This month, the BI-SAM Insights Blog has been tackling the specific challenges posed by fixed income attribution, and a recent comment from a reader regarding the importance of good data got me thinking about not only the complexities of fixed income attribution, but of the complexities and multiple considerations that come with managing fixed income data.
So for the sake of today’s post, let’s agree on a basic premise - 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 better get the data right!
1. 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. And any successful bond trader not trading plain vanilla corporate AAA rated bonds will tell you to make sure you’re covered and have the indenture in conjunction with the prospectus. Yes, wading through the jargon can be time consuming but not doing it is unacceptable risk.
Ok so once we have a sense of the basics related to the documentation and can ensure that we’re 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’ll find that they often get calls from bond traders trying to find out the answer to the elusive question – how many bonds are truly outstanding? Outstanding or free to trade? Two very different concepts and a distinction that 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.
These are of course 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 difference. Are there 180 days, based on a 360 day year, in the semi-annual period? This is the standard US methodology for corporate bonds – but not in the rest of the world. What happens when you’re dealing in the actual number of days and the period extends into a year that has a leap year day? That one-day difference can be significant when it comes to the payment and calculation of accrued interest. What about the concern about ‘through’ and ‘to’? Is the interested calculated through the end of the period OR to the end of the period? This apparent 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, it’s the return on your investment usually expressed as a percent and has an inverse relationship to price. Economics 101, right? But, what about all the options for determining yield – should your yield be based on maturity? What happens if the bond has an embedded call, is it now the yield to call and if so which call date? What happens if the bond has a put option? What about a sinking fund – this allows the issuer to purchase bonds in the open market and hold them to meet his mandatory requirement to retire the security? All of these prepayments could be part of any one bond and then what do you do? Better have the data right, otherwise, you’re not going to have the analytics even close to right. Being able to do this side by side comparison of different yields based on different analysis of the terms and conditions, the market circumstances, the cost of money – all have an impact on the output of your analytics engine. Understanding the yield to the 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.
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. But, what about the data nuances – what’s out there in the terms and conditions that can impact our analytics? Well, 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 causes 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.
Finally, what about something even more complex – mortgage-backed and asset-backed securities? For instance, 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. For example, if borrowing money gets cheaper and there is an uptick in homeowners refinancing their high cost mortgages, those same homeowners may prepay their mortgages early. What’s the impact on prepayment speeds? All of these scenarios are 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.
Vetting the Data
At BI-SAM, via our tools and complex analytics engine, we can use our experience and understanding to vet your holdings data and ensure that your performance is accurately represented day over day. The combination of our engine and the field expertise of our Professional Services team allow us to work closely with portfolio managers during implementation and beyond to validate the variance and question the output so that you can adjust or review as you see fit. This expertise is critical from day one as implementation and analysis can lead to long-term consistent results and the outstanding performance you require.