Lionpoint Executive Director Bill McMahon sits down with LevPro Co-Founder and COO John Porges to discuss data challenges and solutions in the leveraged loan market.
1. In the leveraged loan markets, what types of data challenges are firms faced with and how are they solving them?
John Porges: There are probably dozens of different data challenges in this market, but many stem from one thing, which is a lack of a common identifier. Because of this, loans can be referred to one or many different nicknames that are some derivation of that issuer, tranche name, a combination of the coupon and the maturity, or just something that’s unique about that tranche.
So then to look up a price, traders have to search for multiple iterations of the same name in Bloomberg because dealers may refer to it differently. This has resulted in many a trade error because the two counterparties aren’t on the same page with what they’re trading. This is an extremely common problem in this market.
Then there are challenges caused by a combination of this lack of common identifier and the medium of exchange. Now with hybrid work, more and more investment decisions are made electronically, which creates this huge reliance on searching email as a form of information discovery. Anyone who’s set up this way knows how frustrating it can be to search your email for some specific nugget from a few weeks prior.
Slack and Teams are definitely an improvement for some of those issues. Great advancements but because they’re not built specifically for this market, there’s no intuitive understanding, no named entity recognition or industry specific natural language processing, which means you can’t easily group things by issuer without opting in ahead of time into a channel.
But even if you are, a lot of this investment related information may be coming from external parties via email, via the news, or other internal systems like order management or portfolio management systems. And when one investment is called something different in all of those places there’s this disconnect where, if you want to get to get a true fulsome picture you’re forced to toggle across three or four different applications and do a lot of guess work searching.
Bill McMahon: To piggyback off of that, I would even stretch it into the core data platforms that clients rely on, and how do they stitch that information together? Organizations use platforms like WSO data, Bloomberg data, rating agencies, pricing sources, and coming back to that original theme of common identifier, it’s a fragmented ecosystem where you’ve got loan X on WSO’s side, you might have a CUSIP on the Bloomberg side, and the European side they use LIN’s. It becomes a complex ecosystem to marry together. Oftentimes you’ll have subsidiaries that are trading underneath a parent company, and as we think about the overall credit quality of a particular firm it’s not necessarily the subsidiaries’ financials that we’re interested in.
I want to be able to look up through the parent company and really understand their credit quality. How do I bridge this lineage of different tranches through different firms of various levels? How do I look across the systematic sources to be able to unify and collect that information? The absence of a common denominator is a real roadblock in trying to create a unified data ecosystem. Every firm that we work with relies on this fragmented data landscape. They are reliant on their own capabilities to stitch all of these disparate sources together into a unified view, and that’s created a substantial challenge around data integrity. Data quality is an industry-wide challenge, and everyone is asking “How do I have timely, accurate, and comprehensive data that I can make informed decisions off of?”
Being able to solve that unification problem would go a long way in improving the overall operational and, for that matter, economic performance of a lot of the leverage loan specialists in the market.
2. What challenges are firms facing overlying forward pipeline and hypothetical secondary deals to understand the pro-forma impact to their funds?
BM: From my perspective, a lot of firms are dealing with technology that is really focused on their current book of investments. What names do I hold? What names have I reviewed? This lens is narrower than how portfolio managers are thinking about portfolio construction. The challenge is firms are getting a feed from LCD news that’s telling me, “This is the forward pipeline,” but that’s sitting in an Excel book. I can see indicative characteristics on the new issue coming to market, but I have no way of 1) integrating that into my OMS without going through a proper security master setup 2) overlaying that investment from a portfolio construction perspective to see what the proforma impact is and 3) evaluating the investment impact from a compliance standpoint.
