Bonding Time with Solve

And then most recently we launched pricing platform, which, interestingly when we launched that platform, Michael, it was not designed with the regulatory bend in mind, but it was actually to help people basically become more efficient in terms of creation of scales. And then, as you know, most recently the MSRB came out with compliance resource where new issue pricing is a focus as it relates to underwriters and MAs.

And our focus really has been on aggregating as much data as accurately and as real time as possible. So the idea of the platform, so there's always there's three pillars. One being data, the other two being workflow and analytics. And our focus in phase one of SOLVE has really been on data and somewhat on workflow and analytics to support researching and visualization and some of the workflow processes.

But there was also this really unique opportunity for us to be able to bring SOLVE's unique data and capabilities to our existing clients, our then existing clients, which is part of the plan going forward. And then the idea to also where it makes sense, leverage some of the software that we've built and incorporate, again, SOLVE's unique data, but going out into different markets. Something that we've always been just muni focused, and so the strategic plan, as we had talked about it as we were going through the acquisition process, just made so much sense for us and created really, really exciting opportunity.

And in addition to that, there's a crowdsource data product which is anonymized, which provides the larger fixed income ecosystem with more transparency into where bonds are being quote.

Ultimately, I think everyone's dealing with the same challenges. It's incredibly time consuming to figure out what the value of the bond is. That's one side of it. And on the other side of it is between the buy side and the sell side. There's a sense that you're always working with incomplete or stale information. So there's real risk in making good decision.

And really this is where the data comes from. So the key value proposition across the buy side and the sell side is save me time and give me confidence that I'm making good decision.

So on the one hand, the machine is reading chatter and trying to make sense of it and trying to decide whether something is a price or spread or yield, which is not always obvious in the context of the message. Then there are a number of algorithms that are meant to clean up that data and help decide is this really a price or is this really a yield? Is this potentially outside the bounds of what we think is reasonable?

But there's always that trade off where, especially when markets are volatile and the spread between a bit an offer could be quite wide, am I almost hiding too much information just because it appears to be so volatile? So where's that fine line between showing too much but it could be less accurate, or not showing enough, but potentially leaving out valid data. So that's always been a bit of a challenge. And as I mentioned, it becomes a greater challenge during periods of volatility, which we've been dealing with.

But then part of it is, and this is where I think the larger team is coming together to make some more sense of some of the nuances of individual bonds. That's the other challenge with fixed income is there's so many idiosyncrasies between not just asset classes, but if you look at municipals or you look at corporates, there's all sorts of optionality built into bonds. So just knowing what they are will absolutely help these post-processing algorithms throw out bad data and make sure that we don't throw out good data.

Then we have corporates, which are all the different flavors of investment grade, high yield, emerging markets, sovereign, and so on, syndicated bank loans.

Then we have convertible bonds and single-name credit default swaps.

So very diverse grouping of asset classes. And every one has its own nuances. But specifically when you look at uniqueness, this is where a lot of our backgrounds being in fixed income markets really comes in handy. So understanding the nuances of where certain types of products will be quoted in the price domain versus the spread domain or yield domain. So building that intelligence into the machine to make sure that we are collecting as much and as accurate as possible.

Because otherwise there's way too many nuances and it's one solution is really not going to be one size fits all.

And then there's also the aspect of comparables in the market as well because of the inherent illiquidity that's in the muni space and the ability to look at the structural and credit characteristics of a given bond, of a given CUSIP, and be able to identify other comps in the market as well as to enrich the data that's available.

And this is something that exists both in the pricing platform under the DIVER banner right now, but it's also to an extent something that's also available when we think about what SOLVE is doing as well.

So on the sell side, as I mentioned earlier, our focus has really mainly been on providing data and the data is valuable for a number of different use cases. We do have some clients that are utilizing it in the context of best X and some of the validation around some of the valuations that they're receiving from some of their primary providers.

But I think for the most part, our focus has been more on front office price discovery and transparency across the different asset classes that we spoke about.

Gregg, you could probably talk to what the focus has been with some of the DIVER products.

On the regulatory side, Michael, as you noted, we've been very active over the years starting with helping folks with retail time, a trade disclosure through the DIVER Advisor platform. So that's MSRB Rule G-47, and also supervision. We built in a best X tool into that platform, but also into our pricing platform.

And then obviously also through our Underwriter application work to help folks address their obligations under SEC Rule 15c2-12 and their diligence obligation there.

And then most recently, as I mentioned at the outset, one of the unintended regulatory places where we help out is around new issue pricing given the MSRB's compliance resource that came out for Underwriters and MAs. What we historically have done is you never get what I'll call the good housekeeping seal of approval from a regulator, but we've always sought out feedback from the MSRB, from FINRA, from the SEC as we've built and as we've completed platforms that touch on, even tangentially, something from a regulatory perspective with the idea, again, not the expectation that we're going to get the good housekeeping seal of approval, but more so to understand from the regulator if they think we're missing anything.

They know what they're looking for, whether it's an examiner or the rule maker as the case may be. And what we are trying to do is to deliver to the market something that's going to help them meet their regulatory obligation in a very efficient and cost effective way. And so that dialogue with the regulators has always been and will always be important, so long as we are touching anything related to regulation.

And part of what's interesting in this equation is what SOLVE does today and the credibility that SOLVE has in the marketplace, being able to provide this information already to our clients. So it's interesting. It may be something they're looking at from a regulatory standpoint, but, at least with regard to our clients, it's something that they already have access to that information.

So I think from a client's perspective, they're achieving the objective of seeing a lot of the quote data that's available to them in a singular environment where they don't have to piecemeal it together. And, as I mentioned, that that's been the focus of our platform, just make it as easy as possible without changing the way that they're used to doing things.

But the truth is it really has been across the board.

And just, Michael, on your last question, I'll just say one of the things that I have found exciting is, and Eugene mentioned this, is as we've spoken to our legacy clients, existing clients, around the acquisition and what SOLVE does, I think there's a tremendous opportunity for growth on the muni side. I think we seemingly as munis have always seemingly operated a few years behind the rest of the market when it comes to technology and data. So I think a big growth opportunity for bringing what SOLVE offers.

And then as we look ahead, we'll be continuing to do some, I'll call it refinements and enhancements to our pricing platform. So as a module, we just added something called a debt analysis, which is pretty in depth for every algor in the marketplace. We'll be looking at integrating some of the SOLVE quotes data, especially on the secondary pricing tool and the muni trade ticker. So that'll really enrich both of those, not just to have actual trades, but also to have the quotes as well.

And then one of the things that we'll be working on and releasing hopefully late '23 built into the pricing platform as well as another module will be a tool to perform refunding analyses, which hopefully at some point, again, will be meaningful to the market when rates start to turn the other way.

Eugene?

We have a number of strong products in the family now, so some of the legacy Lumesis, legacy Advantage, legacy SOLVE, which have some interesting analytics and workflows. Some are asset class specific or use case specific, so really bringing all of this together into more of a singular offering and doing a best of breeds when it comes to the three pillars: data, workflow, analytics.

So yeah, there's a lot in the immediate as well as long-term pipeline.

And as I mentioned, really across all of our asset classes we've been fortunate enough to work with some amazing customers across the buy side and the sell side and help them achieve the two objectives that we discussed. So save them lots of time and help them make better decisions.