Four Questions for Evaluating Strategic-Beta Fixed-Income Funds

Strategic-beta (often called "smart beta") fixed-income funds attempt to offer the best of active and passive management: benchmark-beating performance with transparent portfolio construction rules and low fees. Evaluating strategic-beta fixed-income funds can appear to be complex. However, by asking the following four questions investors can easily perform litmus tests on this new breed of fixed-income funds.

1. What is the investment universe? 2. How does the strategy select bonds? 3. How does the index weight bonds? 4. How much active risk does the portfolio take?

The investment universe provides a rough idea of how risky the strategy may be. For example, a junk-bonds-focused fund is likely to exhibit higher volatility than an investment-grade-oriented strategy. Also, this starting point serves as the yardstick against which an investor can measure whether a strategic-beta fund has delivered value.

The process by which a fund selects and weights securities is the most important area to evaluate to understand how it will likely perform. Investors should be wary of strategies that aren't supported by a clear economic theory, like

Investors must look for an investment thesis supported by a sensible economic rationale, and must be cautious about strategies heavily relying on back-tested results. For example,

It is important to carefully examine how the fund defines the factors it uses to select securities. There is a virtually infinite number of factors to choose from, since it is up to a fund company's discretion to define what constitutes a factor. For example,

Active risk is necessary to beat traditional market-cap-weighted benchmarks. Funds that stray further from those benchmarks (taking greater active risk) have greater upside potential, but also greater potential to underperform. The magnitude of a fund's active risk is determined by how aggressively it pursues its targeted factors, including how stringent its selection criteria is and how it weights its holdings. Tracking error relative to a cap-weighted benchmark is a useful gauge to assess how much active risk a fund takes.

Below, I will apply the four-question framework to

What Is the Investment Universe? The investment universes of GIGB and IGEB are nearly identical except for their minimum issue size. Though less relevant for these two funds, the issue-size criterion can be an important distinction when evaluating other portfolios. This is because the liquidity of a security is directly related to its issue size, and the liquidity of constituent securities in an index dictates whether the benchmark can be replicated at reasonable costs. GIGB tracks the Citi Goldman Sachs Investment Grade Corporate Bond Index, which has a minimum issue size of $750 million, whereas IGEB's benchmark, the BlackRock Investment Grade Enhanced Bond Index, requires a minimum amount outstanding of $500 million. The $250 million difference may look significant, but it is unlikely to affect IGEB's index-tracking capability given the ample liquidity of the U.S. investment-grade bond market. However, it is likely to cause IGEB to lag its index more than GIGB does during market downturns when liquidity evaporates. Both funds invest in U.S.-dollar-denominated, BBB or higher-rated corporate bonds with at least one year remaining until maturity.

How Does the Strategy Select Bonds? GIGB ranks bonds by their year-over-year changes in leverage (debt to enterprise value) and operating margin (EBIT over revenue) ratios within its larger-issuer biased universe. The bottom-decile-ranking issuers are then removed. This is because the bottom-decile group historically exhibited unfavorable risk/return traits. The bottom band experienced twice the standard deviation while delivering one third the return among its investment-grade bond peers from February 2007 to November 2017, according to Goldman Sachs.

This approach captures the improving or declining health of companies better than a traditional index, which takes a snapshot of debt loads at a given point in time rather than how those metrics change over time. Goldman Sachs developed this screening tool in collaboration with its internal actively managed fixed-income credit analysts, who believe these two accounting-driven measures (leverage, a balance sheet ratio; and operating margin, an income statement ratio) are good indicators to avoid companies with deteriorating fundamentals.

On the other hand, IGEB incorporates myriad market data such as the prices of issuer's equity and options, as well as economic variables, to estimate their probability of default over the next 12 months. The strategy's quality factor screen then removes the 20% of issuers with the highest probabilities of default within each credit rating bucket. Based on its default estimate, the portfolio recalculates the option-adjusted spread of the remaining securities, and tilts toward the bonds with the widest OAS. This step is IGEB's way of capturing a value factor. BlackRock relies on market data more than accounting numbers because it believes quarterly financial statement data are slower to detect deteriorating fundamentals than market prices.

