Fixed income attribution is a complex business. Along with that complexity there are multiple considerations that come with managing fixed income data itself. In the world of fixed income securities and data, a minor change can result in millions of dollars in cost differential. In other words, you had better get the data right!
Start With Documentation and Corporate Insight
There are several key differences between fixed income and equity-based securities, including, but not limited to, interpretation of the official documentation and corporate insight. The world of fixed income securities is driven by the terms and conditions as defined by the prospectus/offering circular, right? Well, in most cases, that’s true. However, in some cases, there are terms that are more clearly defined in the indenture. That’s right, the indenture, the overwhelming, bulky, full-of-legal-jargon document. Any successful bond trader not trading plain vanilla corporate AAA-rated bonds will tell you to make sure you are covered and have the indenture in conjunction with the prospectus. Yes, wading through the jargon can be time consuming but not doing so is an unacceptable risk.
Once we have a sense of the basics related to the documentation and can ensure that we are interpreting the offering correctly, how does corporate insight help or hinder the valuations associated with these securities? Talk to a corporate treasurer sometime and you will find that they often get calls from bond traders trying to find out the answer to these elusive questions: How many bonds are truly outstanding? Are the bonds outstanding or free to trade? These are two very different concepts and the distinction between them can be impactful when determining the price and subsequent valuations associated with a security. Being able to accurately identify the number of bonds that are free to trade can provide a significant piece of insight for a corporate bond trader.
Of course, these are just some very basic concepts related to bond terms and conditions and ensuring that your documentation is complete. But how does all this tie to the analytics of these bonds and the impact that incorrect or different data can have?
Let’s think about this in terms of accrued interest. Something as simple as the number of days in the calendar year or in the accrual period can have a significant impact. Are there 180 days, based on a 360-day year, in the semi-annual period? This is the standard U.S. methodology for corporate bonds, but this is not the case for the rest of the world. What happens when you are dealing in the actual number of days and the period extends into a leap year? That one-day difference can be significant when it comes to the payment and calculation of accrued interest. What about the distinction between "through" and "to?" Is interest calculated through the end of the period or to the end of the period? These seemingly minor language differences can mean significant swings in a firm’s cash outlay.
Let’s think about another very standard analytic: yield. According to bond traders everywhere, this is the return on your investment, usually expressed as a percent and having an inverse relationship to price. Economics 101, right? But, what about the many options for determining yield? Should your yield be based on maturity? What happens if the bond has an embedded call? Should you now use the yield-to-call and, if so, which call date? What happens if the bond has a put option? What about a sinking fund, which allows the issuer to purchase bonds in the open market and hold them to meet the mandatory requirement to retire the security? These prepayments could be part of any one bond; then what do you do?
Better have the data right, otherwise you are not going to have the analytics even close to right. The ability to do a side-by-side comparison of different yields based on analysis of the terms and conditions, the market circumstances, and the cost of money will impact the output of your analytics engine. Understanding the yield-to-worst scenario might be the best answer, but unless you can easily and accurately define each one of those components, you may make a crucial and costly mistake.
Convertibles Can Be Complicated
How about something slightly more complex: convertibles? Convertibles are hybrid securities; they look like fixed income securities but have an embedded optionality that causes them to sometimes trade like equities. What about the data nuances? What in the terms and conditions could impact your analytics? Besides the obvious optionality and the underlying pricing of the tracking stock, what happens with dividend payments? When are you entitled to receive the payment and when aren’t you? How does the dividend payment date of the underlying stock impact the increase in price over the payment period?
Understanding the relationship between all these data variables allows the analytics to be consistent and accurate. Missing any of these variables can cause problems. Misunderstanding the period the stock needs to trade at a certain level can cause a bondholder to miss a key trigger and a window of opportunity can be lost.
MBS and ABS Bonds Are Even More Complex
Finally, what about something even more complex: mortgage-backed and asset-backed securities? MBS and ABS bonds can be backed by a pool of residential mortgages or credit card receivables. Depending on many factors, cash payments made on these pools are distributed over the individual tranches within the bond structure. If borrowing money gets cheaper and there is an uptick in homeowners refinancing their high-cost mortgages, those homeowners will prepay their existing mortgages early. What is the impact on prepayment speeds?
These scenarios are all driven by massive amounts of data. All of that valuable data subsequently directly contributes to your performance, either positively or negatively. Being able to trace these performance inhibitors or drivers, can be a key to long-term success and insight.