The Evolution of Credit Trading: Industry Survey

This survey was conducted during a webinar on “The Evolution of Credit Trading: Technology, Analytics, and Data,” hosted by Quantifi, featuring Celent and 7 Chord Inc. More than 100 individuals from the financial services industry registered for the webinar and were invited to participate.
19 Aug, 2021

With the growth in bond issuance in 2020, credit is playing an increasingly important role in investment portfolios. The international bond market represents over $128 trillion in debt outstanding. The current credit market environment, characterised by uncertainty and persistent structural inefficiencies, is rich in relative value credit investment opportunities.  Over the past five years, there has been robust growth in the electronic trading of fixed-income markets, but it continues to lag equities in technological development. 

This survey was conducted during a webinar on “The Evolution of Credit Trading: Technology, Analytics, and Data,” hosted by Quantifi, featuring Celent and 7 Chord Inc. (, the creators of BondDroid, a proprietary AI engine that nowcasts sovereign and corporate bond prices and credit spreads in real-time. More than 100 individuals from the financial services industry registered for the webinar and were invited to participate in the survey.


After a volatile year triggered by COVID-19, bond funds and other market participants face new challenges, particularly in fixed income and credit. Recent events have disrupted the financial markets but have also created growth opportunities. With an increasing demand for scalability and transparency enterprise-wide, leading firms are leveraging the latest advancements in technology and the best expertise to assist with the generation and retention of alpha. Firms are also increasingly adopting emerging technologies such as data science and artificial intelligence to provide actionable insights and new investment opportunities. This has driven FinTech providers such as Quantifi to evolve and accelerate innovation to help clients reduce default risk, maximize returns, and improve resilience.

Key Findings

What is the current state of your credit data, analytics & technology infrastructure?

New technologies have opened the door to a revolution in data and analytics, leading to new products, efficiency, and higher margins. The myriad of technologies that are changing how firms operate includes data science, artificial intelligence, and machine learning. Firms are investing in technology to improve competitiveness and automate compliance to ensure robust adherence to regulatory requirements. 80 percent of the firms surveyed are investing in data, analytics, and technology. Companies are increasingly turning to FinTech providers such as Quantifi to drive technology innovation. The growing adoption of cloud and open APIs creates new avenues for smoother interaction in the technology ecosystem. Only 7 percent of respondents did not have the infrastructure needed to capitalize on opportunities, and 13 percent stated they rely on manual processes that need improvement.

What is your main challenge in your credit trading today?

The use of analytics for debt securities has come into sharper focus and has been the subject of increased investor attention over the past 12 months. There are two main reasons why credit/fixed income strategies have become a hot topic during this time. The first is an extraordinary surge of issuance seen in the bond market. The second is the extreme volatility within the credit sector in the face of COVID-19. The two biggest challenges in credit trading cited by respondents are analytics performance (33 percent) and data normalisation (25 percent).

To be able to spot the opportunities, institutional investors need adroit analysis at their disposal. They have to survey the entire credit landscape to isolate opportunities and then execute trades quickly before they vanish. Risk managers also need state-of-the-art tools to measure and report on portfolio risk at all times.

Data normalisation is a major challenge given the high volumes of data sources and format. Data no longer resides in a database somewhere; it is streaming over the cloud and needs to be normalised in real-time. In the coming years, it is likely that digitisation trends and the appetite for firms to adopt data science approaches will increase. With structured, high-quality data, firms can solve data normalisation and standardisation challenges, translating into better, more accurate insights.

When will the market become comfortable with pricing corporate bonds by machines?

The bond market is slowly but surely moving toward automation. In the past, it would take traders, analysts, and quants to price a corporate bond. For complex trades involving a portfolio of securities, the process could take hours. Today, thanks to the availability of data and advances in machine learning, accurate bond prices can be generated by machines in seconds which gives investors and traders alike better price transparency. The findings highlight that, at some point, all firms will be comfortable with machines pricing bonds. Thirty-six percent of respondents are somewhat comfortable today, and 43 percent expect that they will be comfortable within a few years. The slow and steady digitisation of fixed income and the credit markets especially has accelerated in the percentage of fixed income trading that occurs electronically and the overall automation of front-to-back processes, use of data, and use of machine learning. This has been most true in liquid bonds that are part of major indices. Still, going forward, there is keen interest in the electronification of the less liquid parts of the investment grade bonds, high yield bonds, and emerging markets globally. As Dan Frommer, COO of 7 Chord, Inc., noted on the panel, “For predictive bond pricing to gain acceptance, we need to be confident that it can deal with the unexpected…we need to have models that adjust to market regime changes in the same way that the human trader would process data and make price decisions differently depending on the changing market conditions. 

What do you consider the biggest challenge for credit analytics?

The financial landscape is rapidly evolving, and fixed income investing is transforming. There are various challenges that hedge funds are dealing with regarding credit analytics. Advanced modeling (29 percent) and consistent relative value (29 percent) were the biggest obstacles. Today’s institutional investors and individual investors need reliable data and powerful analytics to help them gain actionable insights for better portfolio outcomes. The ability to anticipate and respond to market and portfolio changes are key motivators for investment managers to maintain a strong risk function. Sophisticated and robust analytics are an important component of this capability. The 14 percent for multi-discounting and IBOR replacement indicates that most firms are prepared for the recommendations published early in 2020.

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