This blog post is available as a PDF here.


  • The term “smart beta” is ubiquitous in the market these days as investors acknowledge the benefits of moving away from price-based market cap weighted indices.
  • This trend has been much more prevalent in equities than in fixed income.
  • However, market cap weighting fixed income has an unsettling consequence; it leads to higher allocations for companies with more debt, regardless of their ability to service it.
  • While there is a limited set of smart beta fixed income ETFs available in the market, thoughtful strategies can mimic some of the benefits of smart beta fixed income, leading to portfolios with better overall risk management.

Market cap weighted equity indices are somewhat like a popularity contest. The more investors bid up certain stocks, the larger weight that stock will garner in the index.

But as we are often reminded by looking at the wall in many 5th grade classrooms: “What is right is not always popular and what is popular is not always right.”

If we frame it in a more relational sense, market cap weighting is like investing money with people in proportion to their net worth. The implicit assumption is that a high net worth means that those people will generate the most return for us, regardless of how they invest, whether their methods have changed, and what their current prospects are.

Rather than market cap weighting, many indices now resort to alternative weightings that fall under the name “smart beta”. These are rules-based indices that obey any weighting scheme but market cap – from as simple as equal weighting, to weighting based on a single factor such as value or momentum, to something more complex like multi-factor products or a strategy based on harvesting factor premiums…in factor premiums!

As the assets in ETFs have grown, so has the proportion of assets in smart beta ETFs.

Smart Beta Flows

But what about market cap weighting fixed income?

By market cap weighting, we are investing more money in larger issues, i.e. in bonds of companies or countries with the most debt.

To continue our previous analogy, this is like lending money to people in proportion to their net debt. Our “index” is allocated more heavily to those with multiple mortgages and hefty credit card bills. And if they rack up more debt, they get even more lent to them.

One could say that bond ratings prevent some of the risks that come with market cap weighting fixed income. Presumably, if a company issues debt beyond its ability to repay, its rating will go down. But since many indices cover a range of credit ratings, there is no change made until the rating passes a lower threshold (e.g. a bond in a corporate bond index moving to a high yield index).

Another argument is that investors are compensated for the elevated risk with higher yields. While this is true in principle, it has not been borne out across the board (e.g. in corporate bonds).

It also assumes that we are able to accurately gauge the elevated risk we are undertaking. The housing market during the financial crisis illustrated that this is not always the case.

To better assess the risks of market-cap weighted fixed income within a specific market segment, consider high yield bonds. As the credit cycle ebbs and flows, more companies will tend to issue debt at times when credit is easier to come by and borrowing terms are favorable, potentially weighting investors even more towards vintages of bonds that are pressured as credit inevitably tightens up down the road.

Also remember that size of a bond in the high yield or loan space does not necessarily correspond to liquidity. Not only are you loading up on riskier assets, but you are possibly biasing your portfolio toward assets that may not trade easily during the riskiest market environments.

Going beyond a sector level view, in broad market fixed income indices, market cap weighting doesn’t always mean we are taking on more risk, but it still may mean we are allocating more to assets we do not necessarily want. For example, Jack Bogle has come out against the Barclays Aggregate bond market index for U.S. investors because it allocates over 50% to U.S Treasuries and mortgage-backed securities, most of which are owned by investors outside the U.S. Basically, his argument is that it does not represent the U.S. bonds held by U.S. investors, which is what most investors are after.

Either way you look at it, the implications of market cap weighting fixed income seem more pressing than those in equities.

Yet, even if we can agree that this is not the right way to go about constructing an index, it sure is popular. As of November 2015, smart beta bond ETFs comprised only 1.4% of the total fixed income ETF market, according to article entitled “Can Smart Beta Bond ETFs Gain Traction?”. Common reasons cited for the lack of smart beta ETFs in fixed income are low liquidity in certain bond issues, the shear size and general over-the-counter nature of the bond market, and the lower premiums garnered by bonds relative to equities

There is also an issue of getting the historical bond data to back up smart beta approaches in fixed income in the academic sphere, as has been done with many smart beta equity approaches, but promising research has nevertheless been done.

Asness and company showed that value and momentum work in government bonds (Value and Momentum Everywhere), and Jostova et. al. showed that momentum has worked in corporate bonds (Momentum in Corporate Bond Returns). Still, we have yet to see the broad adoption of these strategies in fixed income.

Despite these challenges, if we truly believe that market-cap weighting in bonds is a structural flaw given that the market does not always correctly price risk, what are some options that currently exist in the market?

