Alphacution has devised a method to arrive at the most highly credible estimate for what any asset manager spends on technology, no matter their level of financial disclosure. And, the implications for that discovery are huge…
Clearly, this is a bold, provocative – if not, entirely ludicrous – claim. And yet, we still make it, out here in the open, with confidence – thanks to our collection of data.
There is a persistent relationship between assets under management (AUM), technology spending, and headcount. The change in these factors is predictable along a continuum of AUM – and repeatable from period to period. With a wink and a doff of the cap to our old pal, Pythagoras, we only need to know two of these factors – AUM and headcount – in order to reliably guess the third – technology spending. (And, sometimes, we only need to know one factor to get in the range…)
Here’s another way to think of it: If one were to build a “context machine” whereby the full possible range of the factors – observable examples of AUM, headcount and technology spending – was captured, then the estimation of unobservable technology spending for any Manager X would take comparatively little effort.
This is precisely what Alphacution has achieved, as of today. And, ongoing expansion of our modeling will provide further growth in contextual power of this framework, thereby improving the detail and accuracy of its estimation capabilities in the future, like the evolution of a map or like the improvement in navigational intelligence, as if migrating from a sextant to GPS.
This is only our first attempt at validating the hypothesis of such factor persistence – which we have achieved. As we move a few more steps to complete the initial “roundtrip” on our mission to develop a 360° constituent model library of the global financial services ecosystem, subsequent modeling (and roundtrips) will serve to enhance the detail and accuracy of what amounts to a complete ecosystem composite model; like the process of solving a massive Sudoku puzzle – which then amounts to a massive ecosystem context machine to be useful for detailed market-sizing of numerous technology spending patterns as well as the benchmarking of some fascinating operational dynamics like processing efficiencies…
With all that as preamble, Alphacution is very proud to announce – along with its content distribution partner, Aite Group – the publication of its latest study:
“The Context Machine: Estimating Asset Manager Technology Spending”
This research is specifically developed for the full spectrum of asset managers (including large hedge funds and prop shops) and their key stakeholders (in particular: solution providers, institutional investors, asset allocators, and advisors).
One more thing before we get into the mechanics of how to acquire this study: If you remain a bit skeptical about our claims, you might be asking yourself right about now – Who cares about technology spending?
Well, this is where the story becomes exponentially more fascinating: Because, it turns out that by understanding the relative positioning of technology capital (via spending patterns) with that of human capital, we achieve a unique and quantitative – if not, unprecedented – level of understanding about the underlying scope of automation embedded in that firm’s strategy selections. This path of analysis leads us to the benchmarking of leaders and laggards in the space on an operational alpha basis (because now we have a method to quantify operational beta, which is a very cool story for another day).
Like donning a new pair of glasses and suddenly “having new eyes,” we are able to peer further inside, like never before, to the operational dynamics – like the gears of an engine – of some of the most secretive, and many not-so-secretive, asset management enterprises in the world. And, once we are in there with this new vision, an abundance of new insights about specific managers, segments of managers (perhaps grouped by strategy), and the landscape as a whole start coming to life…
Consider coming along for the ride, starting with this one…
About the Study
To learn more about this study, there are a couple easy options that are only a click away:
- Set aside some time to watch Alphacution Riffs Ep 4 wherein we walk through the foundational hypothesis; key highlights; an extraordinary case study involving Citadel, Millennium Management, Point72 and Vanguard (teaser); and, the strategy behind the release of this groundbreaking study, The Context Machine…
2) Next, you can easily download a comprehensive executive summary of the study, below.
A link to the Aite Group version of the summary can be downloaded here.
How to Purchase
Terms of Purchase
Terms of purchase are identical from either party, Alphacution or Aite Group.
An enterprise license for this report also includes a credit for the total purchase price which can be used as a discount for Alphacution’s annual enterprise subscription, equivalent customized engagements, or some combination thereof valued at $50,000 or more for a period ending one year after the re-launch date of our content management platform, which is currently estimated to be a period ending 3rd quarter 2019.
Annual Subscription and Upcoming Research
The discount credit on this study purchase is extended into late 2019 because annual enterprise subscriptions to Alphacution’s research archive have been temporarily suspended while we execute on a substantial content management feature upgrade. Clients may still access Alphacution’s core studies via its feature page at Aite Group and/or ad hoc arrangements with Alphacution, including custom engagements.
In the interim, and over the course of the next 12-18 months, the following studies are scheduled on our research roadmap; ultimately becoming accessible via single report purchase, annual subscription, or as an embedded feature of a customized engagement:
- Enterprise IT Solutions and “Big FinTech” Study (Initial): Focus on the largest solution providers related to global financial services, including 20 Enterprise IT firms and 30 “Big FinTech” firms. Key objectives of this study are to compare vendor revenue patterns with technology buyer expense patterns in support of further segmenting tech buyer spending beyond the current status, which is enterprise-level hardware, software and IT human capital (for banks) – and into workflow categories, like front-, middle- and back-office and/or task categories, like research / signal generation, execution, portfolio management, risk, and post-trade processing.
- Global Bank Technology Spending Study (1st Update): Focus on modeling, measuring and benchmarking enterprise technology spending patterns for the largest banks – with segmentation into hardware, software and IT human capital components; expanding Alphacution’s existing bank composite model from 60 to as many as 80 individual bank models (which means modeling further into the retail bank arena); and, beginning to apply the “human capital leverage” concept and the potential to benchmark levels of automation that was first used in the asset manager sector modeling.
- Asset Manager Technology Spending Study (1st Update): Focus on expanding the roster of asset managers and hedge funds from the current US$10 billion AUM minimum threshold down to a US$1 – 5 billion AUM threshold; and, applying the recommendations for further analysis from the initial asset manager technology spending study, each of which relates to the application of additional segmentation factors that have potential to improve the estimation accuracy and benchmarking potential of the existing framework.
- Global Exchange Technology Spending Study (Initial): Focus on modeling, measuring and benchmarking technology spending patterns among a global roster of approximately 25 exchanges, including updates to Alphacution’s existing modeling on market data / non-market data themes.
- Note: Core studies are supported by premium-access episodes of Alphacution Riffs (including video tutorials, select case studies); and, detailed exhibits from related modeling. Furthermore, the modeling components, timing and chronology of the aforementioned studies – as well as features and pricing of annual subscriptions – are contingent upon ongoing client, stakeholder, data availability, new data discovery and other drivers, and therefore, are subject to periodic modification without notice.
Thanks for your attention…
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