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Jun 01, 2020
Distressed loan settlement: Is this the calm before the storm?
2019 was mostly a quiet year for distressed trades booked on ClearPar, our loan settlement solution, with low activity and clients pondering whether the long-awaited turn of the cycle would arrive - not when. But a cycle is a cycle, after all, and by the end of Q4 2019 the LSTA had announced more shift dates than the previous year combined. By early 2020, volumes of distressed loan trades on ClearPar had increased dramatically, leading to a 58% increase in Q1 over the previous quarter, and an 81% increase YOY. Signs of a major market shift, amidst global challenges brought on by the COVID-19 pandemic, were starting to appear.
The evidence tells us a storm is coming, but its size and duration remain to be determined. In 2007, one year before the start of the financial crisis, global allocations on ClearPar totaled 243,000. The market was different then - significantly smaller in notional traded ($663 billion), with far fewer market players and, importantly, fewer legal entities trading and holding leveraged loans. ClearPar itself was in its relative infancy, and the automation and system-to-system integration capabilities that exist today were not in place.
And yet.
Consider 2019, when global allocations settled on ClearPar totaled 1.3 million and settled notional reached $1.3 trillion. The number of entities holding positions that may shift to distressed documents is bound to be significantly higher than in 2007, meaning that the next turn will encompass not only more master trades, but more allocations per trade, and more pressure to manage risk properly.
The question many market participants are asking themselves now is: do we really think we can (or should) do things the way we did them during the last crisis? Are we ready to rely on vast quantities of email, spreadsheets and PDFs of LSTA Distressed Purchase and Sale Agreements (PSAs) as we support increasing volumes?
In a word, no. There is too much risk associated with manual processes, and thankfully, the market has come a long way towards digitization and automation. We now have an integrated, real-time, audited inventory on ClearPar. We now produce PSAs instantly for however many allocations are needed per trade. We now automate the administrative details that populate the PSAs, freeing up precious time for counsel and closers to manage risk and focus on value-add tasks.
Since 2019, when Deutsche Bank used the new ClearPar functionality to settle a distressed trade in just nine days, we have had a chance to stress and improve our system in preparation to support our clients in a growing distressed trading cycle. On live trades, ClearPar has uncovered and corrected inventory discrepancies that our clients were keeping manually; it has generated hundreds of PSAs in minutes; and it has identified exceptions within upstreams and allowed users to resolve them using our clearing methodology.
Perhaps most importantly, as we consider how dispersed our users are in the current environment, counterparties and attorneys interact directly via the platform in a way that is transparent and auditable. Now that our clients are working from home, the value of a centralized system for storing structured data and evidencing workflow actions is even more apparent.
We helped the industry weather the storm that hit a decade ago and we are even better placed to support again today.
To learn more about ClearPar for distressed loan trade settlement, click here.
To discuss this blog post or our ClearPar solution in more detail, please contact: distressedsupport@ihsmarkit.com
S&P Global provides industry-leading data, software and technology platforms and managed services to tackle some of the most difficult challenges in financial markets. We help our customers better understand complicated markets, reduce risk, operate more efficiently and comply with financial regulation.
This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.
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