Did Corporate Bond ETFs Kill the “Alpha Stars”?

Credit markets have developed a highly concentrated buying structure since the Global Financial Crisis (GFC). Driven largely by regulators, this has limited the ability of financial institutions to provide liquidity to the market at a critical time. As low interest rates and central bank bond buying have inflated corporate bond issuance, facilities to provide liquidity are more important than ever.

As a result, market participants have turned to exchange-traded funds (ETFs) to access a seemingly alternative source of liquidity, creating a new and important buyout investor as a result. However, as our analysis shows, this liquidity expectation is not entirely accurate. High concentration among ETF providers, and the resulting replication of ETF algorithms, has focused trading pressure on specific bonds, creating more volatility and higher liquidity costs when ETFs face selling pressure .

Within this context, other questions remain: for example, what are the implications for the wider fund management industry, particularly active managers seeking alpha and asset owners considering portfolio construction decisions? ?

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How has corporate bond ETF growth affected “Alpha Stars”?

The increasing market share of passive investing has put pricing pressure on the business models of active managers. Beyond the low-cost nature of the ETF, the scalability of the ETF is a direct threat to the larger active funds that have dominated the space. In fact, just 10 firms account for 38% of assets under active management (AUM).

We compared the risk budgets of active and passive funds to see how much they devoted to alpha generation. As expected, active funds directed more of their risk budgets to generating alpha than their passive counterparts. However, while this was true, the largest funds, those with more than $5 billion in AUM, had no more specific risk than comparably sized ETFs.


Active funds vs. liabilities: percentage of variation explained by top five PCA factors divided by 2020 AUM of funds for 2016-2021, monthly data

Source: Bloomberg, ICE
Universe of active corporate bond mutual funds with AUM greater than $50 million as of December 31, 2020. Alpha is estimated to be the difference in performance between a portfolio of ETF funds and each active fund in the universe every year. Replications are based on the loadings of each fund’s return regression on PCA factors calculated on a set of 487 ICE-BofA indices in the same year over five years.

Typically, alpha generation based on credit selection relies on identifying mispricing at each instrument level. However, these mispriced opportunities cancel out on average and are not scalable.

So can active managers tailor their alpha generation skills to their need for scale? Is alpha generation scalable? Robert F. Stambaugh argues that the skills of active managers are likely to produce diminishing returns with scale: “Higher skill allows these managers to identify profit opportunities more accurately,” he writes, “but active management overall corrects more prices, reducing profits these opportunities offer”.

Intuitively, active managers who strive for the selection of alpha emitters at scale will accelerate price discovery to the point where their skill returns disappear. If this is correct, the race for scale among active managers in response to competition from low-cost ETFs may be self-defeating.


Corporate Bond Mutual Funds: Alpha Distribution Divided by AUM 2020, 2016–2021, Monthly Data

Corporate Bond Mutual Funds: Alpha Distribution Divided by AUM 2020, 2016–2021, Monthly Data
Sources: Bloomberg, ICE
Universe of active corporate bond mutual funds with AUM greater than $50 million as of December 31, 2020. Alpha is estimated to be the difference in performance between each active fund in the universe and an ETF portfolio every year. Replications are based on the loadings of each fund’s return regression on PCA factors calculated on a set of 487 ICE-BofA indices in the same year over five years.

Our assessment of how alpha generation has evolved in a defined corporate bond universe over the past five years reflects this conclusion. To echo Stambaugh, the scalability of the observed alpha generation remains a challenge: the larger a fund’s AUM, the smaller the dispersion of results in terms of alpha.

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Selection can clearly add value for funds below $200 million in AUM: the top quartile of these funds generated more than 0.75% annual alpha and up to 2% annually over the past five years . However, this shows that higher AUM reduced the magnitude of potential results: in funds with more than $5 billion in AUM, even the top quartile funds barely provide more than 0.5% of the alpha every year.

