The growth of applied quantitative analytics within investing and trading is driving an evolution of data provision. The capacity to assess who is engaging in the market around specific events, defined by demographics such as proprietary trading firms, market makers and investors, makes it easier to assess the positions being taken by traders, and therefore the type of liquidity that activity levels indicate.
Stefan Schlamp, head of quantitative analytics at Deutsche Boerse Market Data and Services, talks through the new features that Xetra Flows users can access, such as addressing total notional/quantity traded to calculate the average price and profitability of different market participant groups for specific instruments.
Interview
Dan Barnes Welcome to Trader TV, your insights into institutional trading. I’m Dan Barnes. The growth of quantitative analytics is having an outsized impact on the way traders perform in the market. Joining me today is Stefan Schlamp of Deutsche Börse and we’re going to be discussing exactly how those traders can better apply data to their trading strategies. Stefan, welcome back to the show.
Stefan Schlamp Thank you for having me back.
Dan Barnes So first of all, how has the very rapid growth of quantitative analytics impacted your data offering?
Stefan Schlamp So the capabilities of the typical trading participants on the analytics side has actually increased quite a bit. Hardly aided by modern machine learning and artificial intelligence techniques. So our customers are now much more able to use more data, more subtle signals, which only makes sense when combined with other signals. And as a result, we’ve seen a lot of demand for more data sets, basically anything that’s not in the public data already.
Dan Barnes Are there specific metrics that people are looking for that perhaps weren’t available before or weren’t usable before?
Stefan Schlamp One example is people are interested in who is really doing what, the disintegrated flow. So for example, between market makers, proprietary traders, and brokers agents, who is doing the aggressive buying, the passive selling, who’s trading in the auction, and this kind of opens the door to a whole range of analytics. So for examples, you can check how competitive are different instruments. Maybe one instrument has fewer prop traders than others. You can kind of spot market opportunities. You can look at market trends. It’s really fascinating what you can extract from the data set.
Dan Barnes Now, are there other real-world examples that traders use at the moment to actually generate alpha or to hedge risk that you could give?
Stefan Schlamp So it’s all T+1. But for example, if you saw that one participant group did a lot of buying, and you see possibly that the open interest on the derivative side hasn’t kept up, then you can anticipate a lot of hedging or de-risking activity the next day. You can see how people react to unexpected events, like unfortunate Financial Times articles in the case of Wirecard, insolvency announcements in case of other companies, and then you can kind of see it. who does actually then buy or sell these chairs the next day and the week after. So it’s kind of fascinating.
Dan Barnes Are there any surprising pieces of information that have come out of the data as you’ve delved into it more deeply?
Stefan Schlamp I looked quite in detail a bit at the day of the COVID vaccine announcement. A lot of market turmoil, a lot of activity, the market jumped up like crazy. And so then on the derivative side, for example, you can see that the prop and the market makers, they were heavy sellers in this market. And you can say, look, they were good market makers. They stood their ground. And then when you combine this then with the flows data set on the cash equity side. You can see that these are just hedges, because on the equity side. They were strong buyers. What you saw in the derivatives were the hedges for the cash equity side. So it’s kind of cool, right? It kind of shows how much additional value comes, right, not from a single data set, but combining it with other sources that you have available. So the Xetra flows for every sub-bucket, right. We not only provide the traded quantity, but also the total notional traded, right so that means you can actually calculate the average price, right that for example. market makers bought a particular stock during continuous trading. Overall, you can then also kind of gauge the profitability of different participant groups for different individual instruments, so it’s kind of interesting and partly unexpected.
Dan Barnes Absolutely. And so you mentioned Xetra Flows. What can clients and potential clients expect from XetrA Flows over the next 12 months?
Stefan Schlamp So on the roadmap, next is to make our other data offerings kind of compatible with Xetra flows, right? So that they match in terms of coverage, and censoring criteria, and they should have the same granularity so that these different data sets become interchangeable, right, and that they perfectly complement each other. We hear this criticism that, you know, oh, this stock is in this data set, but not in the other, and that means they kind of lose a lot of value. And we see that in these data sets, there are some network effects. But the set of all data is much more valuable in the sum of the individual parts, and we kind of try to address this by making them uniform and complement each other as best as we can.
Dan Barnes That’s excellent. Stefan, thank you so much. It’s been brilliant.
Stefan Schlamp You’re welcome.
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