Ford Motor Company (NYSE:F) was one of the thirty most heavily traded corporate bond issuers in the U.S. bond market on September 13, 2013. This note uses the default probabilities and bond spreads of Ford Motor Company to measure the relative reward-to-risk ratio on both firm's bonds. A total of 146 trades were reported on 15 fixed-rate non-call bond issues of Ford Motor Company with trading volume of $27.3 million. After eliminating bad data, we used 144 trades on 14 bond issues in this note. We leave for another day an analysis of spread on the bonds of other legal entities associated with Ford Motor Company.
Institutional investors around the world are required to prove to their audit committees, senior management, and regulators that their investments are in fact "investment grade." For many investors, "investment grade" is an internal definition; for many banks and insurance companies "investment grade" is also defined by regulators. We consider whether or not a reasonable U.S. bank investor would judge Ford Motor Company to be "investment grade" under the June 13, 2012 rules mandated by the Dodd-Frank Act of 2010, which requires that credit rating references be eliminated. The new rules delete references to legacy credit ratings and replace them with default probabilities as explained here.
Assuming the recovery rate in the event of default would be the same on all bond issues, 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. In this note, we also analyze the maturities where the credit spread/default probability ratio is highest for Ford Motor Company
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. The graph below shows the current default probabilities for Ford Motor Company ranging from one month to 10 years on an annualized basis. For maturities longer than ten years, we assume that the ten year default probability is a good estimate of default risk. The default probabilities range from 0.24% at one month to 0.12% at 1 year and 0.97% at ten 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 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. We used the bond data mentioned above for the 14 Ford Motor Company fixed rate non-call bond issues mentioned above.
The graph below shows 5 different yield curves that are relevant to a risk and return analysis of Ford Motor Company bonds. These curves reflect the noise in the TRACE data, as some of the trades are small odd-lot trades. The lowest curve, in dark blue, is the yield to maturity on U.S. Treasury bonds, interpolated from the Federal Reserve H15 statistical release for that day, which matches the maturity of the traded bonds of Ford Motor Company. The second lowest curve, in the lighter blue, shows the yields that would prevail if investors shared the default probability views outlined above, assumed that recovery in the event of default would be zero, and demanded no liquidity premium above and beyond the default-adjusted risk-free yield. The third line from the bottom (in orange) graphs the lowest yield reported by TRACE on that day on Ford Motor Company bonds. The fourth line from the bottom (in green) displays the average yield reported by TRACE on the same day. The highest yield is obviously the maximum yield in each Ford Motor Company issue recorded by TRACE.
The liquidity premium built into the yields of Ford Motor Company above and beyond the "default-adjusted risk free curve" (the risk-free yield curve plus the matched maturity default probabilities for the firm) is more erratic than that of any bond issuer analyzed so far in this series. We explain in more detail below.
The high, low and average credit spreads at each maturity are graphed below. The credit spreads are gradually increasing with the maturity of the bonds. We have done nothing to smooth the data reported by TRACE, which includes both large lot and small lot bond trades. For the reader's convenience, we fitted a cubic polynomial that explains the average spread as a function of years to maturity. This polynomial explains 25.53% of the variation in the average credit spread over the maturity term structure. This is a much lower figure for explanatory power than we have seen on average in this series of notes:
Using default probabilities in addition to credit spreads, we can analyze the number of basis points of credit spread per basis point of default risk at each maturity. This ratio of spread to default probability is shown in the following table for Ford Motor Company The credit spread to default probability ratio ranges from 1.86 to 3.57 times. This reward to risk ratio indicates below average risk-adjusted returns relative to the other firms analyzed in this series of bond notes.
The credit spread to default probability ratios are shown in graphic form here. We have again added a cubic polynomial relating the credit spread to default probability ratio to the years to maturity on the underlying bonds. The smoothed line explains 11.38% of the variation in the reward to risk ratio.
The Depository Trust & Clearing Corporation reports weekly on new credit default swap trading volume by reference name. For the week ended September 6, 2013 (the most recent week for which data is available), the credit default swap trading volume on Ford Motor Company was 15 trades with $80.5 million of notional principal, so the average daily trading volume ($16.1 million) is about 30% below the daily bond trading volume on September 13. The number of credit default swap contracts traded on Ford Motor Company in the 155 weeks ended June 28, 2013 is summarized in the following table:
Ford Motor Company ranked 69th among all reference names in weekly credit default swap trading volume during this period, which is graphed below:
On a cumulative basis, the default probabilities for Ford Motor Company range from 0.12% at 1 year to 9.32% at 10 years.
Over the last decade, the 1 year and 5 year default probabilities for Ford Motor Company have been very volatile during the heart of the credit crisis. The one year default probability peaked at more than 50%, and the five 5 year default probability peaked at more than 20%.
Ford Motor Company can be compared with its peers in the same industry sector, as defined by Morgan Stanley (MS) and reported by Compustat. For the USA "autos and components" sector, Ford Motor Company has the following percentile ranking for its default probabilities among its 60 peers at these maturities:
1 month 62nd percentile
1 year 52nd percentile
3 years 42nd percentile
5 years 37th percentile
10 years 35th percentile
The percentile ranking of Ford Motor Company default probabilities at one month through one year is in the riskiest half of the peer group. The percentile ranking for Ford Motor Company at 3 through 10 years is in the second safest quartile of the peer group. Taking still another view, both the actual and statistically predicted Ford Motor Company credit ratings are barely "investment grade" by traditional credit rating standards of Moody's Investors Service and the Standard & Poor's affiliate of McGraw-Hill. The statistically predicted rating is the same as the legacy rating.
We believe that a small majority of analysts would rate Ford Motor Company as investment grade. From a bond risk and return point of view, Ford Motor Company bonds offer a ratio of credit spread to default risk that is below average. The volatility of the pricing observed in the TRACE system means that investors must be extremely cautious in trade execution to avoid a trade that produces a risk-return trade-off that is even more disadvantageous than what was reported by TRACE on Friday. Like AT&T (T), featured in another Kamakura research report, one can speculate that the iconic nature of the Ford brand name has led investors to overprice the bonds of Ford. In fact, in a recent paper, van Deventer and Zimmermann ("The Impact of Heuristics on the Practice of Risk Management: The Example of Default Probabilities" Kamakura Corporation memorandum, September 12, 2013), quoted Nobel Prize winner Daniel Kahneman on the subject of investing in the securities of Ford:
"Kahneman (Thinking, Fast and Slow, 2012, p. 12) tells the story of a fund manager who invested in the stock of Ford Motor Company because he liked Ford cars, not because Ford's stock was undervalued in the market place. Kahneman explains, 'The question the executive faced (should I invest in Ford stock?) was difficult, but the answer to an easier and related question (do I like Ford cars?) came readily to his mind and determined his choice. This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one, instead, usually without noticing the substitution.'"
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.
Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.
Additional disclosure: Kamakura Corporation has business relationships with a number of firms mentioned in this article.
This article was written by
Dr. Donald R. van Deventer has been in the risk management business since completing his Ph.D. in Business Economics at Harvard University in 1977. He founded the Kamakura Corporation in 1990 after 13 years with two of the 10 largest banks in the US and a stint as investment banker in Tokyo. He joined SAS Institute Inc. as co-head, of the Center for Applied Quantitative Finance in 2022 when SAS acquired Kamakura Corporation. At the time Kamakura was acquired by SAS, Kamakura's institutional clients had total assets or assets under management of 48 trillion dollars.He leads the investing group Learn more.