We recently sat down with Stephane Galzin, Microstructure Researcher, who is using our Market Insights Solution to support his research on market fragmentation. In this video series, we explore different use cases in which data is used to understand the Canadian equity markets. We’ve summarized and cut the clips below so you could get a high-level overview of the conversation we had with him.
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Stephane and other researchers investigate how the regulatory and mechanical aspects of markets affect the way stock prices vary. Microstructure as a research field has been around for decades but has heavily changed in the past decade due to a combination of regulatory changes (regNMS in the U.S, MiFID in Europe), and a leap in information technology. Perhaps one of the most noticeable impacts is the fragmentation of market share between competing venues.
National Best Bid-Offer
Stephane Galzin starts off the discussion around his research by diving into the explanation of time spent at NBBO (National Best Bid-Offer), a metric that consolidates all the venues in Canada so institutional investors can decide where to route orders in order to get the best quotes at any point in time. For his research, Stephane was inspired by a publication from National Bank of Canada which looked at how effective alternative venues were at quoting the NBBO. And to start addressing that, Stephane started looking at the number of symbols quoted at the NBBO at least 50% of the trading day.
Stephane highlights that when talking about fragmentation it’s crucial to understand how the information is being aggregated, especially when faced with stocks originating from different listings. What Stephane points out is that since this NBBO metric averaged across multiple days but also across several stocks, there is information on the listing venues that may be lost in the mix. For instance, TSX and TSX-V have vastly different types of stocks traded on them so he questions whether using equally-weighted averages is sensible when dealing with such a contrast.
Trading Cost Approach
In this part of the discussion, Stephane starts off by discussing the value-weighting metric and how it impacts the different tickers from various listings.
He explains that when a market is not at the NBBO, using it to trade might mean higher trading costs. Every share traded further away from the best quotes has an implicit cost proportional to the traded volume. From a cost perspective, it makes sense to associate the dollar value to these missed opportunities when a venue does not quote the NBBO. In the present case, we are aggregating daily data for each symbol by using an arithmetic average.
Market Depth Approach
Here, Stephane dives in deeper with a new metric, “distance to NBBO.” He explains that this metric essentially takes the best spread, as one number, and looks at how far the spread is from one specific venue at a specific point in time. If it’s not exactly the best but very close, then it’s safe to say it’s close enough so that it’s not very costly.
He explains that what is surprising about his results when having applied this metric is how much higher all the venue listing charts go up, even though the modified metric is only relaxing the criteria for counting stock as being represented by a small margin.
Market Depth Approach
Stephane then provides a quick demo of an application that estimates, pre-trade, the market impact of an institutional trade by using analytics for tickers traded across all exchanges and ATSes in Canada.
Stephane continues the conversation by showing us how charting in the Canadian markets changes over time and delves into a particularly interesting correlation between competition and the market turbulence which was observed during March 2020, when the Coronavirus pandemic hit North-American markets.
Stephane leaves us with this: market fragmentation comes with a high price-tag due to the extra complexity it involves; it is therefore, worthy to try to assess its benefits. We discussed why industry knowledge is a key aspect to understand what assumptions we are willing to make when trying to assess fragmentation. It helps us frame the context of an analysis, and determine whether this context is enough or adequate for our own use-cases. While we argue that a trading-costs approach to looking at best quote metrics is achieved by using value-weighting as well as a flexible “distance to NBBO” approach, we find that alternative venues seem to not play as big a role to absorb liquidity when markets are agitated.
If you’d like to discuss interpretations as to why competition (as measured by this specific analytic) is lower during the COVID-19 induced market turmoil, you can send us an email at firstname.lastname@example.org and we can sign you up for any upcoming webinars around the topic.
In the meantime, if you would like a more comprehensive view of everything outlined here, you can download Stephane’s whitepaper. The whitepaper looks at the gritty details of market structures and takes a step back to get a bird’s eye view of their evolution. The extrapolation it offers in the process is what we call market outsights; a forward-looking perspective of what the future holds in terms of liquidity and innovation.
Alternatively, if you are interested in the Market Insights Solution, we can give you access to the portal and underlying data back to January 2016, for a simple subscription fee. If you would like to use the portal to see if the product is the right fit for your team, fill out the form below and we’ll be happy to provide you with a demo and a 2-week free trial.