Overview: Systematic Active Fixed Income Signals

Reliable raw data can provide important insights into where fixed income market valuations may be headed. The breadth of data contemplated is broad, crossing fixed income and equities, fundamentals and technicals, and market pricing. Using the data, fixed income market participants can pinpoint opportunities for upside potential and downside protection.

In this blog post, we provide information about three data signals that are used to drive alpha in our first systematic active strategies, which we are offering in US Dollar Investment Grade Credit. Our systematic strategies will be implemented in collaboration with Barclays Quantitative Portfolio Strategy (QPS), a provider of industry-leading fixed income data and analytics.

In addition, we detail how combining these three data signals can lead to diversification benefits for systematic active fixed income investors.

Unpacking the Signals

The three signals that form the core of our systematic approach are excess spread over peers (ESP), equity momentum in credit (EMC), and equity short interest (ESI). These signals represent the relative value, momentum, and sentiment factors, respectively, in the corporate bond market.

Excess Spread to Peers

ESP is a relative value signal designed to identify mispriced bonds. Relative value has long been used by credit investors to identify attractive opportunities across corporate issuers in a given sector. ESP, based on quantitative data, has an advantage relative to a fundamental analyst-driven approach as it can cover a wide range of securities and analyze a large number of corporate bonds simultaneously.

To derive the ESP signal, we have a set of rules that attributes observed bond spreads to issuers’ fundamentals and peer characteristics, including rating, sector, and maturity. The part of the spread that is not attributed to those characteristics forms the basis for ESP. In a given sector and rating group of the US investment grade universe, the algorithm identifies bonds that trade cheap to their peers, after controlling for risk characteristics and fundamentals (Figure 1).

Source: State Street Global Advisors, as of March 31, 2023. The information contained above is for illustrative purposes only.

ESP tends to be a relatively stable signal as corporate bond mispricings market correct slowly. A higher signal value means that a bond trades at a discount to its peers after accounting for fundamentals. Such mispricing tends to correct over time, leading to outperformance of high-ESP names over their peers. Historical data shows that diversified portfolios of undervalued bonds with high ESP scores have persistently outperformed otherwise similar low-ESP portfolios (Figure 2).

Equity Momentum in Credit

EMC is a cross-asset signal (i.e., it is drawn from both equity and credit data). EMC allows investors to differentiate credit issuers by their past equity performance. It helps to identify issuers that may exhibit improving prospects, reflected by positive equity returns, or declining prospects, demonstrated by poor equity returns, compared to their peers (Figure 3).

Source: State Street Global Advisors, as of March 31, 2023. The information contained above is for illustrative purposes only.

Empirical results indicate that historically there has been a strong positive relationship between an issuer’s past equity returns and the subsequent returns of its corporate bonds. Our analysis showed that bonds of companies with strong relative equity performance usually outperformed their peers with weak or negative equity returns (Figure 4).

The EMC signal reflects information spillover from equities to corporate bonds, which tend to react with a lag to changes in a company’s fundamentals. There are several reasons why equity momentum leads performance in corporate bonds:

• Different reaction times. Equity markets tend to react faster than credit to company news.
• Divergent motivations of equity and credit investors. Credit and equity markets remain segregated, with credit investors focused on the strength of the balance sheet and equity investors focused on profitability and growth opportunity. However, over time, if a company’s balance sheet is in bad shape, equity investors will also react and unload the stock.
• Varying behavioral biases. Responses to transaction costs could be stronger in credit than in equities. For example, there could be stronger propensity among credit investors to “sit out” negative news because of high transaction costs.

Data suggests that EMC works best in combination with relative value because combining the two signals allows investors to focus on undervalued issuers with strong recent equity performance. This approach helps avoid “value traps” — issuers that are cheap, with weak or negative equity returns — for a reason. In other words, negative equity momentum can provide credit investors with an early warning for credit performance declines.

