New Year New Tech New Research

In your new year resolutions, did you pledge to understand more the technology that scares you? Or at least the one that some people (aka analysts like me) claim will replace you? If the answer is “No” and you are working in the field of Investment Research, whether producing, consuming or distributing it, then you may want to read our latest report Start Coding Investment Research: How to Implement MiFID II with Robots and AI.

I get paid to write research on fintech so theoretically I am not the tech scared type though I am the first one to control screen time at home. I know we have more and more competition from free research you can all find at your fingertips on the internet, and from cheaper research that leverages outsourced resources crunching a lot of data, but so far we are keeping up probably because our clients think we provide insight that those competitors do not provide yet.

I know however that we have competitors that have technological platforms that distribute their technology in a more user-friendly way with podcasts and fancy databases, that write their research in a more automated way and that you can consume easily because you pull the information with selective search technology that knows what you want and how much you can pay for it.

So before the holiday season, to make sure we were all going to start this new year with the right information in hand, I did look into what artificial intelligence and robotic process automation tools will be doing to research; not exactly my kind of markets fintech research, but more specifically to Investment Research, those written recommendations about equity or bonds or macroeconomic environments to help the buy side make investments.

The result is very honestly scary and exciting at the same time. These new  technologies are maturing at a time of big regulatory change in Europe, MiFID2 is finally kicking in and that means the unbundling of investment research cost from the execution costs the brokers and banks charge their buy side clients. Some buy side will keep using them and be happy to pay that fee, some clearly will start looking at other solutions that will have to propose a different business model provided by banks or by new market players, based on technology.

In our recent report we do look exactly at that: new business models and live case studies that have already been implemented in investment research production, distribution and consumption. Enjoy.

Being smart with artificial intelligence in capital markets

Artificial Intelligence (AI) is the new buzzword to talk about on the street. Financial institutions need to embrace AI, as we have explained in our January report, or else they risk to lose competitiveness or be coded by the regulators more than they can do it themselves.

I am in NYC next week to share Celent’s view on AI for capital markets. A little preview for your here.

Today we are at a crossroad where data scientists have the computing power, the alternative mind-sets to search and the willingness to look for narrow AI solutions, not the wide AI brain that we should get to in 2030 according to experts. This enables vendors to come up with amazing solutions from Research Scaling with Natural Language Generation to Market Surveillance/Insider Trading with Machine Learning Natural Language Processing or even Virtual Traders via Deep Learning of technical analysis graphics traders look at to take decisions.

The amount of data available is another big driver for the rebirth of AI, and regulators are looking at ways of accessing that data and using it. This is borderline what my colleagues would call RegTech, and it’s coming.

Our Q2 agenda reflects our understanding that you want to know more about AI: we will share ideas on solutions for the buy side, for exchanges and for the sell side. But in the meantime I hope to bring back some cool ideas from the big apple, hopefully also from the secretive quants working in the dark Silicon Alleys.

Most of the vendors I have profiled are specialists’ boutiques, but the cost of such research is however so enormous that generalists are trying to productize their fundamental research for various sectors, from health to homeland security, including financial services in partnership with financial institutions.

This morning I woke up to great news that Microsoft is at the forefront of Deep Learning on voice, imagine what this could bring to Anti-Money Laundering or Insider Trading products.  The other news was that some top quants of Two Sigma just solved an MRI algo to predict heart disease, and I hope other great minds will, as most of them usually do, also give back to society by applying their amazing knowledge to such grand challenges.

Of games played for thrones

As Season 5 of Game of Thrones nears its end, I have come across a number of articles that have been talking about the leadership lessons that we can draw from the TV series (and presumably the books too). The first thing that comes to mind is that our world has enough problems already without having to take pointers from a bunch of people who probably kill each other more often than we have cups of coffee in a day. But then one starts realizing that maybe there is something after all to this fuss about the knowledge to be gained from the GOT. The next thing that comes to mind from there is that if there are leadership lessons to be learned for the world of business in general, surely there must be some for the capital markets too. The leading capital market sellside firms in JP Morgan, Goldman Sachs, Morgan Stanley, Deutsche Bank and so on probably are the parallel of the seven families, along with a couple of the leading buyside firms (the upstarts to the throne?). Daenerys’ dragons can be an example of game-changing technology such as Big Data processing, Cloud services or Machine learning. Similarly, the recent resignations of leading figures at some of these firms can be an example of the blood-letting we are so used to in GOT. If anything, GOT teaches us that merely changing the leadership does not change the direction the kingdom, or the firm in our case, will take. Nor does it change the impact of the environment around us. In the case of the for capital markets, for example, the tough regulatory and economic environment is here to stay. The ‘winter has come’ and we would do well to get used to it. We could presumably go on in this vein, but I am not sure we should draw too many parallels with, or take too many lessons from, a series that makes Greek tragedies look mild by comparison. If the pain on the screen is not enough, we are going to have to live with the very real tragedy of the season ending just when we were getting all warmed up and beginning to benefit from its infinite wisdom.

Market Surveillance issues

As I begin work on the last in the current series of Market Surveillance reports, there are some important points that we can reiterate from the recent research. The first is the all encompassing requirement for surveillance. The recent Deutsche Bank co-CEO resignations have shown the negative impact the benchmark manipulation related sanctions and fines had on not just this bank but the industry as a whole. Similarly, the investigation of a couple of British banks regarding the payments made in the FIFA bribery scandal also shows the need for constant vigilance on part of banking and capital market participants. Firms are embracing the need for holistic surveillance and compliance, which covers not just trading but also related areas such as best execution, cyber-security and AML. Firms that have legacy systems in place might want to continue with several systems, but for the better part, most firms would prefer to have one system that meets most of their requirements. As more advanced technology becomes available, this is becoming more of a reality. Another important aspect is the rising use of machine learning capabilities. Surveillance systems are becoming more advanced, processing both structured and unstructured data, especially through the use of cloud based processing and Big Data technologies. Machine learning takes this to the next level, as it reduces the need for human intervention, and allows for reduction in false positives and negatives. Furthermore, such advanced systems also allow firms to keep tabs with new compliance requirements more efficiently as they can anticipate problem areas based on learning from past experience. Finally, exchanges and sellside have been the main users of market surveillance technology. But increasingly regulators and buyside firms have also started acquiring these systems. For regulators, it makes sense because it allows them to monitor the market independently and reduces their dependence on the exchanges and the sellside for data and analysis. For buyside firms that are playing a more active role in the market, it is important that their trade surveillance is upto scratch, otherwise they are making themselves vulnerable to the same issues that are plaguing sellside firms at the moment.