How To Invest In Bonds Despite Rising Rates

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Includes: AGG, BND, CWB, IEF, JNK, MUB, SHY, TLT, WIP
by: Fred Piard
Summary

Should investors stay away from bonds in a rising rate environment?

Probably not, with the condition of applying 2 rules.

First, diversifying across various bond categories.

Second, following relative and absolute trends in these categories.

This article gives an example of strategy implementing these rules.

Keeping some bond exposure in a rising rate environment may still temper a stock portfolio’s volatility without being a heavy drag, but it needs a process to allocate dynamically the capital to the right kinds of bonds. A 2-position bond ETF model has been added this year in Quantitative Risk & Value. This article gives insights on the strategy and its performance on various time frames.

History and rationale of the strategy

The first version of this model was designed in 2013. At this time, I had not yet started the research on market-timing indicators that resulted in the concept of systemic risk and MTS10. I just wanted a bond model outperforming the U.S. Aggregate Bond Index (AGG, BND). It is a dynamic allocation model in a bond universe, inspired by readings on tactical asset allocation, mostly articles and white papers by Mebane Faber and Gary Antonacci. It belongs to the dual-momentum category: there is an absolute momentum indicator, and a relative momentum indicator. As I think simplicity is a factor of robustness, the model is quite simple. However, the exact rules are kept private. The list of possible holdings includes US treasuries on the short-term (SHY), mid-term (IEF) and long-term (TLT), aggregate bonds (BND), high yield corporate bonds (JNK), convertibles (CWB), municipal bonds (MUB), inflation-protected international treasuries (WIP).

Performance since launch

The first version was launched for subscribers on another platform in January 2014. Since then, the set of possible holdings has been slightly modified, but the basic rules are still the same. Like in any tactical allocation strategy, there is a balance to find between a big set, which may result in increased whipsaw and transaction costs, and a small set, which may result in missing some trends. The next table shows the returns of the strategy since it is available to subscribers, compared with BND. ETF dividends are reinvested.

2017

2016

2015

2014

Compounded

2014-2017

Compounded Post-QE 2015-2017

Bond Rotation

6.3%

10.8%

-4.0%

8.2%

22.4%

13.1%

BND

3.5%

2.5%

0.4%

6.0%

12.6%

6.3%

The table shows total returns by year since inception and post-QE. In fact, QE3 purchases were halted in October 2014.

20-Year Treasury Bond rate, from St Louis Fed database

The Bond Rotation Model has fulfilled its objective, beating BND by about 10% in total return since 2014, and 7% post-QE. We can also have a look at simulations on past data.

Simulations

The next chart shows an “in-sample” backtest from 5/1/2009 to 12/31/2013 in red (design test period), with BND as benchmark in blue. Before May 2009, price data were not available for all possible holdings.

Bond Rotation simulation 2009-2013, on portfolio123

The next chart simulates the model with a reduced list (2 ETFs missing) from 1/1/2008 to 5/1/2009, including the 2008 stock market crash.

Bond Rotation simulation 2008-2009, on portfolio123

Recent performance

Finally, below is the performance since the model is available to my subscribers on Seeking Alpha (about 1 month from 1/1 to 1/26/2018 on close). The Bond Rotation is in red (+3.7%), BND is in blue (-0.7%).

YTD performance, on portfolio123

Conclusion

The data above tell that a tactical bond allocation, and more specifically this Bond Rotation model, may be an interesting strategy for:

  • Bond investors trying to beat bond benchmarks.

  • Investors willing to keep a significant exposure in bonds, despite rising rates.

  • Investors following any kind of static stock/bond allocation (80/20, 60/40, 50/50...).

  • Subscribers following the RMSB model, by replacing the 2 static bond ETF positions with 2 dynamic positions.

Performance in the recent (and short) post-QE period gives some clues that the model might continue outperforming its benchmarks in a rising rate environment. Simulation during the 2008 crash shows that the model was able to outperform its benchmark in a very volatile market. However, past performance, real or simulated, is not a guarantee of future results.

This is for information purposes, not investment advice. Data and charts are provided by portfolio123 (this is a partner link giving you an extended period of free trial. I may receive a fee if you buy later a paid subscription, at no additional cost to you).

Disclosure: I am/we are long CWB, WIP, TLT. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: I may buy or sell any of these ETFs at any time depending on my models' signals.