Welcome to the fourth part of our Credit Data Strategy blog series.
Based directly on conversations with our clients and other market participants, this special blog series will spotlight how the credit market is leveraging technology and automation to unlock greater performance. Whether your firm has already implemented a credit data strategy or is yet to begin, this series will help you benchmark your practices and offer resources and advice on how to succeed in an ever-shifting landscape.
Why are credit markets late to digital transformation?
Up until very recently, credit analysts still worked as they had a generation ago. This manual, expensive, repetitive process entailed ad hoc sourcing of financing disclosure, “spreading” relevant numbers into individualized spreadsheet models, calculating credit ratios, then incorporating market data to identify relative value.
This repetitive process frequently limited investors’ capacity for higher-value work and their agility in a fast-moving market. Separately, access-to-information has become even more challenging for most market participants over the past decade, adding additional friction to the credit research process.
This is counter-intuitive in at least three respects:
1. Other major asset classes have already been digitalized
In a parallel development over this period, most other major asset classes (equities, fx, rates, etc.) have been dramatically transformed by data and automation. This is seen through the growth of algorithmic trading, quantitative investment strategies, and real-time text parsing and sentiment analysis, for example.
2. Credit trading is ever more electronic
While credit analysis has been largely untouched by innovation, every year that goes by, credit trading is operating more and more like it does in these other markets, with electronic execution and portfolio trading now commonplace in both investment grade and high yield bond markets.
3. Most analytical fields have undergone a data revolution
Most other analytical fields have been dramatically transformed by data science, algorithmic processes, and process automation over the past decade. A few widely observed examples include diagnostics in healthcare, personalized marketing in consumer retail, supply-chain management in manufacturing and logistics, personalized instruction in the education industry, and recruiting analytics in professional sports.
"... with the number of issuers and nominal debt outstanding in public leveraged credit markets having grown by roughly 3x over the past 10 years, we rarely find investment firms actively covering more than 50-60% of their tracking benchmark."
While objectives and incentive structures may differ slightly across the complete range of credit market participants (including asset managers, hedge funds, insurance companies, investment banks, commercial banks, etc.), the general remit is largely the same: maximize returns / minimize losses across a given opportunity set or investment index.
Yet with the number of issuers and nominal debt outstanding in public leveraged credit markets having grown by roughly 3x over the past 10 years, we rarely find investment firms actively covering more than 50-60% of their tracking benchmark.
The opportunity to find additional value in the market and not forgo potential diversification should be reason alone for overcoming the blockers to transformation that we covered in the previous chapter.
Coming up: Advantages of implementing a credit data strategy
Next week, we outline the benefits of implementing a credit data strategy, based on our first-hand experiences with our clients.
As always, if you'd like to discuss your own strategy, contact us if we can be of further assistance.