We believe that now is the time for systematic investing in credit. Only more recently with the advances in technology, electronic trading, and market efficiency in credit — accelerated by the pandemic — have we finally seen an enabling environment for the capture of value-add from a systematic, factor-based investing approach.
Historical data show that a factor-based, systematic approach to investing in credit can drive outperformance relative to a broad benchmark index. In addition, systematic investing can provide a wide range of benefits to investors, such as consistent data-driven excess returns, a diversifying return profile, and clear transparency and process. Credit market innovations such as electronic trading and portfolio trading have led to greater transparency in corporate bond trading and lower transaction costs over time, facilitating the successful implementation of systematic strategies today. The implementation of systematic portfolios relies on data analysis and implementation skill to achieve three objectives:
Within the investment grade corporate universe, not all issuers or bonds are created equal. Rather, some have more (or less) desirable credit characteristics than others. For example, some companies may exhibit fundamentals such as profitability or indebtedness that are not fully reflected in their pricing. Alternatively, market sentiment may favor a different set of companies. Each criteria that could potentially lead to outperformance (or underperformance) can be identified as an alpha factor to which a portfolio tilt can be applied. A systematic strategy takes a data-driven approach that evaluates all securities in an investment universe based on their exposures to these factors in order to best position the portfolio. Three alpha factors that can be identified in the investment grade corporate bond market include value, momentum, and sentiment.
As an illustration of value as an alpha factor, we looked at its performance over time. Value addresses how attractive (rich or cheap) a bond is relative to a group of peers with the same issuer fundamentals and bond characteristics (such as quality, sector, and maturity). To capture value, the Barclays Quantitative Portfolio Strategy (QPS) team developed a relative value signal for corporate bonds. QPS’ proprietary value signal assigns scores from 1 to 10 to each bond in the relevant investment universe according to its relative valuation versus peers. A high score indicates attractiveness.
In a simple performance test, over a series of months, we partitioned the bonds that made up the Bloomberg US Corporate Index into quintile portfolios based on their value scores, and measured their returns. Figure 1 shows the cumulative excess return performance (versus like-duration Treasuries) going back to 1993 of the top-quintile portfolio, the bottom-quintile portfolio, and the index.
This analysis demonstrates the power of the investment insights that the data reveal. Quite simply, were one to invest only in securities scoring highly, the results would have been favorable. High value bonds have delivered consistently better performance than low value bonds, in the form of higher average excess returns and information ratios.
Similarly, we can use signals to capture momentum and sentiment as alpha factors to learn more about the characteristics, beyond risk factors and issuer fundamentals, that make a bond attractive and likely to outperform in the future.
The evidence shows that maximizing exposure to value and other quantitative signals with demonstrated efficacy is likely to result in positive outcomes for investors. Systematic strategies rely on data analysis and implementation skill to build these alpha factor exposures in portfolios. This approach aims to outperform an index by seeking opportunities to allocate to attractive securities, and away from those deemed unattractive according to these signals. These strategies take a rules-based approach to decision-making, with a three-fold objective:
Guardrails ensure that portfolio outcomes are driven by the issuer selection signals, rather than by unintentional exposures to market risk.
The key reason that systematic investing has been much more widely adopted in equity markets than in credit markets is that equities are considered far more liquid. However, recent innovations in fixed income markets now make it possible to cross this liquidity barrier. The proliferation of electronic trading and fixed income ETFs, giving rise to basket and portfolio trading, has improved price transparency and increased the efficiency of trading in credit, even in the face of more recent upticks in volatility. Figure 2 shows the growth of electronic trading and the rapid rise in the volume of portfolio trading inquiries over time, which have helped facilitate greater transparency and efficiency in the corporate bond market.
The key to a systematic strategy is breadth, or the ability to spread risk among a large number of small active risk exposures to issuers and bonds that score highly on various signals. This was not practical when only a thin layer of the most active corporate bonds could be traded on a liquid basis. The technological innovations in credit markets now make it more feasible to transact less liquid bonds, allowing for greater portfolio breadth. Armed with these new tools, as well as the ability to substitute hard-to-trade bonds with more investable equivalents, an experienced execution team can finally make systematic investing in credit economically viable.
The systematic approach provides several benefits to investors:
With clear signals and implementation processes in place, systematic strategies offer transparency; a complementary, diversifying return profile relative to fundamental active strategies; and attractive economics to investors.
1 Simon Polbennikov, Albert Desclée, and Mathieu Dubois, “Implementing Value and Momentum Strategies in Credit Portfolios,” The Journal of Portfolio Management, Quantitative Special Issue, 47, no. 2 (2021): 82–98, https://doi.org/10.3905/jpm.2020.1.190.
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The views expressed are the views of Thomas Coleman, Jay Hyman and Arkady Ho through March 5, 2023, and are subject to change based on market and other conditions. This document contains certain statements that may be deemed forward-looking statements. Please note that any such statements are not guarantees of any future performance, and actual results or developments may differ materially from those projected.
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Past performance is not a reliable indicator of future performance.
Bonds generally present less short-term risk and volatility than stocks, but contain interest rate risk (as interest rates raise, bond prices usually fall); issuer default risk; issuer credit risk; liquidity risk; and inflation risk. These effects are usually pronounced for longer-term securities. Any fixed income security sold or redeemed prior to maturity may be subject to a substantial gain or loss.
Sample portfolio returns shown above are hypothetical and are based on the returns of the underlying market indices in the proportions shown above. The methodology used was taking the Bloomberg US Corporate Index, dividing it into quintiles from lowest to highest Value score, and measuring the returns. The Value score is a proprietary relative value score from Barclays QPS. Market indices are unmanaged and not subject to fees and expenses, which would lower returns. Neither index performance nor sample portfolio performance is intended to represent the performance of any particular mutual fund, exchange-traded fund, or product offered by SSGA. SSGA has not managed any accounts or assets in the strategies represented by the sample portfolios. Actual performance may differ substantially from the hypothetical performance presented. Past performance is not a guarantee of future results.
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