A new report on AI provides new and useful context for the many "uniquenesses" of London-based proprietary trading firm, XTX Markets...
Many thanks to Oliver Renick (@OJRenick) and the team @TDANetwork for this one. We had some fun discussing the IBM-RedHat deal, and the implications for the larger cloud infrastructure and AI segments of the enterprise IT arena. Also touched on some thoughts for NVIDIA, AWS, Google and HPE. Based on our recent post, IBM Hides Watson Under a Red Hat. Link to Video here. It's a good one. Enjoy... As always, if you value this work: Like it, share it, comment on it – or discuss amongst your colleagues – and then send us email@example.com. 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…
A charitable assessment might have called it an experiment, really; an experiment to find out if the headwinds on the hardware and infrastructure services side of the house could be mitigated to some meaningful extent by perceived tailwinds on the software side of the house. A less charitable assessment might have called it a gamble... But now, with the (Sunday evening) October 28, 2018 announcement that it will be acquiring open source software leader, Red Hat for an astounding multiple of nearly 12x FY2018 revenue, or $34 billion, IBM signals that the promise of the high-profile experiment / gamble within their software group - relabeled, Cognitive Solutions in 2015, and where "Watson" became the poster child for the recent hyperbolic expectations of artificial intelligence (AI) - was not likely to arrive soon enough to stem the internal bleeding caused by competitive threats within the cloud infrastructure services market. With Red Hat, IBM now turns to defend its 3rd fiddle position in the cloud market (just ahead of Google) - which [...]
Episode 2 of our new video series, Alphacution Riffs, picks up where Episode 1 left off - and begins to describe our research mission, modeling methodology and research workflow. We also begin to lay the foundation for our "T-Greeks" benchmarking framework that focuses on measuring and comparing "return on technology" (RoT) - otherwise known as "technical leverage" - for banks and asset managers. Here, we describe how much of our core research effort is currently built on the basis of just 3 simple data points collected for a model library that currently represents 200 large banks, asset managers, hedge funds, and even certain proprietary trading groups, among others - more than 250+ FSI-related companies in all - and how each of those models covers several years, with many of our core models beginning in 2005. With these 3 data points and our 360-degree modeling strategy, we can move beyond the benefits of various market-sizing exercises to more impactful benchmarking exercises. This tutorial is important for our clients and broader network [...]
In late April 2017, we noticed a new string of dominoes falling at the fast, automated end of the trading spectrum: With Virtu about to gobble up KCG - not to mention additional consolidations of principal trading groups like RGM Advisors (to DRW), Timber Hill (to Two Sigma) and Chopper Trading (to DRW), among others - it seemed pretty clear that one of the next dominos to fall would be in the direct-feed market data space. The question was: To what degree? (See: "Nasdaq Under Virtu Market Data Axe," April 28, 2017) And yet, when we went back to look - via updating our Nasdaq model - this picture showed up: As Paul Harvey used to say: "...And now the rest of the story..." Obviously this trajectory is the opposite of what was expected. Better yet, in a dictionary somewhere is this chart - at least, of late - next to the words, "fairly smooth sailing" or "strong growth." Over the last few years, data products (and the growth in [...]
It's only happened twice since the peak, recorded nearly 6 years ago (at the end of Q3 2011): Alphacution's bulge bank headcount index has recorded a rare uptick, as of the end of Q2 2017 (see Exhibit, below). Now, of course, it may be too soon to sound the trumpets that a major turn has been made for headcount in the global banking sector. The moves - in either direction - are still small. Although, who knows? Maybe the expectation of regulatory rollbacks has got bank hiring managers feeling more exuberant of late. Or, maybe - as we suggested in our prior post - that process automation, particularly among quant shops, actually requires more people is something that applies more broadly in financial services (given the push to implement more AI). One thing is for sure, most of our bulge banking tracking sample (7 of 9) is bigger in terms of headcount than they were more than 10 years ago. Only UBS and Citi are smaller, but that has been [...]
Here's a quick jolt of provocative thought, just in case your brain - like mine - has become a little soft over these summer months: Talk of AI and various other forms of process automation have reached a fever pitch. With that phenomenon comes a flood of new intelligence - and also a heavy dose of mythology. Sometimes the difference between the two is not immediately obvious. The idea that automation has a tendency to kill jobs is one of those if-then statements that is rarely if ever questioned. In the world of trading, quantitative (aka - automated) strategies have earned a reputation for becoming incredibly successful with few employees, thereby supporting the prevailing wisdom. Well, it turns out that "quant shops" just might scale headcount relative to assets under management (AuM) differently than other managers with other trading strategies - and not in a way that is supported by prevailing wisdom... Alphacution just sent a completed draft of its first major asset manager study over to the editor. This [...]