Return on Technology Spending: Can It Be Measured?
Welcome back! Some of you may have caught my blog earlier about improving your operational alpha, and why we truly believe it’s a critical differentiator in today’s world. To recap, one way to describe operational alpha is as the positive impact of technology spending on investment strategy and operational performance. But how do you measure it? To get at the heart of the matter, Eze Software sat down with Paul Rowady, director of research for Alphacution Research Conservatory, to talk about the methodology it developed to measure Return on Technology – a proxy for operational alpha – and how its T-Greeks Framework might apply to your world.
Eze: What is Return on Technology, and why should funds pay attention?
PR: Think of it like this: All firms have two engines of productivity – human capital and technology capital – and they are intimately intertwined. How the latter is utilized by the former will be correlated to some degree with the performance of a diversified enterprise, discrete business segment, or specific workflow. Therefore, what an enterprise spends on technology and what it receives from technology in terms of revenue is going to be indicative of the return on such spending. The difference in these figures yields a measure of technical leverage.
Alphacution was launched specifically to focus on modeling and analysis of technology spending patterns because technology is a permanently critical engine of productivity in the financial world. We noticed that the topic of TCO (total cost of ownership) was going viral – and a ton of lip service was being paid to the awareness of its importance. And yet, it didn’t seem that anyone was stepping up to provide numbers in support of this theme or to quantify its impacts.
Eze: Tell us a little about the T-Greeks framework.
PR: Our first major study focused on technology spending patterns among the largest banks on the logic that these are among the biggest spenders on technology in the financial ecosystem. We started with the simple question: What do firms spend on technology? To answer, we developed individual models for 60 of the largest banks in the world for the 11-year period 2005-2015 so that we could capture impacts on technology spending and strategy from before the GFC. Each of these models is based on publically-available financial and operational data harvested from annual reports and the like. Individual models are then aggregated into a segment composite model which is where we discover prevailing themes.
During the course of modeling banks, we ran into the challenge of how to compare their spending patterns. Due to vast scale differences, we couldn’t use absolute tech spend. Our first major discovery was the use of headcount as a normalizing factor. We defined the difference between revenue per employee (RPE) and TCO per employee (TPE) as the return on technology. Long story short, this simple formula became known as “T-Spread” – and which gave rise to our “T-Greeks Benchmarking Framework.” This framework ultimately allows us to quantify leaders and laggards among a community of peers such as banks, brokers, exchanges, asset managers – essentially all key participant profiles in the ecosystem.
Eze: How do you think the methodology applies to asset managers?
PR: Operational alpha is the state in which your business runs with more efficiency, agility and integrity than your peers thereby incrementally enhancing the possibility of improved performance and an optimized cost structure. And, yet, in no way can we expect to reliably influence operational outcomes if the inputs and impacts are not measured. In the process of cultivating this new mindset, asset managers should also expect positive impacts throughout the organization. Not only for budgeting purposes and support for technology investments decisions, but also for investment strategy performance as well as marketing and fundraising. Having sat in similar seats for many years, I know firsthand that investors love this type of quantitative and methodical approach to operations as well as trading. It’s a win-win.
Eze: You are about to launch a study benchmarking RoT in the asset management industry. Tell us more.
PR: Asset management firms – hedge funds, in particular – are unique in that they are typically private if not secretive enterprises. And so, we can’t rely on the same sources of data we used to model banks, brokers or exchanges, for instance. However, in the absence of a readily available level of transparency, our fallback strategy is first to establish some context. It turns out that a new analytic – assets under management (AUM) per employee – provides reliable context because it is indicative of the nature of one’s trading strategy; and, knowing one’s trading genre yields insights into inherent levels of automation and potential for various levels of technical leverage. Moreover, tracking and benchmarking AUM per employee over time exposes shifts in operational efficiency levels; which is how we expect to develop an initial proxy for measuring operational alpha levels for the asset management community.
We are excited about this approach using AUM per employee. We are currently in the process of refining our asset manager dataset which will be featured in an upcoming study. Furthermore, our expectation is that this type of analysis for the asset management community will serve as a magnet to fuel heightened interest in further analysis and ultimately direct engagement which will allow us to model with increasing detail for the benefit of our clients.
Eze: What should asset managers be thinking about when investing in technology?
PR: For starters, asset managers need to have a much more comprehensive understanding of tech and data budgeting requirements relative to trading strategy design, performance expectations and AUM goals. On the digital stage, asset managers cannot expect to remain competitive for very long without this kind of “navigational intelligence.” Alphacution is focused on promoting awareness of this need – as well as to provide the relevant intelligence to manage it. Today, intuition alone is simply not enough.
Intrigued? Check out more on Alphacution’s research here, or drop Paul a line. With Alphacution’s upcoming focus and forthcoming studies to benchmark tech spend for asset managers, we believe our network will be interested in the analysis and, ultimately, eager to participate in this work. We believe strongly that understanding your current and future technology costs and choosing a platform that can support your long-term growth strategy are crucial factors to making a good technology investment.
We expect to be following Alphacution’s progress closely, so subscribe for updates.
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Jeff serves as president and CEO of Eze Software and has been with the company since 1999. During his tenure he has held a number of leadership and executive positions, including chief operating officer from 2005 to 2012, and prior to that he was chief technology officer. Jeff also served as executive managing director on the executive committee for former parent company ConvergEx Group from 2006 until the formation of Eze Software in 2013.