US corporate bond trading totalled $40 billion in investment-grade and $13 billion in high-yield in May, a combined $53 billion, Crisil Coalition Greenwich said. Disclosed RFQ made up just over 40 percent of electronic trading that month, the highest share since August 2024. At the same time, market participants are moving to adopt artificial intelligence tools to handle liquidity checks, bond selection, counterparty choice and post-trade reporting, Jim Kwiatkowski, CEO of LTX, wrote in February 2025. The mix of larger block orders and faster automation is changing how large corporate bond trades get executed.

Traders are already sizing up big moves. Many are rebalancing corporate bond books after recent rate cuts from the US Federal Reserve and lower odds of more easing in 2025, Jim Kwiatkowski wrote. Those shifts created demand for faster execution and deeper liquidity.

Electronic trading remains a partial solution. Fewer than half of investment-grade bonds and about a third of high-yield securities trade electronically, LTX said. The market is still a patchwork of infrequently traded issues with different tenures and coupons. That makes matching large orders to the right bonds hard and time consuming.

Why block trades matter

Block trades let institutions move big positions without showing their hand in the open market. They also set trade sizes. In May, combined on-venue volumes for investment-grade and high-yield reached $53 billion. That put pressure on dealers and electronic platforms to offer quicker, more reliable ways to match buyers and sellers.

But the presence of large blocks has also kept some fast, small-algo flows at bay, Greenwich said. Market makers still step in to handle blocks. They act as the bridge between a large seller and a large buyer. That keeps trading orderly when liquidity is thin.

AI moving from idea to workbench

Artificial intelligence is pitching itself as a way to speed up each step of a block trade. LTX described tools that assess liquidity, identify suitable bonds, suggest counterparties and automate post-trade reporting.

Mastheads

Those tools aim to cut hours of manual work into minutes, the firm wrote.

Automation can also standardize decision steps. That helps a desk evaluate many possible fills at once. It reduces reliance on intuition and phone calls. Traders can get a clearer read on how a proposed block will affect prices and spreads.

Firms are testing models that recommend whether to trade a bond electronically, route a request-for-quote, or run an internal crossing. The goal is to pick the method that minimises market impact and cost. That's especially useful for the many bonds that rarely trade.

Who wins and who changes workflow

Investment managers stand to gain from lower execution costs if automation widens access and tightens spreads. Dealers could see more flow routed through electronic rails instead of bespoke, time-intensive negotiations. That would change how dealers staff their trading floors and allocate capital.

Smaller shops may face an adoption test. They need systems and data to use advanced tools well. Larger firms already running electronic workflows will get more leverage from AI. That could concentrate trading volume in fewer platforms and counterparties.

Operational hurdles

AI tools need clean, fast data from many sources. Corporate bonds are fragmented across thousands of issues, and many trade rarely. Sparse trade history makes model training harder. Models also must be tuned to avoid pushing prices when they suggest aggressive fills.

Regulatory and compliance checks remain essential. Post-trade reporting, best execution records and audit trails must still be produced. Automation can help generate those records faster. But firms must ensure controls keep pace with new execution methods.

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Crisil Coalition Greenwich said disclosed RFQ accounted for just over 40 percent of electronic trading in May, the highest share since August 2024. The shift in trading patterns is pushing firms to accelerate AI deployment for execution and post-trade reporting.

This article was created with AI assistance.