Credit Strategies

Top-Down Credit Strategies

Top-Down Credit Strategies is a method of credit strategy that emphasizes on a wider range of factors influencing the bond market, contrasting the detailed, issuer-specific bottom-up approach. This strategy is guided by macro factors such as economic growth, real rates and inflation, market volatility and risk appetite, recent credit spread changes, industry trends, geopolitical risk, and currency movements. These factors assist investors in identifying credit market sectors with appealing relative value characteristics.

With this approach, bond allocation is increased in more attractive sectors and reduced or possibly shorted in less favorable sectors. For instance, if a top-down investor anticipates credit spreads to narrow, they might prefer the relative value opportunity of high-yield bonds over investment-grade bonds.

Role of GDP Growth in Credit Cycle

GDP growth plays a pivotal role in the credit cycle. This is evident from the correlation between global speculative-grade default rates and the real GDP growth rate among G7 countries from 1962 to 2019. A sharp decline in GDP growth often corresponds with rising default rates. For instance, if a portfolio manager or analyst predicts a higher-than-expected real GDP growth, they might increase high-yield allocation, anticipating future defaults to remain below market expectations.

Credit Quality Assessment in Portfolio Management

Active top-down and bottom-up credit managers often utilize public ratings to categorize and rank the credit quality of bonds within a portfolio. This is a crucial aspect as default risk tends to increase more rapidly as ratings decline. Therefore, investors need to consider this while comparing investments across different credit ratings.

Weighted factors, such as those established by Moody’s, are used based on the likelihood of credit loss over a specific period versus ordinal factors across the credit spectrum. This allows managers to capture the effect of default risk more accurately. For instance, consider a portfolio where half of the bonds are from Apple Inc. (rated A1/A+) and the other half are from a startup company XYZ (rated Ba3/BB-). Using the weighted scale, the portfolio’s average credit quality score would be 918. This score is closer to Ba1/BB+, two levels below the average rating derived using an ordinal scale.

However, it’s important to note that public credit ratings have their limitations. They tend to lag the market’s pricing of credit risk, which is critical to an active investor. Furthermore, different ratings agencies like S&P and Moody’s capture different types of risks. S&P ratings focus on the Probability of Default (POD), while Moody’s focuses on expected losses. This could influence historical comparisons.

The credit rating time horizon is also a critical factor as ratings agencies issue both short-term and long-term ratings for specific issuers. This might require additional attention.

Due to these reasons, active managers often prefer to use credit spread measures such as Option Adjusted Spread (OAS) to measure average portfolio credit quality. To calculate a portfolio’s average OAS, each bond’s individual OAS is weighted by its market value.

Bond Portfolio Management and Option Adjusted Spread (OAS)

In bond portfolio management, bonds are often grouped by their Option Adjusted Spread (OAS) for analysis, which can be compared to public ratings. This spread-based approach allows for the measurement of changes in portfolio value due to spread changes, providing a more nuanced understanding than a rating-based approach.

For instance, consider a portfolio manager overseeing a diverse portfolio of corporate bonds. The manager might use the OAS to compare the relative value of bonds from different sectors or with different credit ratings.

Quantifying Portfolio Value Changes

$$ \begin{align*} \% \Delta PV_{\text{Spread}} & \approx -(\text{EffSpreadDur} \times \Delta \text{Spread}) \\ & + \left(\frac{1}{2} \times \text{EffSpreadCon} \times (\Delta \text{Spread})^2\right) \end{align*} $$

provides a framework to quantify portfolio value changes due to yield spread movements. This equation is particularly useful for investment-grade bonds with low credit spreads.

However, for bonds with higher default risk, adjustments to the equation might be necessary to accurately reflect how credit risk changes affect the overall portfolio value.

Empirical duration estimates using statistical models often diverge from analytical duration calculations over time and in different interest rate environments. For example, during a financial crisis, government bond yields might decrease while high-yield bond credit spreads increase due to an expectation of a greater likelihood and higher severity of financial distress.

Industry Sector Allocations

Industry sector allocations play a pivotal role in a top-down credit strategy. This strategy involves identifying sectors to over- or underweight based on an interest rate and overall market view. This view is formulated using macroeconomic variables and is crucial in determining whether specific sectors of the economy are likely to over- or underperform over the manager’s investment time horizon.

