New research reveals the future of the trading desk

Productivity gains on the trading desk across asset classes are driving increased use of automation, connectivity and information flows. In new research, LSEG and Coalition Greenwich have tracked the goals that traders are targeting and the best routes to achieving them.

In this interview, Kevin McPartland and Quentin Limouzi look at the steps that trading desks can take to boost their own use of these new technologies and techniques in order to improve best execution outcomes. This means skill sets are changing and partners like LSEG are working more closely with clients to help support deployment of data, analytics and trading tools to help them optimise trading workflows.

Dan Barnes: Welcome to Trader TV – your insights into trading for professional investors. I’m Dan Barnes. Trading desks are evolving at an astonishing pace across asset classes and new research by Coalition Greenwich and the London Stock Exchange Group shows exactly how they’re changing and what that means for traders.

Joining me today are Quentin Limouzi of LSEG, and Kevin McPartland of Coalition Greenwich, and we’re going to be discussing the research and what that tells us about trading going forwards.

Kevin, Quentin, welcome to the show.

Kevin McPartland: Thanks for having me.

Quentin Limouzi: Thanks, Dan, great to be here.

Dan Barnes: What are the dynamics that traders are battling against in order to manage trading today?

Kevin McPartland: So we conducted research, Coalition Grenwich and Refinitiv. We went out to almost 250 market participants around the world. We split those respondents between equities, fixed income and FX, so we could understand the different dynamics in each of those markets, because obviously they can be very different markets in some cases.

So some of the key findings; automation remains key. We’ve seen a shift over the last five or so years from a big focus purely on electronic trading at the point of trade, to more of a full end-to-end workflow, including pre-trade, post-trade, risk compliance. So, all big themes there.

Another big one is cloud computing. 92% of those respondents are on a journey towards greater adoption of cloud computing and at some stage in their journey. Some fully there and some are just getting going.

One other interesting finding, which in some way flies in the face of a lot of this sort of innovation and process forward that we’ve seen, is email. Email is still sort of a widely used tool for communication among capital markets professionals. To some level, we obviously see that changing, whether it be for compliance reasons or just for efficiency, but we thought that was an interesting little nugget that we found buried there in the data.

Dan Barnes: That’s really interesting. We’ve heard from Kevin about what the researchers found. How would you describe the dynamics that your clients are trying to manage on trading desks today with those innovations?

Quentin Limouzi: We’ve seen pretty much the same as Kevin was discussing throughout the survey. If I was to summarize it under one umbrella, I would call it productivity. So when it comes to cost pressure, when it comes to automation, even moving from one tech stack to another, productivity is at the heart of what we’re trying to do. And really, what we’re trying to do here is ‘more with less.’

It’s nothing new, but it continues on the trend of cost pressure, doing more with less people. So trading desks constantly evolve, in my mind, from being highly skilled operators that are morphing into engineers; planning, automating the workflow chain, allowing the possibility to concentrate on high risk trades and alpha generation.

Dan Barnes: So Kevin, looking at those dynamics in terms of time and costs and limited resources, how is technology being employed to help manage those?

Kevin McPartland: So a big part of the picture here is data. Data is obviously useful for those trading analytics, but also it’s a big part of automation where you can’t automate a process if the systems that you put in place don’t have access to data to decide what they should do next.

Another big theme that has come up over the last few years is integration. There are very few trading desks, if any, that use a single piece of technology anymore, right? You’re using a variety of systems, usually from a variety of providers, and they all need to talk with one another. If an order comes in from a client on screen one, you need to be able to click it and have it stage of the order on screen two and then go down, perhaps to an exchange on screen three. And that should be seamless. Cutting and pasting doesn’t work anymore. There’s a number of facets at play here. Some of it is the industry that’s made a lot of progress. In other parts there’s just the human component that continues to be difficult to automate and make near perfect, but we’re certainly getting a lot closer.

Quentin Limouzi: There is data. There’s also the mechanics, right? Your portfolio, your order, your execution management systems. If you have multi asset class capabilities, you need to have very strong connectivity to as many liquidity venues as you can find. I would say also derived data and everything that you do with that data to achieve the trading desk goals, produce meaningful pre- and at-trade analytics to feed your automation tools and your trading workflow properly during the day.

So, you know, we’re really well placed at London Stock Exchange Group to offer this to our clients. We have the analytics platform Trade Performance Analytics that gives us real time analytics in pre-trade across asset classes. We’re very strong in the portfolio order and execution management system. You would have seen in the press that we agreed to acquire eToro and we’re expecting to close later this year, subject to regulatory approval. eToro will strengthen our multi-asset, at-trade capabilities, our automation toolkit and our footprint in Asia, so we’re very excited about this.

Dan Barnes: Kevin, turning to you. What effects do these innovations and changes that you’ve reported on in the research make to the skills needed on a trading desk today?

Kevin McPartland: As part of the study, we asked that question, right? What are some of those important skills trading desks are looking for in the next one to three years? The number one answer; data science and data analysis. Understanding how to work with data. How to manipulate data.

If we dig a little bit deeper, we actually see a little bit more of that on the fixed income side than we do in equities and FX. Sometimes more complex products, understanding bonds and derivatives which sort of falls into that world. It’s also a market that is earlier in its evolution of automation, right? So there’s a lot of work that equity and FX markets have already sorted. There has been a lot of talk over the last few years about traders who code going on. Interestingly we did ask that question as well about coding as a skill, and it’s on the list, but a good ways down the list compared to that data skill. So, you know, thinking about a head trader sitting there, writing Python code during the day. Not that common. But even those head traders still very much need to understand how Python can be used and how to look through data, and that seems to be the real trend here.

Dan Barnes: That is really interesting. Quentin, can I ask you, how did those changing skill sets manifest in terms of your interactions with clients and what your clients are trying to achieve?

Quentin Limouzi: We work a lot more together in order to come up with the analytics and the data the clients are trying to get to. We work with them in explaining the models. We work with them in offering the tools for them to change the models to what they need. I think we still need the traders to be fast thinkers, to understand the markets, especially when it comes to more complex asset classes or less liquid products, and for them to have the ability to work under pressure. That’s not going away.

You will always have tail risks. However, orders are just too plentiful in any trade or blotter today to just be reacting. So understanding, deriving, analyzing the data to better the processes and workflow is the name of the game, in my opinion, and that’s not going away.

Dan Barnes: We’ve talked a lot about data and skills needed to manage data. How do you see the use of qualitative vs quantitative information changing in terms of the volumes and the way it’s being used?

Quentin Limouzi: I think quality comes with deriving the right quantity. If you see what I mean? So, you know, you will have qualitative data points that you can use to trade better that will be coming from a quantity of different data sources. Volume traded in the markets may have been an indication of liquidity in the past. I don’t think it’s a good indication of liquidity. It’s a good indication that there’s a lot of trading being done. However, if you look in depth, if you look into the tick by tick data, and if you look closer into that quantity of data, it will result in better data points in order to make decisions based on liquidity fx.

Dan Barnes: That’s been great. Kevin, Quentin, thank you so much.

Quentin Limouzi: Thank you, Dan.

Kevin McPartland: Thanks for having me.

Dan Barnes: I’d like to thank Quentin and Kevin for their insights, and of course, you for watching. To catch up on our other shows ot to subscribe to our newsletter go to TRADERTV.NET.

Published on April 19, 2022

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