Can equity investors profit from corporate bond market data? Yes. In fact, stock portfolios constructed based on bond momentum signals can outperform their traditional stock price momentum counterparts.
But as our study shows, signal design is crucial.
Momentum spillover
The momentum spillover effect describes the signal that an organization’s bond momentum sends about its future stock returns and is attributed to information asymmetry in financial markets.
There are several the explanation why bond market data could provide unique insights for equity investors:
- Institutional investors with advanced expertise and access to more and higher data dominate the bond markets in comparison with their equity counterparts. This may give bond markets an information advantage.
- Because bonds have more predictable future money flows, their prices may higher reflect their fundamental value.
- Low liquidity and high transaction costs can protect bond markets from speculation and short-term volatility.
Bond momentum design
To reap the benefits of the momentum spillover premium, an appropriately designed bond momentum signal is required. Unlike equity momentum, there isn’t any consistent definition for bond momentum. According to scientific literature, there are three types of binding impulse signals:
- Total Return Bond Momentum reflects the mixture total return of all of an organization’s outstanding bonds.
- Excess return bond momentum describes the difference between the whole return of a bond and the whole return of a duration-adjusted risk-free bond.
- Spread change bond momentum is the negative value of the spread change.
In “Dynamics in corporate bond returnsGergana Jostova et al. examine total return bond momentum and discover a robust momentum effect in non-investment grade bonds. However, rating stocks based on total bond return or rate of interest and spread yield will not be advisable, as the previous is a scientific factor determined by the dynamics of presidency rates of interest. As a result, the rate of interest risk of an organization’s debt can have a big impact on total return bond momentum. For this reason, we’ll focus here on spread change bond momentum and excess return bond momentum.
Applying Bond Momentum to a Stock Portfolio
Our bond data set is predicated on the Russell 1000 stock universe and begins in 2003, shortly after the launch of the Trade Reporting and Compliance Engine (TRACE) bond database. We mapped corporate bonds to their stocks using a typical corporate ID. As of December 2022, roughly 60% of Russell 1000 firms, representing 86% of the index’s total market capitalization, have bond data.
We calculated market value-weighted excess bond returns and spread changes for all debt issuing firms with a trailing three-month lookback window and created portfolios that mimic aspects by sorting stocks into quintiles (Q1 to Q5) based on their bond momentum scores. The first chart shows the performance summary of equally weighted and market capitalization weighted Q1 to Q5 factor portfolios, in addition to a Carhart Momentum Factor portfolio for comparison purposes.
Both bond momentum signals outperformed traditional equity momentum on an equal and market capitalization-weighted basis and had higher information ratios. Additionally, spread change bond momentum outperformed excess return bond momentum with higher annualized returns in the primary quarter and yield spreads between the primary and fifth quarters.
Summary of hypothetical bond momentum portfolio performance
(Russell 1000, 2003 to 2022)
| Portfolio | Excess return bond momentum | Spread change bond momentum | Stock dynamics | ||||||
| Annualized return | Excess return | information ratio | Annualized return | Excess return | information ratio | Annualized return | Excess return | information ratio | |
| Balanced portfolio | |||||||||
| Q1 | 12.2% | 1.9% | 0.34 | 12.9% | 2.7% | 0.41 | 11.5% | 1.3% | 0.24 |
| Q2 | 12.5% | 2.3% | 0.44 | 12.6% | 2.4% | 0.47 | 11.3% | 1.1% | 0.28 |
| Q3 | 12.6% | 2.4% | 0.47 | 12.1% | 1.9% | 0.40 | 12.0% | 1.7% | 0.36 |
| Q4 | 11.3% | 1.1% | 0.25 | 11.1% | 0.9% | 0.23 | 11.4% | 1.2% | 0.25 |
| Q5 | 11.1% | 0.9% | 0.20 | 10.9% | 0.7% | 0.19 | 12.9% | 2.7% | 0.29 |
| Q1-Q5 | 1.1% | – | – | 2.0% | – | – | –1.4% | – | – |
| Market capitalization weighted portfolio | |||||||||
| Q1 | 10.0% | –0.2% | 0.04 | 10.5% | 0.3% | 0.10 | 9.3% | -0.9% | -0.11 |
| Q2 | 10.9% | 0.7% | 0.17 | 11.4% | 1.2% | 0.29 | 11.3% | 1.1% | 0.26 |
| Q3 | 10.6% | 0.4% | 0.11 | 10.7% | 0.5% | 0.11 | 10.7% | 0.5% | 0.14 |
| Q4 | 10.1% | –0.1% | -0.02 | 9.4% | –0.8% | -0.13 | 9.3% | -0.9% | -0.12 |
| Q5 | 8.8% | –1.4% | -0.24 | 7.6% | –2.6% | -0.36 | 10.5% | 0.3% | 0.13 |
| Q1-Q5 | 1.2% | – | – | 1.9% | – | – | –1.2% | – | – |
The data contained herein doesn’t represent the outcomes of an actual investment portfolio but reasonably reflects hypothetical historical performance. Past performance is not any indication of future results.