They may be more difficult to find but here are some ways that companies are packaging smart beta fixed income strategies, which we will classify into two categories:

  1. Those seeking to generate increased return or capture a premium (return generators)
  2. Those seeking to simply weight the universe in a more thoughtful way (risk mitigators)

While these examples are not exhaustive, the intent was to pick a representative sample of smart beta fixed income ETFs currently available. If you’d like our take on any other products in the market, feel free to reach out.

Category 1: Return Generators

These ETFs are generally trying to seek a return premium. Many may seem like actively managed strategies at first glance, but they all track rules-based indices, making them smart beta in our book. The rules may vary in complexity, but in theory, armed with the index methodology and the underlying data, you could derive the weights yourself.

Barclays U.S. Aggregate Bond Enhanced Yield Fund (AGGY)

Last summer, WisdomTree launched the Barclays U.S. Aggregate Bond Enhanced Yield Fund (AGGY), which reweights the components of AGG to maximize yield subject to constraints on duration, sector concentration, turnover, and tracking error (for more info on tracking error constraints, see this past commentary).

We can see the differences play out in both credit quality and sector allocations between the two funds.

AGGY Credit AGGY Sector

Source: WisdomTree, iShares, Data as of 4/20/2016.

AGGY takes on more credit risk to increase its yield. Its duration is 6.42 years compared to AGG’s 5.19, so it also takes on more interest rate risk. But from the sector allocations, we see that it is more diversified across asset classes and even buffers with some cash.

Market Vectors Fallen Angel ETF (ANGL)

ANGL is simpler than AGGY in its portfolio construction, but it also attempts to earn a premium in fixed income. While AGGY focuses on the broad bond market, ANGL focuses on high yield by investing in fallen angels, securities that were originally issued as investment grade but have fallen into the realm of high yield bonds.

Because the bonds were originally issued by solid companies with investment grade ratings, the hope is that the bonds are of better quality than high yield bonds that are issued with junk status. In addition to this quality exposure, because the bonds likely declined significantly in value as the company’s debt was downgraded, ANGL is also pursuing the value factor.

One interesting phenomena seen in ANGL is its contrarian approach. We can see this on a sector level, most recently, with the energy sector.

ANGL Sectors

Source: Morningstar. Sector holdings as of 3/31/15 and 3/31/16.

As oil prices dropped, many energy companies that had issued debt with good credit ratings eventually found heir bonds in the high yield category. While ETFs like HYG and JNK would hold these, ANGL would hold them in more concentrated positions as long as other sectors were not under as much credit pressure.

Between 2015 and 2016, ANGL cut its already underweights to consumer staples, healthcare, and utilities. It beefed up its overweight to materials and reduced its position in financials back in line with that of JNK.

From JNK’s peak on September 2, 2014, it suffered a maximum drawdown of 16.9% compared to ANGL’s drawdown of 10.1% from that point. From the lowest points in the drawdowns, JNK regained 12.6% until 4/20/2016 resulting in an overall return of -6.5% (-4.0% annualized) while ANGL gained 16.8% to end up 5.0% (3.0% annualized).

In a low interest rate environment when many high yield bonds suffered, ANGL’s quality and value came through as a benefit for investors relative to JNK.

Category 2: Risk Mitigators

The ETFs in this category are generally not explicitly trying to generate excess returns; by weighting their holdings based on more logical schemes (e.g. fundamental factors), they aim to manage risk, and may generate higher returns as a consequence. Many of these ETFs zero in on specific fixed income market segments such as high yield, corporates, and emerging market bonds.

PowerShares Fundamental Bond Portfolios (PHB and PFIG)

In terms of specific sectors, PowerShares offers both high yield (PHB) and corporate bond (PFIG) ETFs that are weighted based on fundamental measures of the company’s capacity to service debt. This methodology is driven by Research Affiliates who has been advocating for fundamentally weighted indices before “smart beta” had the buzz it does now.

Again, we can see how these differences are manifested in the sector allocations relative to more standard, market-cap weighted benchmarks for high yield (HYG) and corporates (LQD).

Corporate Sector High Yield Sectors

Source: iShares, PowerShares. Note that Consumer Staples contains both Consumer Staples and Health Care since iShares shows them both as Consumer Non-Cyclical. Data as of 4/20/16.

From these charts we notice some differences between the market-cap weighted ETFs and their smart beta counterparts. The smart beta version of the corporate bond index allocates away from telecom and financials in favor nearly every other sector. The smart beta version of the high yield index allocates away from telecom and mainly into consumer discretionary and energy.

Apparently, telecom and financial companies issue a lot of debt but, using the methodology of Research Affiliates, are not deemed to have enough capacity to service it.

