New Trends Impacting the Credit Markets

1. Portfolio Trading: A Sum Greater than its Parts

The straightforward logic of packaging several diversified securities into a single portfolio to offset risk, streamline trade processing and find new sources of liquidity is not new. The approach, often referred to as program trading, is a staple of equities trading where packaging baskets of stocks to assert a particular position is common practice. Until recently, though, the strategy was not often used in credit markets.

That has largely been a function of the fact that credit markets primarily trade risk of any significant size manually over the phone and instant messaging apps. If market participants working in this environment want to exchange baskets of multiple bonds, they need to assemble spreadsheets containing all of the bonds and prices they wish to trade and then e-mail back and forth with liquidity providers until they can agree on a price for each individual bond. The whole process typically takes up to a full day for a single trade.

The growth of electronic credit trading has made it possible to automate the most labor-intensive parts of this process. Starting in January of 2019, when Tradeweb became the first trading platform to offer electronic portfolio trading for corporate bonds, market participants were able to package multiple bonds into a single basket of buys and sells, negotiate a portfolio level price, and execute the trade in a single electronic transaction. This makes it easier to quickly execute large trades and—importantly—to move securities in bulk, creating a single risk profile and quietly moving a combination of highly liquid and less liquid securities in a single trade.

Portfolio trading has caught on quickly since. In the first six months of 2019, more than $14 billion in credit portfolio trades were executed on the platform. But that was nothing compared to what we happened in 2020.

In January of 2020, Tradeweb saw more portfolio trading volume on its credit platform than in the first five months of 2019 combined. It only grew, ultimately playing a key role in helping the buy-side source new liquidity during the COVID-19 crisis. Setting new records in May and June at the height of the crisis and again in October and November, portfolio trading became a key tool for institutional investors seeking to navigate volatile markets. When all was said and done, a total of $146 billion in portfolio credit trades were facilitated on Tradeweb in 2020.

In the first half of 2021, $147 billion in portfolio credit trades have already been executed on Tradeweb, further affirming that the electronic trading trends of 2020 were not an anomaly, but rather part of an industry-wide trend toward more efficient modes of trading.

2. Breaking Down Silos to Expand the Liquidity Pool

Another major trend has been the steady expansion of liquidity providers participating in institutional credit markets. Credit trading used to be more siloed. Buy-side institutions sought access to sell-side dealer liquidity. Wholesale dealers worked only with other dealers. And retail asset managers played in their own, separate sandbox.

That’s largely an artifact of relationship-driven model of credit trading that has been the norm in the industry since its inception. While relationships are still a critical part of the credit trading workflow – whether trading electronically or over the phone—the ability to scale beyond those core relationships can be an asset, particularly in volatile markets. Through periods of heightened volatility, March 2020 being the obvious example, there are some significant advantages to expanding the liquidity pool, to create as many trading options as possible.

Electronic trading protocols have made it possible to safely tap new sources of liquidity, creating an all-inclusive trading network that allows traders to maximize their reach and tailor their trading strategies based on which counterparties they want to target for each particular trade. For example, a buy-side institutional trader executing a complex transaction or a dealer looking to move un-matched orders may choose to work with a select group of known counterparties, based on existing relationships, or send them out as automated request for quote (RFQ) trades on an anonymous all-to-all platform—reaching the widest possible universe of potential buyers.

During the peak volatility of the COVID-19 crisis, both strategies became important for traders looking to find the best approach for each trade. In March and April of 2020, anonymous all-to-all credit trading remained high Tradeweb as market participants cast a wide net. However, during that same period, there was also a noticeable increase in attributed trading, wherein the parties to the trade are limited and disclosed.

Ultimately, what emerged during the pandemic—and has continued into a trend in 2021—is a move toward more customization of electronic trading strategies, with traders deploying a mix of anonymous, all-to-all blanket strategies and more targeted, relationship-driven strategies. The glue that makes both possible, of course, is the flexible electronic trading platform.

3. Streamlined Workflows Across Trading Desks

The closer collaboration between previously siloed areas of the institutional markets goes deeper than just finding more counterparties. It has also extended to many of the administrative aspects of the trading workflow.

Take the process of Treasury spotting for example: U.S. investors of investment grade debt have long priced bonds at a spread versus the corresponding U.S. Treasury bond. To hedge interest rate risk on their credit trades, sell-side participants execute a Treasury trade with appropriate size to offset the risk. The price executed on the Treasury trade, along with the agreed yield spread to the treasury, are then used to compute the final execution price traded—commonly known as spotting the corporate trade.

