This example starts with a look from the top down, starting with some macro analytics, and then brings it down to a more micro level of actual credit selection possibilities that can fit the theme developed by the macro analytics.
A macroanalysis might include looking at how the average yields on the leveraged loan market are trading relative to the average yields in the high-yield bond market versus historic trading patterns. In this analysis, you would consider many factors, including the following:
- The bulk of the loan market has floating-rate coupons while the high-yield bond market does not.
- During various historic cycles, what was the economy doing versus the current environment?
- During various historic cycles, what direction were interest rates going versus the current environment?
- How has the makeup of the two markets changed over time? (For example, does the high-yield market now have significantly more secured bonds than it did during some other cycle?)
In addition, you would likely consider and address many other factors in the analysis.
In the preceding example, let’s assume you see a historical pattern that during a period of soft gross domestic product (GDP) growth, you actually see that yields on the high-yield bond market go up materially more than on loans. You then might want to take this to another level and see if this pattern is true for all bonds or just certain types. Perhaps it is most pronounced in notes that have a subordinated ranking and that were issued by companies that are in cyclical industries.
If you are worried about entering a period of soft GDP, you might then want to analyze which bonds fit the criteria to make sure you do not have exposure to them. You can then use a database with a query system to develop a list of debt issuers that are in cyclical industries and have subordinated bonds outstanding. You might then want to take this list of subordinated bonds, compare it with the spread on the same company’s loans, and compare this spread to historical trends while also having a credit analyst explore the overall credit strength of the company.
This is just a simple example of how data analytics can be used in the corporate debt market.
I believe that analytics in the corporate debt market is still in the early stages of development and usage. There will be significant advances in the use of analytics going forward and this will lead to an increase in spending in coming years. Money will need to be spent to improve risk management and develop new analytical tools for the markets. Data management and analysis techniques that are being used in other fields, such as in marketing, are likely to be adapted for these markets.