Moreover, the dynamics of alpha generation over time shows a recurring pattern: the vast majority of funds record good and bad years at the same time. For example: 75% of our identified fund universe underperformed an equivalent ETF-based strategy in 2018, while 75% outperformed the following year. This is inconsistent with the concept of alpha and suggests either a common factor is missing in the ETF sample or a high correlation between time and credit selection bets among active managers.


Corporate Bond Mutual Funds: Annual Alpha Distribution, Weekly Data

Source: Bloomberg, ICE
Universe of active corporate bond mutual funds with AUM greater than $50 million as of December 31, 2020. Alpha is estimated as the difference in performance between an ETF portfolio and each active fund in the universe every year. Replications are based on the loadings of each fund’s weekly return regression on PCA factors calculated on a set of 487 ICE-BofA indices over the same year.

Identifying the funds with the best alpha generation skills is a difficult job at the best of times, but our analysis suggests that whatever the AUM, the probability of selecting the right manager is comparable to a random coin toss .

Announcement for ETFs and systemic risks

What does this mean for investors?

The increased complexity of global credit markets brought on by the GFC and exacerbated by the pandemic leaves investors with a lot to consider. Two conclusions stand out. First, intense competitive pressure on the buy side of the corporate bond market is highly concentrated for both ETFs and active management. And while ETFs have increased their market share in the credit space, this comes at a cost to long-term investors: they face the same concentration risk as the indexes they track, a higher liquidity premium large and a greater concentration of the buy side in the race. to reach critical mass.

Second, active managers, particularly larger funds, face major challenges in delivering alpha. They show a convergence towards the passive in terms of risk assigned to bond selection or market-timing skills as performance drivers. This alpha delivery challenge raises questions about how well active managers can operate in credit markets at scale.

With this in mind, quantitatively driven credit investing may be the only realistic way for active managers to achieve ETF-like scalability. An approach based on maximum diversification principles, for example, can expose investors to a broad set of risks and thus excess return generators through issuer selection while controlling these exposures over time . Portfolio construction based on this quantitative compass can also position a portfolio similarly to bar trading in the credit market risk factor space. This could enable a scalable investment process that addresses the formidable breadth of fixed income markets.

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All posts are the opinion of the author. Therefore, they should not be construed as investment advice, nor do the views expressed necessarily reflect the views of the CFA Institute or the author’s employer.

Image credit: ©Getty Images / Haitong Yu


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Axel Cabrol, CFA

Axel Cabrol joined TOBAM in June 2016 as a credit portfolio manager at Butler Investment Advisory, where he co-managed the WB Opportunities Fund, a long-short credit fund invested in European high-yield corporate bonds. Before that, he spent two years at Barep AM, managing the Barep Global Credit Fund in the same team of four portfolio managers. From 2003 to 2005, he traded European government bonds at Caisse des Depots (CDC) and from 2005 IG bonds for one year. Cabrol graduated in 1999 from ENSAE (the leading French engineering school) with a major in statistics, actuarial studies, finance and artificial intelligence and holds a postgraduate degree in statistics (with distinction) from the Université Pierre et Marie Curie, Paris VI.

Tatjana Puhan, PhD

Tatjana Puhan, PhD, is responsible for investment management activities at TOBAM, overseeing the research and portfolio management teams. He joined TOBAM from Swiss Life Asset Managers where he was Head of Equity and Asset Allocation for Third Party Asset Management. In this role, he was responsible for some of the company’s flagship strategies, notably its investment solutions that use systematic and proprietary quantitative approaches, as well as contributing to asset allocation and equity research initiatives from Swiss Life Asset Managers. Dr. Puhan has over 15 years of investment experience, having worked at several leading asset management and private banking firms, while bringing a strong academic and research background. Dr. Puhan holds a master’s degree in finance and business administration from the University of Hamburg and a PhD in finance from the Swiss Institute of Finance at the University of Zurich, with research fellow appointments at the University of Zurich, Kellogg Business School (Northwestern University). ), and the University of Hamburg. She is a professor of finance at the University of Mannheim and an associate researcher at the Center for Financial Research in Hamburg.

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