These days, making a point – and then creating enough initial magnetism to draw folks’ attention further down into that point-making exercise – has become quite an art form. Would anyone even notice if a piece of writing had already been published under a different image and title? Perhaps someday we will perform that exercise. 😉
In any case, this post completes the natural progression of our tour of the three primary segments of Alphacution’s asset management ecosystem “map” and our attempt to illustrate the overarching driver that is impacting each of them: automation. We started this tour back in July with the highly popular post, When Market Makers Ate Their Own, wherein we showcased how advancements in technical performance by a declining roster of players had led to the current self-destructive extremes of consolidation and concentration within the market-making zone of our map (since relabeled, structural alpha zone).
Following on the heals of that came the post When Hedge Funds Ate Their Own wherein we introduced the hypothesis that asset managers in the neighboring relative-value zone (since relabeled, active management zone), which include most notably “prop shops” and hedge funds, were subject to a more formative stage of the same phenomenon that had impacted market makers.
Here, we extend this hypothesis to the last of those primary segments with an analysis of the impacts of automation on the more traditional – and largest – subset of asset managers who reside in our passive management zone, a picture of which can be found below:
So, to summarize why this is important – as if one is even necessary at this point – the impacts of automation on the full continuum of players in the global asset management industry, and their stakeholders, is enormous.
Returning to the story at hand about how ETFs are “eating up” all the beta, here’s the setup:
Exchange-traded funds (ETFs) may have their critics, but with nearly $3.5 trillion in total value represented by over 1,900 unique funds as of June 2018 (according to the Investment Company Institute – ICI), this segment of the market has grown faster and is now larger than total assets under management (AUM) of hedge funds (which BarclayHedge estimated at nearly $3.0 trillion for Q1 2018). Success is always the sweetest revenge…
Sure, the naysayers point to numerous complexity factors – like variance in replication methods, tracking errors, liquidity issues, exotic-exposure risks, and others – to make their cautionary case and to send up warning flares to novice investors, but the blunt fact of the matter is that the well-designed, cost-efficient ETFs have had a profound impact on the financial landscape.
That the downward trajectory of fees – like we illustrated in, “Those Fees Are No Laughing Matter!” – with competing financial products (like hedge funds and mutual funds) and the dramatic shift in asset allocations toward ETFs are among the most commonly cited attributes of the shifting landscape is obvious to most by now. However, where Alphacution has found another, far less exposed, impact of this phenomenon is in the competitive landscape. Traditional, labor-intensive investment processes that have been clung to by those who decided to avoid the ETF train – or, missed it altogether – have been feeling the squeeze.
Led by Franklin Templeton (FT) – along with the likes of Invesco, T. Rowe Price, AllianceBernstein, and Schroders – many of the largest asset managers in the world have earned a rather painful front-row seat to the success of the ETF phenomenon.
Off the back of developing its initial buy-side technology spending study (based on a dataset comprised of 158 asset managers, hedge funds, market makers and a few proxies), Alphacution produced several case studies. Among those was a piece of research developed to showcase aberrations in headcount relative to AUM.
It turned out that Franklin Templeton exemplified this common dislocation found among other top AUM managers in our sample, including the others mentioned above. Below is an exhibit that ranks all of the managers in our sample by AUM (for year-end 2016) and then compares headcount given that ranking.
Based on this illustration alone, Alphacution’s interpretation would point to the existence of an acute need for process re-engineering. Alphacution believes that this group’s headcount is out of alignment with its AUM because it is executing an investment process that is on its way out: an overly labor-intensive version of bottom-up stock picking. These players are not responding fast enough with workflow innovations, skills-mix shifts, and adoption of new solutions and datasets, putting them at risk of underperforming with respect to operational efficiency, human capital leverage potential, and workflow automation.
In other words, the financial landscape—and financial products—have changed. Fees have become the bogeyman to be slayed. In search of reasonable returns at lower costs, a juggernaut of passive investing has exploded onto the scene. BlackRock (followed by Vanguard) has surfed this wave in unprecedented fashion. AUM growth indices illustrate how this dynamic is impacting those that deploy a traditional investment model and how an overallocation to human capital may be starting to take a toll on asset growth.
Meanwhile, BlackRock has answered the demand for more passive, low-cost investment products. As of the writing of this case study in late 2017, BlackRock is tapping on the glass of the unprecedented US$6 trillion AUM level. This achievement was originally fueled by the acquisition of Barclays Global Investors (BGI) in December 2009, a deal that yielded a 156% increase in AUM to US$3.3 trillion over 2008 as well as 3,500 additional employees. Moreover, this caused a significant and immediate impact on human capital productivity (as measured by assets under management per employee, or “AUM/e”)—a 54% year-over-year improvement (from US$245 million to US$388 million) and one that has been maintained over the seven years ending 2016. With that, BlackRock continues to set the outer extreme of Alphacution’s sample of AUM/e calculations, a significant operational achievement to go along with the outsized AUM and headcount.
And yet, a closer look at productivity measurements in context makes a bolder point: The five players that have overallocated to human capital are getting squeezed, lagging the sample average AUM/e (US$190.6 million) and, more importantly, lagging several of their peers (in this case, Amundi, Legal & General Group, and Natixis Global Asset Management) – see exhibit below. Once again, Franklin Templeton significantly lags the laggards, and BlackRock is indirectly playing a role in exposing a serious flaw in Franklin Templeton’s investment process and business model.
With that backdrop, bringing Alphacution’s framework back into the story underscores another application: the opportunity to enhance strategic, competitive, and sales intelligence via benchmarking. Setting investment performance or fee levels aside for the moment, how would observers otherwise know that these operational vulnerabilities exist? At minimum, our work here validates the idea that there is a quantitatively based goal post toward which to navigate.
Assuming the peer group (AllianceBernstein, Invesco, Schroders, and T. Rowe Price) are operating similar investment strategies and business models, Franklin Templeton at least needs to realign its workflows to generate an AUM/e similar to those players (averaging US$127 million). This move would represent an increase in AUM/e of 44% from Franklin Templeton’s current location in the basement of five-year averages, or US$88 million.
Two paths will lead to this quantified goal (barring a sale of the company): increase AUM by 57% to US$1.15 trillion without net additional headcount or reduce headcount by 36% to (net) 5,800 on the current asset base. Neither of these would be easy to say the least, and neither represents a short-term fix. This kind of change management project would require a five- to 10-year strategic plan that simultaneously reduces overall human capital, shifts skills mix, and increases technical leverage (by adopting various quantitative – or “quantamental” – methods) while maintaining competitive net performance.
In short, though possible, this level of change would be no small feat…
NOTE: This case study was developed in September 2017 based largely on a dataset that ranged from 2005 to 2016. As we prepare for the next version of Alphacution’s asset manager study, and extend our buyside dataset to include more data on more players, the AuM/e analytic for FT has not improved (falling to $73.9 million per employee as of FYE 2018).
As always, if you value this work: Like it, share it, comment on it – or discuss amongst your colleagues – and then send us firstname.lastname@example.org.
As our “feedback loop” becomes more vibrant – given input from clients and other members of our network, especially around new questions to be answered – the value of this work will accelerate.
Don’t be shy…