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Yields Are Rising: Revisiting Factor Research for the Corporate Bond Market

By Dylan O'Connell  |  June 1, 2021

After a full year of the COVID-19 pandemic, where we saw unprecedented equity market volatility, we’re starting to see a reawakening of the bond market. Yields are rising, presenting an opportunity for fixed-income investors. With that opportunity, I’ve updated my previous piece, Factor Research for the Corporate Bond Market, to include the last year of the pandemic. For this iteration, I have included data from environmental, social, and governance (ESG) provider Truvalue Labs in my analysis along with the prior low risk, momentum, ESG, liquidity, and equity factor groups.

Factor Construction

By limiting our target asset class to corporate bonds, we can access the broadest array of factors on both the security as well as the ultimate issuer level.

In line with the latest research on this topic, I’ve limited my security-level factor groups to low risk, momentum, and liquidity. The focus on the low-risk factor group remains on unseasoned highly rated debt, and for momentum I’ve used changes in spread, duration times spread (DTS), and total return over various horizons. For liquidity, I’ve analyzed changes in traded volume and bid-ask spreads.

Using a point-in-time map of ultimate parent issuers for Bloomberg Barclays Global Corporate index constituents gives us access to a wide variety of creditworthiness and ESG metrics. Keeping the standard ESG ratings, I’ve also included some more specialist scores from Truvalue Labs. While also providing the expected metrics, Truvalue moves beyond a ratings approach to include Insight, Momentum, and Rank scores. It also provides more nuanced scores for topical issues such as supply chain management, data security, and business model resilience.

ESG Scores

Having a link to the ultimate parent gives us full access to the underlying financial statement data. I’ve limited my analysis to changes in capital structure, use of cash, and various debt ratios, but there’s a huge swath of data points available for further analysis.

Equity Factors

Using these factor groups separately and as part of a combined strategy will allow me to eventually test whether low risk, high momentum, high ESG, or liquid issues where the issuer uses cash to service debt promptly can outperform the universe benchmark.

All Factors

The quickest way to view all factors is using a scatterplot of the information coefficient (IC) vs. Sharpe ratio; the factors showing promise appear in the top-right corner. As in my previous analysis, the factors that show promise are spread across the low risk, momentum, and equity factor groups.

Information Coefficient vs Sharpe Ratio

As with previous research, ESG doesn’t show significance as an investible factor; this holds true when the data is expanded to include the additional Truvalue scores. Interestingly, some of the Truvalue scoresshowed significant improvement in the IC when comparing the period before the pandemic (light blue) with the period of the pandemic (dark blue). This hints that further refinement under a market regime model might be warranted.

Information Coefficient vs Sharpe Ratio ESG Scores

All Factor Groups

Combining each of the most promising factors from each group into an equally weighted composite factor results in the same fractile profile we saw previously. Strong investible signals are seen for both momentum and low-risk groups; equity also shows promise but to a lesser extent.

Fractile Analysis of Composite Factors

Digging into each factor group individually will show us which factors are driving the overall performance within the group.

Momentum Factor Group

Going further into the momentum factor group, I calculated the change in various spread and total return levels for a lagged period up to one month prior to the period under analysis. As shown in my previous analysis, the momentum in narrowing spread and DTS stand out as the strongest factors.

Momentum Factor Group

Low Risk Factor Group

Risk can be defined in multiple ways but in this analysis, I’ve focused on rating (a direct risk proxy), how seasoned an issuance is, and the overall debt burden of the issuer. I’ve used a composite rating for the actual issue when available; when it isn’t, I used the issuer’s rating. The rating, time since issue, and time to maturity factors all show promising fractile profiles.

Low Risk Factor Group

Equity Factor Group

Digging deeper into the equity factor group, the factors I tested focused on issuers that have a low overall debt burden (absolute, relative to EBITDA, and relative to cash flow), as well as issuers with low use of cash for dividend or equity. With a simple fractile analysis, two stand out as potential signals: Debt/EBITDA and Debt/FFO.

Identifying Signals from the Equity Factor Group

This is not to say that the other factors might not be useful; their signal strength might be more evident over a different horizon or as a component of a composite factor.

A Single Dynamic Strategy Factor

Limiting the factors to those showing significance across the momentum, low risk, and equity groups, I constructed a dynamically weighted composite strategy factor. In creating my composite strategy, I used the trailing month information coefficient of each of the components as the weighting, yielding the below profile.

Factor Weights

Running the total return for my strategy factor, we see the fractile profile matches what we would expect for a significant investible factor (fractile 1 outperforms fractile 2, fractile 2 outperforms fractile 3, etc.). This continues the same trend shown in our last analysis.

Total Return Comparison Across Fractiles

Comparing securities in the top fractile to the benchmark, we see significant outperformance over the evaluation period (approximately 20%). The strategy was particularly effective at amplifying the pre-pandemic 2018 bull market.

Top Fractile vs Benchmark

Conclusion

The follow-up analysis performed in this article doesn’t account for trading costs, limits on shorts, and other mandate considerations, but it could be performed in an optimization tool. Additionally and as seen with the Truvalue factors, there have been widely varying macroeconomic environments over the last few years (e.g., low inflation, low rates) and our analysis could be further sharpened using a market regime model approach particularly given the current projection of rising treasury yields.

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Dylan O'Connell

Vice President, Principal Analytics Specialist, EMEA

Mr. Dylan O’Connell is Vice President, Principal Analytics Specialist, EMEA at FactSet. He started with FactSet in the United Arab Emirates in 2012, shortly after the office opened in Dubai. There he covered asset management and sovereign wealth fund clients, before joining the analytics team in London in 2015 to focus specifically on FactSet’s Fixed Income product suite. He now works as an analytics specialist for the new business and key accounts teams covering the UK, Middle East, and Africa. Mr. O’Connell earned an honors degree in Economics and Finance from the University of Cape Town.

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