To construct a more robust equity momentum signal, we combine past equity returns measured over different horizons. Importantly, EMC tends to be a contra-cyclical signal that often works well in credit downcycles, as it helps identify less vulnerable issuers and avoid drawdowns.

Equity Short Interest

ESI represents the sentiment of sophisticated investors about the future prospects of a company. It helps avoid actively shorted names that tend to underperform the market. Like EMC, this is a cross-asset signal.

Equity shorting is typically implemented by practiced investors who borrow a stock and sell it in the market. They buy the stock back later at a lower price in order to return it to the original owner, thereby benefiting from any price decline. They can implement bearish views by shorting individual stocks.

High levels of short interest have long been documented to predict low future stock returns (Figure 5)). Empirical results indicate that this also applies to corporate bonds. We find a consistent negative relationship between equity short interest and subsequent bond returns across geographies and rating segments. Issuers with significant short interest are risky and tend to underperform their peers.

ESI is constructed from information on shorting activity collected daily. It considers other parameters of the stock lending market for a more accurate interpretation of short interest, which is very helpful when stocks are difficult to borrow.

Putting the Signals Together

Each security will have three scores, one reflecting the strength of each signal, and a composite score, based on a weighted combination of the three. This composite score captures the relative value, trend (momentum), and sentiment drivers that are related to these signals. A systematic strategy selects securities with high composite scores, subject to a set of constraints that are imposed in order to address various considerations such as liquidity, and active risk versus a benchmark (see Building a Portfolio: A Closer Look at Our Process).

Diversification Benefits

One of the most important outcomes for systematic investors is that these signals have low correlation with each other. Data shows that combining signals with low correlation can offer significant diversification benefits to systematic active portfolios.

Our historical backtesting suggests that ESI, ESP, and EMC had low pairwise correlation consistently across all market regimes, which resulted in lower volatility and reduced drawdowns in our sample portfolios. The diversification benefits were especially apparent in historical market stress periods such as the Great Financial Crisis and the start of the COVID crisis in 2020. The low correlations stem from two sources: The signals capture different investment styles (momentum/relative value/sentiment), and they reflect information gleaned from different sets of investors.

Improving Effectiveness

Given the importance of the signals to portfolio diversification, we aim to combine the signals in an optimal way. Part of this effort includes ensuring that weighting schemes and liquidity constraints are implemented effectively.

Weighting Schemes

Given the different cyclicality of the three signals, Barclays QPS employs a constant weighting scheme that uses no hindsight, which in general tends to be more robust. Based on Barclays QPS analysis, this leads to more stable behavior in portfolios.

By contrast, dynamic weighting schemes, which depend on past performance, result in different weights from month to month. They may work well when the efficacy of each signal exhibits a trend, but at the same time, dynamic schemes are sensitive to overfitting and won’t work well when the signals’ efficacies change, such as during transition periods between macroeconomic environments.

Liquidity Constraints

Barclays QPS tested the efficacy of the combined signals for bonds with different liquidity profiles. They used their proprietary liquidity cost scores (LCS) as an estimate of transaction costs. Barclays QPS also used a proprietary score they developed over a decade ago called the trade efficiency score, or TES. It combines LCS and trading volume information to measure a bond’s tradability. Their analysis showed that the signals remain effective across securities with different liquidity profiles.

Furthermore, Barclays QPS analyzed capacity and the sensitivity of the signals to changes in the scale of assets based on them. Their analysts found that the alpha the signals generated was maintained even for very large AUM.

Our Portfolio Construction Goals

One of the most important goals in our portfolio construction process is to generate alpha using systematic, signal-based holdings. We believe that certain quantitative signals can reveal performance trends — including trends that traditional fundamental analysis may miss. We are using these signals, which pull information from both fixed income and equity data, to identify bonds that can generate excess return versus benchmarks.

Our capabilities as a large fixed income manager allow us to source bonds and efficiently build portfolios that tightly control signal exposure while optimizing liquidity and minimizing trading costs. We combine the Barclays QPS data excellence with our practical portfolio sampling and implementation experience.