Quantitative methods such as regression analysis are frequently employed in making industry allocation decisions. For example, consider the financial crisis of 2008. The average spread of bonds within the banking sector and a specific rating category might be compared with the average spread of the bonds with the same rating but excluding the banking sector. This comparison can be done using financial ratios in comparing sector spreads and sector leverage.

Leverage and Credit Risk

Generally, higher leverage implies higher credit risk and thus wider spreads. Therefore, a portfolio manager could compare sectors on a spread-versus-leverage basis to identify relative value opportunities. Sector- and rating-specific spread curves are a useful tool in guiding decision making for top-down sector allocations.

A comparison of curves combined with an investor’s view could lead to credit portfolio positioning based on a view that a specific credit spread curve will flatten or steepen, or that two spread curves will converge or diverge. For instance, the divergence in industrial versus health care spreads for BBB rated US issuers during the COVID-19 pandemic in 2020. The flatter industrial credit spread curve reflects that sector’s relatively weak credit outlook versus health care over the period.

Factor-Based Credit Strategies

Factor-Based Credit Strategies are a modern approach to fixed-income portfolio construction, gaining traction among active credit investors. Unlike traditional top-down strategies that categorize investments by sector and public ratings, this method emphasizes style factors.

While factor investing has been a staple in equity markets, its application in fixed-income markets, especially in relation to systematic risk factors like size, value, and momentum, is a relatively new phenomenon.A 2018 study by Israel, Palhares, and Richardson established a framework for evaluating excess corporate bond returns based on various characteristics. The goal was to determine their significance in explaining fixed-income returns. The study found strong evidence of positive risk-adjusted returns to measures of carry, defensive, momentum, and value.

These returns offered diversification from common market risk sources such as equity or credit risk premia. They were similar in nature to those factors proven to be significant in equity markets, albeit with some modifications.However, neither traditional risk exposures nor mispricing fully explained the source of these excess returns.

Environmental, Social, and Governance Factors

Environmental, Social, and Governance (ESG) factors are becoming a significant part of active portfolio management. This is demonstrated by the increasing adoption of the Principles for Responsible Investment, a global initiative in collaboration with the United Nations to promote ESG factors in investing. This initiative has over 3,000 signatories worldwide, managing assets worth more than $100 trillion.

Active credit investors typically incorporate ESG factors into portfolio strategies in three primary ways:

• Implementing screens to exclude specific industries with less favorable ESG characteristics, such as firearms, tobacco, or coal, or to rule out specific companies or sovereign issuers with ESG-specific ratings below a certain threshold.
• Utilizing ESG ratings to target issuers within a given sector or rating category with relatively favorable ESG characteristics while matching a specific index risk and return.
• Targeting fixed-income investments that directly fund ESG-specific initiatives, such as green bonds.

ESG-specific Ratings

ESG-specific ratings for private and public issuers are a crucial element in the portfolio selection process. The wide range of quantitative and qualitative criteria used to measure ESG attributes and differences in methodology and weighting leads to greater dispersion in ESG versus credit ratings. However, ESG and credit ratings tend to be positively correlated for two reasons:

• Issuers with more financial resources are better able to meet more stringent ESG standards, while those with a greater likelihood of financial distress often face governance or other adverse risks.
• Major rating agencies now explicitly incorporate ESG risks into the traditional credit rating process. For instance, in 2019, Moody’s cited ESG risks as a material factor in one-third of its credit rating actions among private sector issuers.

Green Bonds

Green bonds are fixed-income instruments that directly fund ESG-related initiatives such as those related to environmental or climate benefits. This rapidly growing segment of the fixed-income market includes corporate, financial institution, and public issuers where bond proceeds are directed to projects that reduce air pollution, recycle.

Practice Questions

Question 1: A portfolio manager is considering a top-down credit strategy for their investment portfolio. They are focusing on a broad set of factors that affect the bond universe, including economic growth, real rates and inflation, changes in expected market volatility and risk appetite, recent credit spread changes, industry trends, geopolitical risk, and currency movements. If the portfolio manager expects credit spreads to narrow, which of the following actions might they take to capitalize on the relative value opportunity?

1. They might increase bond allocation to more attractive sectors and underweight or possibly short bond positions in less favorable sectors.
2. They might focus solely on issuer-specific factors and ignore broader macroeconomic trends.
3. They might avoid high-yield bonds and focus solely on investment-grade bonds.

