Since the worldwide financial crisis, credit markets have developed a highly concentrated buy-side structure. Driven largely by regulators, this has limited the flexibility of monetary institutions to offer market liquidity at a critical time. As low rates of interest and central bank bond purchases have boosted corporate bond issuance, liquidity provision facilities are more essential than ever.
As a result, market participants have turned to exchange-traded funds (ETFs) to tap right into a seemingly alternative source of liquidity, making a recent and essential buyer investor. However, as our evaluation shows, this liquidity expectation just isn’t entirely accurate. The high concentration amongst ETF providers – and the resulting replication of ETF algorithms – has concentrated trading pressure on certain bonds, resulting in more volatility and better liquidity costs when ETFs come under selling pressure.
In this context, further questions remain unanswered: for instance, what are the implications for the complete fund management industry, specifically for alpha-oriented lively managers and asset owners who make portfolio construction decisions?
How has the expansion of corporate bond ETFs affected the Alpha Stars?
The increased market share of passive investments has put pricing pressure on the business models of lively managers. In addition to the low costs of ETFs, their scalability poses a direct threat to the biggest lively funds which have dominated this space to this point. In fact, just 10 firms are liable for 38% of actively managed assets.
We compared the chance budgets of lively and passive funds to see how much they devote to alpha generation. As expected, lively funds invested a bigger portion of their risk budgets in alpha generation than their passive counterparts. While this was largely true, the biggest funds – those with over $5 billion in assets under management – didn’t carry more specific risk than ETFs of comparable size.
Active vs. passive funds: Percentage of variance explained by the primary five PCA aspects, broken down by funds’ 2020 assets under management for 2016–2021, monthly data
Universe of lively corporate bond funds with over $50 million in assets under management as of December 31, 2020. Alpha is estimated because the difference in performance between a portfolio of ETF funds and every lively fund within the universe per yr. Replications are based on loadings of every fund’s return regression on PCA aspects calculated on a set of 487 ICE-BofA indices for a similar yr over five years.
Typically, credit selection-driven alpha generation is predicated on identifying mispricing at each instrument level. However, such mispricing opportunities cancel out on average and will not be scalable.
Can lively managers due to this fact adapt their alpha generation capabilities to their scale needs? Is alpha generation scalable in any respect? Robert F. Stambaugh argues that the abilities of lively managers are more likely to result in diminishing returns as they grow: “Their greater skill allows these managers to identify profit opportunities more accurately,” he writes, “but active management leads to larger price corrections overall, thereby shrinking the profits these opportunities provide.”
Active managers in search of alpha in issuer selection at scale will intuitively speed up price discovery to the purpose where their skill returns disappear. If that is true, the race for scale amongst lively managers in response to competition from low-cost ETFs might be counterproductive.
Corporate bond mutual funds: alpha distribution by assets under management in 2020, 2016-2021, monthly data
Universe of lively corporate bond mutual funds with over $50 million in assets under management as of December 31, 2020. Alpha is estimated because the difference in performance between each lively fund within the universe and an ETF portfolio per yr. Replications are based on loadings of every fund’s return regression on PCA aspects calculated on a set of 487 ICE-BofA indices for a similar yr over five years.
Our assessment of how alpha generation has evolved in an outlined universe of corporate bonds over the past five years reflects this conclusion. To confirm Stambaugh, the scalability of the observed alpha generation stays a challenge: the upper the assets under management of a fund, the smaller the dispersion of results when it comes to alpha.
Selection can clearly add value for funds with assets under management of lower than $200 million: the primary quartile of those funds generated greater than 0.75 percent alpha per yr and as much as 2 percent annually over the past five years. However, this shows that higher assets under management reduce the magnitude of possible outcomes: for funds with assets over $5 billion, even first quartile funds generate little greater than 0.5 percent alpha per yr.
Moreover, the dynamics of alpha generation over time show a recurring pattern: the overwhelming majority of funds experience good and bad years in lockstep. For example, 75% of the fund universe we identified underperformed an equivalent ETF-based strategy in 2018, while 75% outperformed the next yr. This is inconsistent with the concept of alpha and suggests that either a standard factor is missing within the ETF sample or that there’s a high correlation between timing and credit selection bets amongst lively managers.
Corporate bond mutual funds: annual alpha distribution, weekly data
Universe of lively corporate bond funds with over $50 million in assets under management as of December 31, 2020. Alpha is estimated because the difference in performance between an ETF portfolio and every lively fund within the universe per yr. Replications are based on loadings of every fund’s weekly return regression on PCA aspects calculated for a set of 487 ICE-BofA indexes in the identical yr.
Identifying the very best alpha generating funds is a difficult task even in the very best of times, but our evaluation suggests that the probability of choosing the correct manager is comparable to a random coin toss, no matter assets under management.
What does this mean for investors?
The increasing complexity of worldwide credit markets brought on by the worldwide financial crisis and exacerbated by the pandemic is giving investors much to take into consideration. Two conclusions stand out. First, the extraordinary competitive pressures on the buy side of the company bond market are highly concentrated for each ETFs and lively management. And while ETFs have increased their market share in credit, this comes at a price for long-term investors: they’re exposed to the identical concentration risk because the indices they track, an increased liquidity premium, and further concentration on the buy side within the race for critical mass.
Second, lively managers, particularly the biggest funds, face significant challenges in generating alpha. They are inclined to be passive when it comes to the chance allocated to bond selection or market timing skills as drivers of performance. This challenge in generating alpha raises questions on the extent to which lively managers can operate at scale in credit markets.
Against this backdrop, quantitatively oriented credit investing would be the only realistic way for lively managers to realize ETF-like scalability. An approach based on principles of maximum diversification can, for instance, expose investors to a broad range of risks and thus additional return drivers through issuer selection, while concurrently controlling these exposures over time. Portfolio construction based on such a quantitative compass may also position a portfolio in the world of credit market risk drivers in a way that is analogous to barbell trading. This could enable a scalable investment process that takes into consideration the large breadth of fixed income markets.
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Photo credit: ©Getty Images / Haitong Yu