Both accounting-driven and market-driven approaches are sensible ways of measuring default risk. GIGB's accounting-based methodology is transparent and simple to understand. But the strategy's reliance on accounting data exposes it to possible financial manipulation and this data is backward-looking. On the other hand, IGEB constructs its portfolio based on forward-looking measures such as stock prices and options, which could be quicker to detect deteriorating fundamentals than accounting data.

How Does the Index Weight Bonds?

GIGB employs a market-cap-weighting approach for its holdings while excluding the bottom-decile group based on its screening method. The resulting portfolio has 45% allocation to BBB rated bonds and 40% exposure to A rated bonds. Its sector allocations were approximately 65% industrial and 30% banking as of June 13, 2018. GIGB's market-cap-weighting coupled with its emphasis on leverage gives the portfolio a slight tilt toward large industrial firms with big balance sheets, such as

IGEB again utilizes its opaque optimization method to weight its holdings to maximize the portfolio's expected return metric, which is calculated as OAS minus probability of default times 1 minus recovery rate assumption. Though the optimization goal is sensible, it still lacks transparency. The resulting portfolio had a sector breakdown of 75% industrial and 10% banking, and a credit rating distribution of 80% BBB and 20% A, as of June 13, 2018. Given the less-than-transparent construction process, it is unclear how the sector and credit rating distribution will change over time and the implication for investors.

GIGB rebalances monthly and does not have any specific reconstitution constraints, which means this strategy could be exposed to sector-concentration risk. This is because its cap-weighted portfolio is heavily influenced by underlying debt-issuance activities. For example, large international banks are some of the most active debt issuers. Consequently, 30% of the portfolio is invested in financial bonds. This sector tilt, however, is on par with the portfolio's starting universe (the Citi USBIG Corporate Index), as well as the popular cap-weighted corporate bond index, the Bloomberg Barclays US Corporate Index. At the same time, this market-cap-weighting method reduces turnover and transaction costs.

IGEB has an explicit 12% turnover limit for its monthly rebalancing. In addition, the fund's duration, duration-times-spread, and issuer weights are kept close to its underlying index.

How Much Active Risk Does the Fund Take? The degree of active risk for both funds is manifested through how they select and weight their securities. Overall, IGEB pursues its factor tilts more aggressively than GIGB does, giving it greater upside potential while assuming more downside risk. First, IGEB excludes 20% of its starting investment universe, while GIGB only removes 10%. Second, IGEB optimizes its holdings to maximize the portfolio's spread; meanwhile, GIGB simply weights its positions by market value. This construction approach causes IGEB's portfolio to deviate further from its starting universe than GIGB, giving it greater active risk.

IGEB's allocation to bonds rated BBB, a notch above junk, was 30 percentage points more than the Markit iBoxx USD Liquid Investment Grade Index's. In contrast, GIGB's BBB exposure is currently in line with that benchmark's. If credit risk continues to pay off, IGEB is highly likely to best GIGB and vice versa. However, BlackRock's opaque security-selection process is less appealing than Goldman Sachs' straightforward approach since it is difficult for investors to anticipate how IGEB would behave under extreme market conditions.

Conclusion Investors have been reluctant to dabble in these so called smart, or strategic-beta, fixed-income funds not only because of their limited records but also because of their complexity. But investors can always start by asking the four questions, no matter how complex these strategies look or sound, in order to narrow down their choices.

Both GIGB and IGEB start from the sensible investment-grade bond universe. GIGB then utilizes accounting data to select its bonds and market-cap-weights them. IGEB constructs its portfolio through its proprietary algorithm that incorporates market data. GIGB's major drawback is its usage of backward-looking accounting data. However, IGEB's black-box-like security-selection and portfolio-construction methods pose risks, including uncertainty around its responses to market stress. IGEB does take more active risk by pursuing its targeted factors more aggressively than GIGB, giving it greater potential to outperform, but also greater downside risk. Investors should be wary of this risk, especially because this strategy has not been proven yet. These considerations lead me to prefer GIGB to IGEB.

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