LSEG Labs—formerly Refinitiv Labs—has been working on a number of projects. One of them is a pre-trade market impact analysis tool for equity traders.

Geoff Horrell, head of innovation at the London Stock Exchange Group, said that traders choose which venue to use and what trade volume and size to put through when executing a trade. They also need to understand how a particular trade impacts the market so they can determine the cost analysis and performance of the trade.

LSEG Labs has developed a pre-trade market impact analysis model that will show the likely impact trades have on the market based on the stock for any particular volume size and time of day.

“So what the model does pre-trade, is tell you based on the current volume that we’re seeing in the stock historically, what we’re seeing in this particular time, what is the likely impact. And then you can decide your trading strategy accordingly, and you could perhaps change the order size, change the timings, change how you’re structuring, or laddering your trades. From the user point of view, it’s a very helpful piece of information for you to see that likely impact,” he said.

LSEG Labs used the I-Star model—a standard market impact model that estimates the instantaneous trading cost for orders—as a baseline and then introduced a machine learning-based model to predict the market impact.

For that, Horrell said the labs used six months of historical tick data from the S&P 500 and Russell 1000 indexes, as well as data from other markets.

“We did this for a number of markets. And you have all of the usual kinds of things, like market open, market closes, slight strangeness around how the exchanges report particular kinds of trades. You have to unpick all of that to understand and build your model,” he said.

LSEG Labs tried several different techniques but ended up using a neural network for the tool, as it performed best across different markets and conditions. Then the team built a user dashboard that visually shows traders what the model is predicting after consulting customers how they might want to interact with the tool.

LSEG Labs used Amazon’s SageMaker, a cloud-based machine-learning platform, for the heavy lifting when it comes to AI.

Horrell says he anticipates that the tool will be available to users within Refinitiv CodeBook (its cloud-hosted development environment for Python scripting) and Workspace (its next-generation workflow solution) in the first quarter of 2022, as part of a suite of trade performance analytics for equities.

“Rather than being a fixed analytic that a customer can see, it’s completely transparent to them in CodeBook in a Jupyter hub-type environment, and they can actually see the analytics, they can actually add their own features to those analytics. So it’s a more open way of delivering the analytics,” he said.