It’s an enormous operational effort to onboard one new primary deal and see what the impact is. That’s not to mention all the secondary names that I can look through and overlay. As we think about the ambition of a lot of firms within a leveraged loan market, it would be to have that unified view of, not only names that I’m invested in, but names that are broadly available in market. Being able to narrow that down to those that I have a positive credit perspective on, and as I think through the capital structure and the tranches that I have price marks on, really being able to track where the market is trading, if we hit a particular price, can I overlay that into my portfolio? What do I have from a new issuance calendar? Throwing that into portfolio. Am I moving capital around to accommodate that? I think the actively managed nature and being able to fluidly move in and out of positions, on a single name or on multiple names, is really the environment people are struggling to achieve. This reinforces that foundational challenge of the fragmented nature of information. Nobody is creating a comprehensive and unified perspective of both active deals in market as well as prospective deals in market.
JP: From a buyside trader’s perspective, the loan new issue process is asinine. Deals come in via banks or via the news and someone in the firm, usually a trader or a trading assistant, is spending an hour or more per day manually putting that information into Excel or a web-based type of grid. The nature of these deals is dynamic so terms change all throughout the syndication process, which is usually a week to 10 days. Pricing changes, timing changes, deal terms discussing what the borrower can do with the collateral, all of this must be tracked by someone at the firm.
The knock on the effects of this are that firms struggle to create the assets in their system, especially when there are last-minute deal changes. There’s lots of email traffic between the people inputting the data and the people creating the assets. So when it’s time to actually trade these names—and for some funny reason banks love allocating deals at 5:00 PM on a Friday when no one is around—the manager can’t book the trade because the asset isn’t in their system yet, so they go through this fire drill of trying to create the assets quickly, oftentimes booking a shell trade with a fraction of the data behind it, and then have to go back days later to clean it up.
And if you can’t properly book the trades, it gets really hard to see the pro forma and hypothetical impact on your funds. Not to mention a huge task for the compliance team to vet that their stated investment processes are actually being followed.
What we see most managers doing is operating this hybrid model, where they have their official books and records in their designated system, but then they really run their day to day, like their hypothetical trading, their allocation methodologies, and all the other analysis in Excel, which then creates this hodgepodge of data that physically sits in an email or a shared drive, is difficult to discover, and poses a massive compliance liability.
3. Given the actively managed nature of leveraged loan funds and CLO’s, how are firms efficiently monitoring for relative value swap opportunities?
JP: I’ll give a real-life example from how I used to operate, and then maybe Bill can expand upon that. Let’s say the PM decides they want to reduce WARF (improve rating quality), they’ll ask an analyst to recommend some swaps. The analyst will then send over some swaps from B3 rated assets into B1s, but the PM is worried about how much par they’ll lose, so she sends it to the trader to price up. They send it back, the PM sees the portfolio price gets crushed by the trades, because they’re going into higher quality, so she tells the analyst to try again.
This is all over email by the way.
From there, the analyst sends a new list that’s mostly B2 recommendations…those go to the PM, who sees the prices are better, but now the spread is crushed, so she sends an email to all the analysts asking for all their best ideas. Then each analyst sends six names but they’re all in different formats, some excel, some email, some Bloomberg and that’s too overwhelming for the PM so they appoint someone to collate. So now a Junior PM comes in and creates a monster spreadsheet of all the scenarios that ends with eight best ideas. They then send that to the trustee who runs the scenario, and four hours later the CLO trustee sends in the results back in a file.
But, surprise! All the data doesn’t match entirely, so that kicks off another email chain to try and fix the data problems, which goes in circles.
So finally the junior PM gets sign off from the PM, inputs the orders into the order management system. The trader then sees these orders and tells the PM that the levels moved, one’s now only quoted and one is 5 points off. The Junior PM is then told to redo the entire exercise without those two illiquid names, so then they revert back and everything gets kicked off again. A couple days later, some trades finally get done!
BM: Everything you just said speaks to the operational challenges and is germane to the idea that the market is moving in such a fluid nature. As you think about, outside of Bloomberg and outside of an analyst’s head, firms don’t have the infrastructure to track all the relative names that they might be interested in, the different price points that they might want to hit on, and all the fundamental credit statistics.