FlexShares Credit-Scored US Corporate Bond Index Funds (SKOR and LKOR)

FlexShares also offers smart beta fixed income options for corporate bonds: the Credit-Scored US Long Corporate Bond Index Fund (SKOR) and the Credit-Scored US Long Corporate Bond Index Fund (LKOR). In the ETFs, they first determine a credit score for each bond and then aim to maximize the average credit score subject to some constraints on duration and other characteristics of the bond universe relevant to each ETF. This process yields a corporate bond index tilted more toward quality rather than toward issuers who have the most debt.

Corporates Credit

Source: iShares, FlexShares. Data as of 4/20/16.

Compared to the iShares Intermediate Credit Bond (CIU), which has a duration similar to that of SKOR, SKOR has much more of its bonds rated as A or BBB and none rated BB.

Anticipating More Innovation

While there are other smart beta fixed income ETFs out there, these examples highlight two ways we are currently seeing it done.

In one sense, smart beta is more difficult in fixed income because of our old friend, Math. Returns in equities can often be described using equations (e.g. decomposing stock returns based on factor exposure), but many of these methods are statistical based on past data. There may be confidence that they will hold going forward, but there is not certainty.

With a bond, we know that a given increase in interest rates will have a certain effect on the price, assuming negligible default risk. This more stringent rule places additional constraints on how much control one has when designing a smart beta bond strategy.

For many of the same reasons why tactical fixed income is different, smart beta fixed income is different. When allocating to bonds in a tactical fixed income strategy, a simple, go to cash model will likely leave much of the income on the table. If income is an objective, a more thoughtful strategy will be required to maintain yield while protecting capital.

For smart beta fixed income products, we see similar effects. Say your goal is to construct a broad-based smart beta fixed income index using bond quality to determine allocations. If income is a goal, having an index constructed purely based on quality is likely to miss the mark since it will be highly concentrated in safe, short-maturity assets. Even if the product earns a premium, investor leverage aversion will make it difficult to sell as a general fixed income asset.

Rather, thoughtful smart beta fixed income products that can balance factors like quality, duration, and credit sensitivity are required to have general appeal. Addressing specific areas of the portfolio (income generation, equity risk mitigation, and fixed income tail risk management) are good areas to start.

The human mind is a wonderful thing, and we fully expect more smart beta fixed income ETF options to hit the market in the coming years. As market and liquidity issues possibly improve, implementation will become easier. And as we begin to experience lower potential growth in equities and fixed income, there may be more demand for smart beta fixed income. After all, necessity is the mother of invention.

In the Meantime…

But until the time when smart beta ETFs hit the market and become decently tradable, there are strategies we can employ to mimic some of the benefits of smart beta strategies.

In our Target Excess Yield suite, we take a similar approach to that of AGGY but flip it around to target a specific yield above short-term U.S. Treasuries while minimizing risk (duration, credit, currency, volatility, tail risk, etc.). By slicing up the fixed income market into its sub-asset classes spanning a variety of risk factors, we aim to capitalize on yield and diversification opportunities. You could do something similar using mean variance optimization as outlined in our recent article.

In our Multi-Asset Income strategy, we allocate to 16 different income-focused asset classes in proportion to their risk adjusted yields. While the universe is split about 50/50 between fixed income and equity-like exposures, all have some degree of interest rate exposure. The strategy selectively removes exposures exhibiting negative momentum and cash move fully to cash, but a strategic portfolio weighted based on risk-adjusted yields could be a good smart beta substitute.

Like smart beta strategies in equities (e.g. value, momentum, low volatility, etc.), we know that those in fixed income will not (and cannot) outperform all the time. Combining smart beta fixed income strategies either with other smart beta ones or tactical strategies is one way to mitigate this risk. Multiple long-term outperforming strategies paired with staggered short-term underperformance is a universal recipe.

Only time will tell whether smart beta fixed income ETFs will catch on. In the meantime, developing a thoughtful, smart beta strategic portfolio can be a good way to manage some of the risks associated with market cap weighted fixed income and the risk of not sticking with a strategy through lack of disciplined rules.


Nathan is a Portfolio Manager at Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Nathan is responsible for investment research, strategy development, and supporting the portfolio management team. Prior to joining Newfound, he was a chemical engineer at URS, a global engineering firm in the oil, natural gas, and biofuels industry where he was responsible for process simulation development, project economic analysis, and the creation of in-house software. Nathan holds a Master of Science in Computational Finance from Carnegie Mellon University and graduated summa cum laude from Case Western Reserve University with a Bachelor of Science in Chemical Engineering and a minor in Mathematics.