Electronic trading of fixed income will struggle to grow so long as buy-side firms continue to value existing sell-side practices, according to a new report.
A US-focused study by financial research firm Celent asserts that despite the potential advantages of increased electronic trading of bonds and other fixed income instruments, institutional investors are often reluctant to give up the pricing and services they currently enjoy in a largely manual execution environment.
“There isn’t much incentive for large buy-side firms to change the status quo any time soon and increase their participation in algo trading,” said Anshuman Jaswal, Celent senior analyst and author of the report. “Big buy-siders currently benefit from good pricing because of the larger volumes they trade.”
However, Jaswal argued that the current situation reduced available liquidity and impeded the growth of automated trading. It also diminished the benefits of algo trading that smaller market participants would obtain.
Jaswal said there were a number of other hurdles which meant fixed income instruments would not see substantial increases in algorithmic trading growth in the next two to three years.
“A major barrier is low liquidity, especially in the case of instruments other than US Treasuries, such as corporate bonds and federal agency securities,” he said.
Cash equity and foreign exchange have both seen healthy growth in algorithmic trading because they enjoy high levels of liquidity. “Fixed income, by contrast, can be seen as relatively slow out of the blocks,” said Jaswal. “Most debt instruments do not have levels of liquidity that encourage the use of low-latency trading.”
Post-trade transparency was also an issue, as not all instruments had the benefits offered in the case of corporate bonds through the US’s Financial Industry Regulatory Authority’s trade reporting and compliance engine.
“The lack of transparency, especially post-trade, means that the markets are not sufficiently mature for algo trading,” he said.
Jaswal believed the lack of availability of historical prices for debt markets was also important, resulting in higher costs and a longer time taken for testing algorithms. He explained it was costly and time-consuming to create a database of historical prices and this was a significant barrier for algo trading. The lack of market benchmarks also made it difficult to employ basic VWAP and TWAP algorithms.
“While multi-asset trading is fast becoming a reality, it would be difficult for us to expect the entire fixed income market to follow suit in the manner that asset classes such as cash equity and foreign exchange have in the US and other leading global markets,” Jaswal said.