers, and under-performers demands establishing and maintaining a formidable credit-valuation function by asset managers.

ASSET ALLOCATION/SECTOR ROTATION

Sector rotation strategies have long played a key role in equity portfolio management. In the credit bond market, “macro” sector rotations among industrials, utilities, financial institutions, sovereigns, and supranationals also have a long history. During the last quarter of the twentieth century, there were major variations in investor sentiment toward these major credit sectors. Utilities endured market wariness about heavy supply and nuclear exposure in the early to mid 1980s. U.S. and European financial institutions coped with investor concern about asset quality in the late 1980s and early 1990s. Similar investor skittishness affected demand for Asian financial institution debt in the late 1990s. Industrials embodied severe “event risk” in the middle to late 1980s, recession vulnerability during 1990–1992, a return of event risk in the late 1990s amid a general boom in corporate mergers and acquisitions, and a devastating series of accounting and corporate governance blows during 2001–2002. Sovereigns were exposed to periodic market reservations about the implications of independence for Quebec, political risk for various countries (i.e., Russia), the effects of the “Asian contagion” during 1997–1998, and outright defaults such as Argentina (2001).

In contrast, “micro” sector rotation strategies have a briefer history in the credit market. A detailed risk/return breakdown (i.e., average return and standard deviation) of the main credit subsectors (i.e., banks, brokerage, energy, electrics, media, railroads, sovereigns, supranationals, and technology) was not available from credit index providers until 1993 in the United States and until 1999 in Europe. Beginning in the mid 1990s, these “micro” sector rotation strategies in the credit asset class have become much more influential as portfolio managers gain a greater understanding of the relationships among intracredit sectors from these statistics.

Exhibit 51–8 illustrates the main factors bearing on sector rotation and issuer selection strategies. For example, an actual or perceived change in rating agency philosophy toward a sector and a revision in profitability expectations for a particular industry represent just two of many factors that can influence relative sectoral performance.

EXHIBIT 51–8
Some Outperformance Methodologies

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Common tactics to hopefully enhance credit portfolio performance are also highlighted in Exhibit 51–8. In particular, seasonality again deserves comment. The annual rotation toward risk aversion in the bond market during the second half of most years contributes to a “fourth-quarter effect;” that is, there is underperformance of lower-rated credits, Bs in high-yield and Baas in investment-grade, compared with higher-rated credits. A fresh spurt of market optimism greets nearly every new year. Lower-rated credit outperforms higher-quality credit. This is referred to as the “first-quarter effect.” This pattern suggests a very simple and popular portfolio strategy: underweight low-quality credits and possibly even credit products altogether until the mid-third quarter of each year and then move to overweight lower-quality credits and all credit products in the fourth quarter of each year.

KEY POINTS

• As prescribed in capital market theory, investors should be rewarded for the assumption of incremental risk. Reality conforms to theory in the global credit market. Over the long run, credit products provide higher long-term returns than presumably risk-free government securities.

• Credit returns and risk are viewed as “asymmetric.” On occasion, asset managers may suffer large, transitory relative underperformance to Treasuries with the onset of systemic risk event (i.e., the Financial Panic in September 2008). And the price of individual credit securities may tumble from the par vicinity to zero in the event of default.

• Credit bond portfolio management requires more work and asset management firm infrastructure than other debt asset classes. There are thousands of credit choices, dozens of security forms, and multiple structures, and the size of the global credit asset class will accelerate during the twenty-first century thanks to the entrance of new emerging market based issuers.

• Global bond management philosophy has evolved rapidly over the past two decades. The arrival of the euro in 1999 curbed the use of currency strategies. Major portfolio-duration bets (more than 10% above or below the duration of an index benchmark) have become less common by asset managers because of frequent duration-timing disappointments. The use of CDS has expanded. New quantitative tools to assist in relative value rankings and asset allocation (i.e., risk budgeting) have proliferated. And particularly at large asset management firms, credit portfolios have truly become globalized.

• In conjunction with the demonstrably higher long-term returns of corporates and an ongoing migration from “government-only index benchmarks” to “government plus corporate and securitized index benchmarks,” this reduction in currency and curve timing has propelled investor interest in global credit portfolio optimization as a path to more consistent overall portfolio outperformance in an increasingly competitive asset management industry.

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