Not only will this be the last Waters Wrap of 2021, unless there’s a major acquisition or catastrophic market event, it will likely be the last “normal” story that we publish this year. Rather, the next two weeks will feature daily “Best of” features.

Yes, we’re doing these because we are short staffed as most everyone is taking off to see family (or, I don’t know, day drink at the White Horse on Bridge Street). Additionally, many of you are on vacation and aren’t paying as much attention to the world of technology and data as you normally would.

With that said, I hope that these are valuable resources for our subscribers as they aim to highlight the best of our coverage from the past year. So for example, one post will look at some of the most interesting projects that we wrote about that involved machine learning. Another will look at DLT. Still, another will examine Big Tech’s growing influence on Wall Street. And of course, we’re going to have a few that focus on the world of market data.

All told, there will be about eight round-ups. We hope you enjoy. Now let’s get to my imbecilic ideas.

8 for ’22

Below are the eight most important trends facing the capital markets, in my humble opinion. They’re not necessarily ranked in order of importance; rather, I tried to make a list where each trend leads into the next trend so as to show how these things all connect.

Also, in journalism we have a motto: “Show, don’t tell.” This basically means that rather than stating something as fact, provide evidence to show the assertion to be true or valid. Below, I’m going to do a lot of telling and limited showing. The “Best of” write-ups will go a long way to show why I feel the way I do, but this is more to lay out what I think are going to be the most important topics that we’ll write about in 2022. If you’re a subscriber, consider this something of an editorial roadmap.

Finally, if you think I’m wrong or missing something, fire away: anthony.malakian@infopro-digital.com.

* Cloud disruption: Just a few years ago, the idea of putting critical workloads on the public cloud was anathema to the capital markets. Now, for a multitude of reasons, banks, asset managers, and exchanges are embracing the likes of AWS, Google Cloud, Microsoft Azure and IBM. And vendors themselves are adjusting to this new paradigm shift—every new startup is essentially born on the cloud, while incumbents and stalwart giants are having to move legacy platforms to the cloud because end-users want the tools they use delivered as a service or via a managed service. For banks, asset managers, exchanges and vendors, alike, most all new tools/platforms are being built cloud native.

While this trend is hardly new, it’s not as well advanced as some might have you believe. Banks, for example, like to talk a big game, but by some estimates, they’ve only moved 10% of workloads to the cloud, and asset managers are behind that. (And numbers drop precipitously when we talk about mission-critical applications in the cloud.) Meanwhile, both the CME and Nasdaq have announced that they are going to start moving trading technology to the cloud after signing exclusive deals with Google and AWS, respectively. Other major exchanges will likely strike up similar deals. BUT, it will take quite some time before core trading technologies move to the cloud within these marketplaces. The fact is, though, the shift to the cloud is kicking into full steam.

* Big Tech disruption: Which brings us to what we call the Big Tech companies—AWS, Google, Microsoft, IBM (and for shits and giggles, I’ll throw in Oracle and Alibaba). These are the companies supplying the major public cloud environments around the world. Trading firms use them to store vast amounts of data, to process data, and analyze data, while underpinning platforms. (It should be noted: major banks/AMs use a hybrid public-private cloud model…no major firm is entirely public or private.)

In just the last three years, financial firms and vendors have come to rely greatly on these powerhouses. What happens, though, when these companies start to turn their sights on various other pieces of the capital markets, and aren’t just merely cloud environment providers. Think about the way that these companies have completely disrupted things like retail shopping, food delivery, communications. They’ve never really given too much thought to the capital markets (from a true disruption standpoint) because there’s so much bureaucracy and regulatory red tape…but that is changing.

As Big Tech weeds into more and more segments of the capital markets, it will create both new opportunities for certain firms, and will put other companies out of business…just like in retail, food delivery, communications. What happens if these companies start acquiring major tech and market data providers? That hasn’t happened, but that doesn’t mean that it won’t. What if they start developing new tools that help with surveillance, KYC, and certain other middle and back office functions? That is definitely happening, just a bit under the radar.

Some will love Big Tech’s advancement in this space, other vendors will have to merge to stay in business or they go out of business. Either way, Big Tech will be the biggest disruptor facing the capital markets going forward.

* Alternative data disruption: Which brings us to alternative data. On Wall Street, data is power. Those who have the best data structures—from data governance to data sourcing and acquisition to data cleansing to data analysis—will be the ones who find alpha where others have not. They’re the ones who are better able to manage risk in increasingly complex and volatile markets. They’re the ones who will more efficiently be able to fulfill their regulatory reporting needs, which saves money that can be reinvested into other areas of trading to give them more edge. Data is king and alt data is becoming just as important as market data…really, it is market data, just in new form.

While there has always been so-called alternative data—hedge funds used to send junior associates to shopping mall parking lots to count the number of cars and people going in and out—cloud and the increasing use of APIs have allowed buy-side firms to acquire and analyze ever-increasing amounts of data to find correlations between an endless number of factors. So the amount of alternative data providers is both expanding, but also leading to significant M&A (more on that in a minute). Just look at ESG…while it’s been proven academically that portfolios that consider ESG factors outperform non-ESG portfolios, there’s so much data out there that it’s tough to connect dots to find materiality among the various ESG factors.

Chief data and information officers, market data execs, risk managers, compliance officers, data analysts and data scientists (and that’s the short list) are all on the hunt for new and better datasets outside the traditional world of market data. It’s no longer the proverbial rainmaker portfolio manager that makes all the money…it’s the engineers who know how to build models to manipulate alternative data and blend it with market data (as mentioned previously). Risk managers aren’t looking at spreadsheets; rather, machines are ripping through a sea of data to spot potential areas of risk and alert these managers to anomalies.

