ABOUT THE AUTHOR

Donald R. Van Deventer, Ph.D.

Don founded Kamakura Corporation in April 1990 and currently serves as Co-Chair, Center for Applied Quantitative Finance, Risk Research and Quantitative Solutions at SAS. Don’s focus at SAS is quantitative finance, credit risk, asset and liability management, and portfolio management for the most sophisticated financial services firms in the world.

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Oracle Corp. Bonds: An Analysis of Risk and Return

07/15/2013 09:21 AM

In this note we analyze the current levels and past history of default probabilities for Oracle Corp. (ORCL), and we compare them to the credit spreads on secondary market trading in Oracle Corp. bonds on Friday, July 12. A sophisticated investor who has moved beyond legacy ratings seeks to maximize revenue per basis point of default risk from each incremental investment, subject to risk limits on macro-factor exposure on a fully default-adjusted basis.  We assume the recovery rate is the same for all bonds traded in this note. All other things being equal, maximizing the ratio of credit spread to the matched maturity default probability will achieve this objective.  As part of this analysis, we consider whether or not a reasonable investor would judge the firm to be “investment grade” under the June 2012 rules mandated by the Dodd-Frank Act of 2010.

Definition of Investment Grade

On June 13, 2012, the Office of the Comptroller of the Currency published the final rules defining whether a security is “investment grade,” in accordance with Section 939A of the Dodd-Frank Act of 2010, replacing references to legacy credit ratings with a reference to default probabilities.  The web page explaining the Office of the Comptroller of the Currency’s new rules defining investment grade and related guidance can be found here.

Term Structure of Default Probabilities

Maximizing the ratio of credit spread to matched maturity default probabilities requires that default probabilities be available at a wide range of maturities. This graph shows the current default probabilities for Oracle Corp. ranging from one month to 10 years on an annualized basis.  Default probabilities range from 0.04% at 1 year to 0.26% at 10 years.

We explain the source and methodology for the default probabilities below.

Summary of Recent Bond Trading Activity

The National Association of Securities Dealers launched the TRACE (Trade Reporting and Compliance Engine) in July 2002 in order to increase price transparency in the U.S. corporate debt market. The system captures and information on secondary market transactions in publicly traded securities (investment grade, high yield and convertible corporate debt) representing all over-the-counter market activity in these bonds. TRACE data for Oracle Corp. shows some volatility in credit spreads for the 91 bond trades (12 other trades were not usable due to reporting errors) captured by TRACE on July 12, 2013.

For maturities between 2.51 years and six years, the average spread per basis point of default risk ranged from 2.79 to 5.76, the value for the shortest maturity bonds.  The 5.76 ratio divides the average credit spread for the bond 0.26% (based on 8 trades) by the interpolated Kamakura default probability, 0.05%.  For longer maturity Oracle bonds, the spread per basis point of default risk ranged from 3.05 to 4.23. The high, low and average spread per basis point of default risk is graphed here:

The Depository Trust & Clearing Corporation reports weekly on new credit default swap trading volume by reference name.  For the week ended July 5, 2013 (the most recent week for which data is available), the credit default swap trading volume on Oracle Corp was as follows: 6 contracts traded during the week, representing a notional principal of $22 million.

On a cumulative basis, the default probabilities for Oracle Corp range from 0.04% at 1 year to 2.60% at 10 years.

Over the last 10 years, the 1 year and 5 year default probabilities for Oracle Corp have varied as shown in the following graph. The 5 year default probabilities peaked near 0.45%, while the 1 year default probabilities hit a maximum of 0.35% during this period.

Over the same period, the legacy credit ratings for Oracle Corp have changed twice.

The macro-economic factors driving the historical movements in the default probabilities of Oracle Corp over the period from 1990 to the present include the following factors of those listed by the Federal Reserve in its 2013 Comprehensive Capital Analysis and Review:

  • Growth in real gross domestic product
  • Unemployment rate
  • 30 year fixed rate mortgage yield
  • VIX volatility index
  • 4 international macro factors

These factors explain 78.6% of the variation in the 1 year default probability for Oracle Corp.

Oracle Corp can be compared with its peers in the same industry sector, as defined by Morgan Stanley and reported by Compustat.  For the USA Software & Services sector, Oracle Corp has the following percentile ranking for its default probabilities among its peers at these maturities:

1 month 67th percentile
1 year 43th percentile
3 years 17th percentile
5 years 12th percentile
10 years 12th percentile

A comparison of the legacy credit rating for Oracle Corp. indicates that the company is overrated by four ratings grades.

Conclusions

Oracle Corp. came through the credit crisis with only modest increases in its default probabilities.  The firm is well below the median default probability for its peers in the USA Software & Services sector for maturities of one year and longer.  At a ten year horizon, Oracle’s default probability is very low, at the 12th percentile of the peer group.   We believe that most sophisticated analysts would rate Oracle Corp as investment grade by the Comptroller of the Currency definition.

Background on Default Probabilities Used

The Kamakura Risk Information Services version 5.0 Jarrow-Chava reduced form default probability model makes default predictions using a sophisticated combination of financial ratios, stock price history, and macro-economic factors. The version 5.0 model was estimated over the period from 1990 to 2008, and includes the insights of the worst part of the recent credit crisis. Kamakura default probabilities are based on 1.76 million observations and more than 2000 defaults. The term structure of default is constructed by using a related series of econometric relationships estimated on this data base. An overview of the full suite of related default probability models is available here.

General Background on Reduced Form Models

For a general introduction to reduced form credit models, Hilscher, Jarrow and van Deventer (2008) is a good place to begin. Hilscher and Wilson (2013) have shown that reduced form default probabilities are more accurate than legacy credit ratings by a substantial amount. Van Deventer (2012) explains the benefits and the process for replacing legacy credit ratings with reduced form default probabilities in the credit risk management process. The theoretical basis for reduced form credit models was established by Jarrow and Turnbull (1995) and extended by Jarrow (2001). Shumway (2001) was one of the first researchers to employ logistic regression to estimate reduced form default probabilities. Chava and Jarrow (2004) applied logistic regression to a monthly database of public firms. Campbell, Hilscher and Szilagyi (2008) demonstrated that the reduced form approach to default modeling was substantially more accurate than the Merton model of risky debt. Bharath and Shumway (2008), working completely independently, reached the same conclusions. A follow-on paper by Campbell, Hilscher and Szilagyi (2011) confirmed their earlier conclusions in a paper that was awarded the Markowitz Prize for best paper in the Journal of Investment Management by a judging panel that included Prof. Robert Merton.

ABOUT THE AUTHOR

Donald R. Van Deventer, Ph.D.

Don founded Kamakura Corporation in April 1990 and currently serves as Co-Chair, Center for Applied Quantitative Finance, Risk Research and Quantitative Solutions at SAS. Don’s focus at SAS is quantitative finance, credit risk, asset and liability management, and portfolio management for the most sophisticated financial services firms in the world.

Read More

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