Answer: Choice A is correct.

If a portfolio manager expects credit spreads to narrow, they might increase bond allocation to more attractive sectors and underweight or possibly short bond positions in less favorable sectors. This is because when credit spreads narrow, it means that the yield difference between bonds with different credit ratings is decreasing. This typically happens when the economy is improving and the risk of default is perceived to be lower. In such a scenario, bonds from sectors that are expected to benefit from the improving economic conditions would become more attractive, and their prices would rise. Therefore, by increasing allocation to these sectors, the portfolio manager can capitalize on the price appreciation. Conversely, sectors that are not expected to benefit as much from the improving conditions would be less attractive, and their bond prices might not rise as much or might even fall. Therefore, by underweighting or shorting these sectors, the portfolio manager can avoid potential losses or even profit from the price decline.

Choice B is incorrect. Focusing solely on issuer-specific factors and ignoring broader macroeconomic trends would not be a suitable strategy if the portfolio manager expects credit spreads to narrow. This is because credit spreads are influenced by a variety of factors, including macroeconomic conditions, market sentiment, and industry trends, in addition to issuer-specific factors. Therefore, ignoring these broader factors could result in missed opportunities or unexpected losses.

Choice C is incorrect. Avoiding high-yield bonds and focusing solely on investment-grade bonds would not necessarily be the best strategy if the portfolio manager expects credit spreads to narrow. This is because when credit spreads narrow, high-yield bonds (which have higher credit risk) often outperform investment-grade bonds (which have lower credit risk), as the perceived risk of default decreases. Therefore, by avoiding high-yield bonds, the portfolio manager could miss out on potential gains.

Question 2: A portfolio manager is using a top-down credit strategy and is considering the impact of GDP growth on the credit cycle. They have observed that sharp declines in GDP growth are often associated with rising default rates. If the portfolio manager has an above-consensus real GDP growth forecast, which of the following actions might they take in their investment decision-making process?

1. They might decrease their high-yield allocation, expecting future defaults to rise above market expectations.
2. They might increase their high-yield allocation, expecting future defaults to remain below market expectations.
3. They might ignore the GDP growth forecast and focus solely on issuer-specific factors.

Answer: Choice B is correct.

If a portfolio manager has an above-consensus real GDP growth forecast, they might increase their high-yield allocation, expecting future defaults to remain below market expectations. This is because a strong GDP growth is generally associated with a healthy economy, which in turn is likely to result in lower default rates. High-yield bonds, also known as junk bonds, are typically issued by companies with a higher risk of default. However, in a strong economy, these companies are more likely to be able to meet their debt obligations, reducing the risk of default. Therefore, if a portfolio manager expects the economy to perform better than what is currently priced into the market, they might see an opportunity to earn higher returns by increasing their allocation to high-yield bonds. This strategy is consistent with a top-down credit strategy, which starts with an analysis of macroeconomic factors, such as GDP growth, before considering individual securities.

Choice A is incorrect. If the portfolio manager has an above-consensus real GDP growth forecast, they would not decrease their high-yield allocation expecting future defaults to rise above market expectations. This is because a strong GDP growth is generally associated with a healthy economy, which in turn is likely to result in lower, not higher, default rates.

Choice C is incorrect. While issuer-specific factors are important in credit analysis, they are not the only factors to consider. A top-down credit strategy starts with an analysis of macroeconomic factors, such as GDP growth. Therefore, a portfolio manager using this strategy would not ignore the GDP growth forecast.

Glossary

• \(\text{Probability of Default (POD)}\): The likelihood that a loan will not be repaid and go into default.
• \(\text{Option Adjusted Spread (OAS)}\): The measurement of the spread of a fixed-income security rate and the risk-free rate of return, adjusted to take into account an embedded option.
• \(\text{EffSpreadDur}\): The effective spread duration, a measure of a bond’s price sensitivity to changes in its yield spread.
• \(\Delta \text{Spread}\): The change in spread.
• \(\text{EffSpreadCon}\): The effective spread convexity, a measure of the rate of change of a bond’s spread duration.
• \(\text{Spread}\): The difference between the yields of two bonds with different credit qualities.

Portfolio Management Pathway Volume 2: Learning Module 6: Fixed-Income Active Management: Credit Strategies.

LOS 6(c): Discuss top-down approaches to credit strategies