* AI disruption: Which brings us to artificial intelligence, and more specifically, the disciplines of machine learning (ML) and natural language processing (NLP). While they have been around for a long time, thanks, in part, to open-source tools and—ONCE AGAIN!—cloud delivery systems, ML and NLP tools have become more democratized, and more engineers/programmers are graduating every day with a better understanding of have to develop these algorithms and models.

Cloud allows you to more cheaply store massive amounts of data, and it has the processing power to allow firms to quickly crunch and analyze that data. And, thanks to an explosion of data (alt data), there are plenty more datasets to be analyzed and correlated. APIs and new data delivery mechanisms make it easier to collect and disseminate information. And firms want more and more data for every single piece of the investment process.

Well, it’s ML and NLP tools that allow firms to find value in all this information. But, there’s a massive talent gap when it comes to finding engineers and data scientists with these skills. There are also risks when it comes to letting these algorithms loose on trading systems (those so-called black box problems). And that fact has led to regulatory concerns around the use of these tools, especially as many forms of ML are essentially black boxes.

AI has been around for a long time, so it’s not quite right to say that it’s “early days” when it comes to AI adoption. Rather, I think that AI’s potential is so great, that it will always be early days when it comes to AI adoption on Wall Street. Just as humans evolve, so too will the machines—only machines will evolve much faster, so it will always seem like early days.

* Interoperability disruption: Which brings us to interoperability. Largely gone are the days of monolithic systems (again…broad strokes, folks). You used to have IBM for your mainframes, the Bloomberg Terminal, the Thomson Reuters market data platform, and some other bits and bobs. Today, because of cloud (are you sensing a theme here?) as well as increased use of APIs and the explosion of data, end-users no longer want closed-off systems. This is also because firms are more diverse in their investment strategies than they were pre-2008, both from an asset-class coverage perspective and from a geographical perspective. So you need systems that can seamlessly talk to one another for cost efficiency, to manage and identify risk, and to improve the investment process.

While it’s a Venn diagram with overlap, there are essentially three forms of interoperability (as I see it) in the cap markets. First is what the likes of OpenFin, Cosaic, and Glue42 are doing, where they use the FDC3 standard to build containers/browsers where any application that sits in that container/browser can communicate data to other apps in real time. Second is what the likes of BlackRock (Aladdin), Goldman Sachs (Marquee), or State Street (Alpha) are doing, where they use APIs and partnerships with other data vendors and application providers to create something of a one-stop-shop for users across multiple asset classes. The third is what the likes of SS&C, Ion, Broadridge or FIS do, which is buy up a lot of companies and try to stitch them together.

But if data is king, you need systems to be able to talk to one another. It’s what made the Bloomberg Terminal so dominant—it both owned the data and it contextualized the data in one place. Others are trying to create something similar using open standards and APIs, because a “Bloomberg Killer” is a myth.

* Market data disruption: Which brings us to our bread and butter—market data. Consider this: exchanges care more today about the data that goes through their platforms than the trading itself, as market data is more valuable than trading fees and services. In the US, there’s a major fight brewing over market data fees. In the EU, there’s a major fight brewing over the implementation of a consolidated tape.

Furthermore, there are new forms of market data delivery platforms. New ways of delivering data (see: APIs). New ways that algos are being used to sort/analyze market data. New ways of discovering new datasets. New ways of procuring data. New methods of auditing/charging for market data usage.

I’m not going to get into the market data sphere too much here, simply because I don’t want to cannibalize some of the “Best of” posts we have, and also because this column has gone long. But trust me, in 2022, we’ll continue to be the leading publication when it comes to covering the market data industry.

* M&A disruption: In the market data space, we’ve seen mergers by/of some of the largest players on through to the middle-market providers. In the order and execution management space, same thing. In the ESG space and wider alt data space, same thing. In the regtech space, same thing. M&A is massively disruptive and by most estimates, we will see some major deals in 2022.

Again, not a groundbreaking trend, I know. BUT, what will be interesting is which companies are involved in M&A in 2022. FactSet, MSCI? Big Tech companies? More Ion and SS&C? Get your popcorn ready.

* Innovation disruption: Which, finally, brings me to the challenge of innovation discovery. This is a subject I spoke with Bill Murphy about recently, but CTOs and senior level technologists are being faced with a fundamental challenge—crisis, really: The pace of technological change increases every single year, and they’re getting overwhelmed.

Just five years ago, public cloud wasn’t really a thing on Wall Street…now it’s becoming the thing on Wall Street. Every year the Big Tech providers release a slew of new tools around cloud, AI, big data analytics, cybersecurity and so much more. And then there are the fintech startups that are popping up all the time because 1) cloud (SHOCKER, I KNOW!) makes it easier to deliver software and to test new tools and datasets, and 2) venture capital firms are flooding the market with $$$ looking for the next unicorn.

Cryptocurrencies and their underlying distributed ledger technology (i.e. blockchain) were something that CTOs didn’t have to worry about in 2014…just a couple of years later every CEO and board member were asking their CTOs about this new tool.

While the terms low-code and no-code development have been around for a while, it wasn’t until just last year that these started to become the hot new buzzwords in the capital markets.

Quantum computing is going to fundamentally change everything we know about data and data analysis, and information security.

Every day, financial services firms are under threat of cyber-attacks and new tools are continuously coming to market to help them combat this scourge.

Every day, advancements are being made in the various fields of AI.

And in January 2020, everyone worked in an office and few used Teams or Slack, and then…

The image accompanying this article is “Happy New Year 1917” by Max Beckmann, courtesy of the Cleveland Museum of Art’s open-access program.