- Citi and JPMorgan in recent weeks closed down the teams on their corporate-bond trading desks that handle smaller sized bonds known as "odd lots."
- The margins on trading these smaller bonds were too thin to substantiate specific teams dealing the volume. Algorithms will now handle the trading of these bonds.
Some of Wall Streets’ biggest corporate bond dealers are replacing humans with algorithms for a portion of their trades, another sign that the robot revolution is in full swing.
Citigroup and JPMorgan both recently disbanded teams dedicated solely to trading small-sized corporate bond transactions, known as odd lots, according to people familiar with the matter. The teams dealt primarily with retail clients who typically deal in the smaller, less liquid bonds.
At Citi, the responsibilities of eight traders who had been part of the corporate bond retail desk (six in investment grade and two in high yield) were subsumed into the firm’s electronic bond trading platform in within the past few weeks, according to two people with knowledge of the matter. That means the handling of the odd-lot bonds will be managed by algorithms instead of humans.
At least two of the traders, Keith McCluskey and Jim Berry, have left Citigroup. It’s not clear what has happened to the other six. The traders didn’t respond to requests for comment sent through LinkedIn.
“We have significantly enhanced our infrastructure and trading capabilities to create a better experience for clients,” a Citi spokeswoman said in a statement.
The path to the change was paved in September 2018, when Citi reorganized the corporate bond trading team to bring institutional trading teams and the retail trading teams under one leadership.
At JPMorgan, a team of less than five people who had been trading in odd lot corporate bonds, got absorbed into the larger credit trading group over the last few weeks, according to two people with knowledge of the move. The bank decided that it could still serve clients just as well by disbanding the group and steering the volumes onto its electronic trading infrastructure, the people said. Most of the traders are still at JPMorgan.
A bank spokesman declined to comment further.
The moves come as big banks are trying to cut-costs and slash unprofitable business lines, particularly in their trading units. JPMorgan and Citi were thought to be the last two dealers to have dedicated teams for odd-lot trades.
Mike Nappi, vice president of investment grade corporate bond trading at Eaton Vance, told Business Insider he estimates 75-80% of trading on bonds under $1 million is handled by machines. Unless a firm is able to do a high volume of odd-lot trades — he cited Charlotte-based Millennium Advisors as an example — the profit margins are too thin to maintain a large human team.
"It’s a trend we’re going to continue to see over the next few years," Nappi said. "That is where the market is headed for those types of trade sizes, no question."
And while the majority of algorithms operate in bonds under $1 million, the machines are getting more advanced. Nappi said he believes some dealers have algos that have permission to operate in deals as high as $2 million.
Only at the $5 million mark do the algos completely disappear, he added. Automating deals above that mark gets tricky, Nappi said, as firms are concerned about filling up the space on their balance sheet with large bonds they might not be able to move.
Nappi said there is a myth most traders aren’t open to automation in their space because they feel they’ll be out of a job. While it’s true that just like in the general economy, machines will reduce the number of some jobs, it will also streamline others.
With algorithms handling smaller deals, traders will be freed up to handle bigger trades.
"I think most traders would welcome that stuff going electronic because there is plenty to do away from the small odd-lot electronic trade," Nappi said. "If I can spend more time with the difficult trades, that is great. If I have to hand trade 100 odd lots, that is just going to take time."
from SAI https://read.bi/2CkgtsO