PIERCE LORD:There’s a lot of data that is available. I like to think about it in terms of different dimensions. The first dimension is the timeliness of that data. In a slow-moving market, data that is a few hours behind might be appropriate. In a fast-moving market, that may not be the case. The second dimension is the timeliness of being able to test your sources. At ICE, we have both a data science team and a quant team. They work together to analyze different market data sources over time. We try to make sure the data we are using is the most accurate. If it is a question of what those tests look like versus executions, for example, it is really an after-the-fact analysis whereby we look at all the bids and offers from a market data source and compare them to the trades that actually happen. In some asset classes, that proves to be difficult. In Corporates, we have much more visible traits to see, but in other areas we do not so it becomes a little tricky. Analyzing these data points versus executions is key, and as a second best, but also very valuable, is comparing the sources to each other. If you have two sources that are contributing on an overlapping set of bonds, you can do a time series from two sources and see if one of them has a lead-lag profile. You are going to use the source that is leading and not the one that is lagging. Using statistics to test these sources is crucial and that is how we approach it at ICE. It is something that is an ongoing process; just because the source was good doesn’t mean it is always good, so you want to be smart about testing those sources.

EUGENE GRINBERG: We have a lot of interesting conversations with clients, specifically around which data they should be utilizing, and which data is more indicative and actionable. Along with the actual levels, we try to capture as much context around the quote as we possibly can. Things like sizes—where clients will look at a quote with a size is something that is a lot more actionable and less indicative around the BWIC process, capturing not just price talk but which bonds actually trade. That goes a long way to determining the same hierarchy that you guys have spoken about.

PIERCE LORD: The tool you showed earlier about how you break the sources out into different tiers—that is important too, knowing the size and the breadth of the market-making that’s happening with some of the various sources. It is valuable that you guys offer that kind of data to your clients.

EUGENE GRINBERG: In some cases, you’re getting five quotes from the same tier one, and in other cases, you’re getting five quotes from five different tier ones. Even though we anonymize the source, we do have a superscript—what we call a tier designation. You’ll see a tier 1a, tier 1b, tier 1c that tells you there is depth to the market—that it is not the same tier one that is updating their levels, that there’s multiple dealers that are quoting the bond and that gives you more confidence in the level as well.

RAJAN CHIARI: You mentioned the size of transaction called lot size. That holds true to another administrative action from the SEC taken a year ago that focused on odd lots and doing some contemporary study continuously to validate the things that you have chosen. In that case, it was: do I apply a discount for an odd lot or not? What hierarchy of data do I end up using? What is more reliable? It is much better if you take a period of time to study these things. If you are in the 40 act world, you do have to make decisions sometimes about whether you have a good price today or not but if you’re thinking about sources of data and reliability, being able to study it over a period of time is much more valuable, and again much more persuasive to explain to anybody whether it be an accountant, a regulator, a board, an investor, whoever might want to really understand the process that you go through.

EUGENE GRINBERG: Size is important to distinguish between a potential retail trade versus an institutional trade which comes into play quite a bit in the Muni space. If you look at MSRB data, sometimes you’ll see two trades that are off by 10-15 points, and when you look at the size, it becomes obvious what’s happening. In the absence of institutional MSRB levels, this is where our data becomes key: when you could source additional observable data even though not all of it materializes in a trade.

Listen to the recording starting at minute 12:38  of the Valuations Webinar

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