Survey: Evolution of the Fixed Income Landscape

The fixed income market is experiencing a wave of increased automation. Over the last ten years the market has evolved considerably but trade processing and pre-trade workflows are still fragmented. Going forward, a firm’s success will be tied to how well prepared they are to respond and participate in automating these processes.
24 May, 2022

This survey was conducted during a webinar on “The Evolution of the Fixed Income Landscape,” hosted by Quantifi, featuring Celent. More than 100 individuals from the financial services industry registered for the webinar and were invited to participate in the survey.


Fixed income infrastructure has a history of waves of increased automation. In turn, the tides of technology have reconfigured the operating landscape in the clearing, pre-trade communication, price dissemination, execution, and post-trade workflows.

In the last few years, COVID-19 has accelerated the demand for increased efficiency as working practices on trading desks call for greater flexibility. Cloud technology is also providing the foundation for expansion in fixed income markets by helping to facilitate the consumption of data and integrating new cloud-deployed applications. Not surprisingly, a firm’s success has been tied to how well prepared they are to respond and participate in the wake of these changes.

What are the key benefits of market making for sovereign debt?

Recent bouts of volatility remind us that liquidity can evaporate quickly in financial markets. Bond market sell-offs highlight that liquidity strains can spread rapidly across market segments. In sovereign debt and, to an even greater degree, corporate bond markets, liquidity hinges in large part on whether market makers respond to temporary imbalances in supply and demand by stepping in as buyers (or sellers) against trades sought by other market participants.

Despite the considerable risks and costs, the participants unanimously thought that Fixed Income market-making is a valuable activity. On the one hand, 56% of the respondents see the potential to leverage their activity or expertise to conduct other profitable activities. On the other hand, for the balance of the respondents, market making is a requirement to maintain or enhance a firm’s brand. Despite facing challenges in competing for activity that is increasingly concentrated in a combination of the top two dealer quintiles and nimble proprietary trading firms, none of the responders shared the view of James Gorman, the CEO of Morgan Stanley, that “it’s pretty hard to be a wannabe or a new entrant trying to break into the group that is dominating the global flow of capital markets.”

Electronic trading volumes have been steadily growing over the last few years. Some estimates put volumes, by ticket count, at more than 70% of all bond trades, and 40-45% of overall volume by size.

In 5 years, what percentage of fixed income trades will be handled by AI/ML-enabled systematic market-makers, without direct human involvement?

* % of trades

The fixed income market is slowly but surely moving towards automation and new technology is reconfiguring the operating landscape. This steady digitisation has accelerated the percentage of fixed income trading that occurs electronically. The overall automation of front-to-back processes, use of data, and use of machine learning has also increased.

As trading volumes continue to grow, tools that were designed to allow traders to execute more trades manually (even with the help of electronic platforms) are no longer adequate. High volume trading desks can receive thousands of RFQs a day, and buy-side clients often execute hundreds of orders. Moreover, algorithmic trading is picking up pace, which drives volumes even higher. All of these factors give rise to the next generation of trade automation that seeks to reduce human involvement in the market-making process and replace or augment it with artificial intelligence (AI) and machine learning techniques. Forty-three percent of participants think that in 5 years’ time, 50-75% of fixed income trades will be handled by AI/ML enabled systematic market makers. This appears to be an accurate representation of where the market is heading. However, there is a chance that the level of automation in fixed income may never reach the level seen in equities. This is because of the more diverse nature and sheer amount of illiquid instruments in the FI market (compared to Equities or FX).

Against the backdrop of automation, firms are also having to contend with regulatory change. The end of LIBOR will have a huge impact across the spectrum of fixed income. The shift to other reference rates such as secured overnight finance rates (SOFR) will impact products including OTC derivatives, short-term borrowing, floating-rate borrowing/lending, and other financing activities.

Which type of rate will be prevailing in SOFR notes in two years?

The transition from LIBOR to SOFR impacts almost every part of the financial services industry including banking, capital markets, insurance and asset management. The transition has led to major changes in the pricing of global financial products.

As expected, going forward most market participants (66%) would prefer to calculate SOFR coupons in a forward-looking way i.e. similar to LIBOR when coupon is known at the beginning of the accrual period. For the two term rates in the poll, the majority of respondents (41%) believe that the “SOFR -in-advance” rate rather than the CME SOFR Term rate (25%) will be the prevailing rate in two years’ time. It is worth noting that currently “SOFR-in-advance” is predominantly used by agency bonds with the coupon calculated at the beginning of the coupon period, based on the SOFR average (can be 30, 90, or 180 day) quoted by the Fed two days before accrual starts. This highlights that even though there is a mismatch of the rate calculation period and coupon period, the convenience of the ‘SOFR-in advance’ rate calculation makes it more attractive for bond market participants.


Fast, accurate analytics is a prerequisite for any automated bond trading or market-making platform. With traditional platforms relying on outdated technology, that is cumbersome and inflexible, firms are turning to technology providers, like Quantifi, that provide sophisticated model libraries and pricing frameworks built on new technology.

Integrating machine learning and/or AI tools into the trade automation process is emerging as a clear trend. Quantifi integrates with data science technology to provide cloud native, scalable performance for large data sets. This enables clients to do more complex data analysis with larger data sets, and to produce flexible reporting with clear, actionable results. Advanced financial analytics and technology, as provided by Quantifi, is designed to help market participants accelerate their adoption of this rapidly developing market.

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