evaluation
It is not any coincidence that spread change bond momentum exceeds excess bond momentum. There are some basic explanations for this result. Using basic bond math, we decompose the bond excess return into the spread carry return and the spread price return in Equations 1 through 6 below. The spread carry return is a function of the spread level, while the spread price return is decided by the spread change. The spread change is the one component that directly captures company-specific market sentiment.
We also applied Fama-Macbeth regressions to further evaluate the 2 binding impulse signals. Specifically, we ran cross-sectional regressions every month, using one-month stock returns as independent variables and customary stock aspects plus bond momentum as dependent variables. The model results are shown in the next table.
Stock return and bond momentum aspects: cross-sectional evaluation, 2003 to 2022
| Model 1 | Model 2 | Model 3 | Model 4 | |
| Interception | 0.0103 [3.46] | 0.0103 [3.44] | 0.0106 [3.56] | 0.0105 [3.52] |
| market | 0.0024 [1.49] | 0.0024 [1.47] | 0.0024 [1.45] | 0.0024 [1.46] |
| Size | 0.0006 [1.59] | 0.0006 [1.55] | 0.0006 [1.70] | 0.0007 [1.85] |
| Value | -0.0004 [-0.53] | -0.0004 [-0.48] | -0.0004 [-0.49] | -0.0004 [-0.50] |
| ROE | 0.0001 [0.04] | 0.0002 [0.06] | 0.0001 [0.02] | –0.0001 [-0.02] |
| Low Vol | 0.0133 [1.55] | 0.0126 [1.49] | 0.0122 [1.46] | 0.0122 [1.45] |
| Momentum | 0.0034 [0.85] | 0.0029 [0.75] | 0.0026 [0.67] | 0.0028 [0.71] |
| Excess return bond momentum | 0.0357 [1.71] | -0.0072 [-0.25] | ||
| Spread change bond momentum | 0.1957 [2.54] | 0.2209 [2.10] | ||
| R^2 | 0.1347 | 0.1382 | 0.1381 | 0.1403 |
The data contained herein doesn’t represent the outcomes of an actual investment portfolio but reasonably reflects hypothetical historical performance. Past performance is not any indication of future results
Model 1 is a basic Fama-French three-factor model plus return on equity (ROE), low volatility and momentum. Model 2 extends Model 1 by adding excess return bond momentum as an independent variable. Model 3 uses spread change bond momentum as an extra variable, while model 4 includes each bond momentum signals as explanatory variables.
The results of Model 2 and Model 3 suggest that each bond momentum signals can increase the explanatory power of the bottom model and Model 1, respectively. When included as a standalone variable, Spread Change Bond Momentum has higher statistical significance than Excess Return Bond Return, and when each signals are included, Spread Change Bond Momentum can higher predict future stock returns.
Diploma
The more comprehensive bond data becomes available, the more scholars and practitioners will apply it to stock signals research. Based on our corporate bond evaluation of U.S. large-cap stocks, a well-crafted bond momentum signal that effectively captures market sentiment will help generate significant equity alphas, and as our backtest and cross-sectional evaluation show, Spread Change Bond Momentum is essentially the most effective technique to reap the benefits of this momentum spillover premium.
further reading
Bittlingmayer, G. and Shane Moser. “What does the corporate bond market know?” .
Chan, Louis KC, Narasimhan Jegadeesh and Josef Lakonishok. “Momentum strategies.” .
Dor, Arik Ben and Zhe Xu.Should equity investors care about corporate bond prices? Using bond prices to develop equity momentum strategies.” .
Gebhardt, William R., Soeren Hvidkjaer and Bhaskaran Swaminathan. “Interaction between stock and bond markets: is the dynamic spilling over?” .
Israel, Ronen, Diogo Palhares and Scott A. Richardson. “Common factors in corporate bond returns.” .
Gergana Jostova, Stanislava Nikolova, Alexander Philipov and Christof W. Stahel. “Dynamics in corporate bond returns.” .
Lee, Jongsub, Andy Naranjo and Stace Sirmans. “CDS Momentum: Slow Credit Ratings and Cross-Market Spillover Effects.” .
Wiltermuth, Joy J. “Electronic trading in US corporate bonds is finally gaining momentum. But it’s still early days, says this investor.” .
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Photo credit: ©Getty Images/atakan