Historically, Treasury spotting has been a manual (and relatively tedious) process. Traditionally conducted over the phone, it requires the credit salesperson to get an up-to-date spot level from their Treasury desk, and relay that information back to the client who would then agree to and execute the trade. Among the many challenges involved with this approach are uncertainty around the quality of pricing, the time necessary to execute the hedge by phone, the possibility for late or incorrect trade reporting and related trading costs. These, of course, are all amplified in highly volatile markets in which the world is suddenly changing by the minute.

Thanks to innovations in electronic trading, the process has been streamlined dramatically. In fact, the seamless spotting functionality originally first introduced by Tradeweb five years ago, links a firm’s trading in the institutional credit market to the U.S. Treasury marketplace all within the Tradeweb platform, replacing the cumbersome process of manually spotting spread trades with one that enables credit trades to be hedged automatically based on real-time Treasury pricing on the Tradeweb platform. More recently they took it one step further to net hedging activity across all Tradeweb clients spotting at the same time with Multi-Client Net Spotting, further reducing interest rate risk and improving the accuracy of spot pricing based on the collective volume of all market participants.

Over the course of 2020, $326 billion in credit trades were submitted for Net Spotting on the Tradeweb platform—streamlining a cumbersome manual process that used to cause delays of several minutes or more while allowing credit traders to keep their focus on the markets. Through June of 2021, $227 billion in credit trades were submitted for Net Spotting, proving the value of the capability in both high- and low-volatility environments.

Here again, incremental tweaks and improvements to existing trading workflows are making it possible for market participants to seamlessly collaborate and share information more efficiently than they ever could before. While the technology became a lifeline in the work-from-home world of 2020, it is now proving itself to be the new standard in workflow efficiency, regardless of whether credit and rates traders are back in the office or still working in their pajamas.

4. Flexible Automation

Another major trend that was amplified by the pandemic was the steady reliance on automated trading throughout periods of heightened volatility. This was a notable departure from the trends we saw in the 2008 Financial Crisis, when it was common for Treasury traders to disable their auto quote functions during periods of extreme market volatility, turning to the perceived “safety” of manual trading.

That didn’t happen in 2020. In fact, across the Tradeweb Institutional platform, 25% of all trades were sent through our Automated Intelligence Execution (AiEX) protocol, which links a client’s order management system (OMS) directly to Tradeweb and auto-executes the trade. That demonstrates a significant evolution in the market’s trust of technology, but it also speaks to the evolution of the technology itself, which has come a long way since the earliest days of automated trading.

Through June 2021, 47% of trades[1] have been executed through AiEX.

At its core, the Tradeweb’s AiEX technology automatically applies preconfigured conditions, such as price tolerance thresholds, minimum number of bids required to trade and client-specific compliance thresholds. Trades can then be executed at the best price and sent back to the client’s system, not only creating obvious workflow efficiencies, but also creating a great deal of flexibility and control over how automated trades are executed.

In practice, a trader can issue a single RFQ list containing both manual and AiEX auto-executable items. This process allows the trader to quickly tag highly liquid, vanilla instruments to be traded automatically, but also pull out more complex, illiquid instruments that will require manual intervention. Once the RFQ is processed, any automated trades that do not auto-execute can then be traded manually.

This process enables the elusive benefit of customization at scale. Time-pressed and cost-constrained traders are able to quickly process large volumes, but—within that wide net approach— they are also able to tailor individual strategies for specific CUSIPs that need more specialized attention.

5. Automated Pricing Increases Transparency and Liquidity

The ability to set parameters for an automated credit trade assumes, of course, that traders have some sense of the price of the underlying bond. That’s easier said than done in the credit markets— particularly if you are trading any of the tens of thousands of bonds that do not trade regularly.

The U.S. corporate bond market is the largest in the world, consisting of over 100,000 individual securities. However, less than 5% of these bonds change hands on a daily basis. That’s largely a function of the diversity of bond market. Unlike equity markets, where a single company generally has a single stock, in the corporate bond markets, a single company can issue several different bonds with variations including tenors, call dates, coupon structures and contract terms.

That combination of diversity and illiquidity can make it very challenging to price a bond. Historically, parties on both sides of the trade would be tasked with checking whether the bond traded recently, analyzing current credit and business conditions, digging into individual bond attributes and taking the pulse of the marketplace to see if the other side of the trade agrees with the price. For a complex trade involving a large portfolio of corporate credits, the process could take days.

New advances in machine learning technology are making that process much more streamlined. By ingesting data from FINRA’s Trade Reporting and Compliance Engine (TRACE) along with real-time data from other sources, it is now possible to get an instant snapshot of where tens of thousands of securities are trading at any given moment, apply confidence intervals and current market information and derive an accurate real-world price in a matter of seconds.

This capability has been a lynchpin to maintaining efficiency and liquidity in the credit markets during periods of heightened volatility when historical pricing is thrown out the window.