As we think about what is the improvement of credit quality for performing a particular swap, as the market’s moving, how do I actually overlay this and illustrate back to the portfolio manager? What is the impact to the fund? What is the impact from a return perspective? What is the impact to risk; to WARF; to WAL?
That entire exercise right now is a completely manual exercise, because the foundational platforms are focused on names that you hold. So if you’re upsizing or downsizing, maybe you have some capability there, but in practice, there’s a broader universe of investments that firms are interested in. Either at original issuance where you weren’t able to get an allocation, or perhaps you didn’t hit your price point initially and you’re actively monitoring names that are interesting to your firm. How do we find a platform that can encompass all the different price movements, all the different credit quality shifts, all the different spread movements in market, and allow firms to fluently shift in and out of names and create this composite of hypotheticals that we can say, “We’ve upsized or downsized particular names, added new and removed old names , and these are our opportunities to either improve spread, improve WAL, improve WARF“
As we think about the overall collateral quality that these managers are measuring against -to your earlier point- there are some shifts and opportunities around optimization. Some of the trustees and administrators are starting to introduce platforms. Other firms are overlaying “collateral optimization” to optimize spread outcomes. The first challenge with these ambitions is retrieving that holistic source of investments. Do we have all the eligible investments that we potentially might want to invest in both actively held as well as prospective?
Then, as you think about overlaying all the constraints that you have, both from an indenture and IMA perspective, and then from a portfolio management perspective, you introduce a entirely different set of challenges. I’ve heard the term, “Head in the oven, feet in the ice box,” type of construct where you’re overweighed on high quality and low quality assets. The idea being that we want to create a well-constructed curve of credit risk so we’re not overexposed on either of the extremes. As you blend those two philosophies, both from a regimented compliance oversight, as well as from a portfolio construction perspective, there are opportunities for technology to be able to recommend trades and optimal position sizes to maximize spread, given a whole diverse set of constraints.
That’s an emerging technology, but first we have to solve that key data issue. Do we have all the terms and conditions, all the ratings that we require, all the industry alignments, all the relative pricing? All the correlated issuer names, so we can properly manage issuer exposure. These are all data challenges. Only once you’ve solved for that, then you have an opportunity to contemplate optimization. These are common problems that every one of our clients is facing and certainly looking towards the technology market in ways that they can help solve for these.
About John Porges
John Porges spent 12 years as a loan, high yield, and distressed trader on the buyside for CLO Managers and Multi-strategy funds, including two 100+bn AUM platforms. He cofounded LevPro in 2020 to build solutions to operational problems that he and his colleagues faced as traders. As the Chief Operating Officer of LevPro, he specializes in product development, systems implementation and client success with a growing number of leveraged credit institutions both large and small.
About LevPro
LevPro is a modern SaaS operating system for Leveraged Credit Asset Managers founded in 2020 by former traders.
The company helps their clients improve their investment processes with an AI-enhanced, data-first approach to pipeline, order management, research continuity, portfolio management and compliance. The platform can be utilized as a stand-alone operating system or as a front office interface on top of existing systems.
About Bill McMahon
Bill McMahon has in-depth private markets experience across credit, real estate and private equity. He is passionate about working with clients to reimagine the way they handle their reporting and calculations, through automation and workflow optimization. In his role he works across global sales and business development, assisting our multi-geography clients on our range of Lionpoint services.
Prior to this role, Bill led the credit practice, where he designed and delivered technology strategies and solutions for front and middle office institutional asset managers and hedge funds.
About Lionpoint Group
Lionpoint is a leading global consulting firm delivering operations transformation and technology enablement solutions to the alternative investments market.
Lionpoint’s consultants have domain expertise across private equity, real estate, infrastructure, and private debt. Its core services include strategic advisory, operating model optimization, technology roadmap and solution selection, and systems integration to solve the complex operational and technology challenges across the front, middle, and back office.
