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# Fridson: High Yield Bond Prices Less Volatile than Math Suggests. Are the Dice Loaded?

Certain things can be proven statistically, even if the cause is unknown.

Consider what happens when your roll a pair of dice. There are 36 possible combinations, e.g., (1 + 1), (3 + 4), (6 + 5). Only one combination—(1 + 1)—produces an outcome of 2. Therefore, you can expect to roll a 2 only one out of 36 times, on average. On the other hand, six combinations produce an outcome of 7— (1 + 6), (2 + 5), (3 + 4), (4 +3), (5 + 2), (6 + 1). Accordingly, you can expect to roll a 7 once in every six rolls, on average. The table below shows the number of times each outcome should occur in 100,000 rolls.

If the results of your dice-rolling experiment diverge from these figures by more than a tiny, tiny bit you can be certain that you were not using fair dice. (We rule out the possibility of psychokinesis.) Note as well that the results do not tell you why the dice were not fair. There may have been a defect in the manufacturing process. Alternatively, someone may have “fixed” the dice by loading, shaving, or some other method. Whatever the cause, the fact that the dice are not fair is incontrovertible, given the laws of probability and the very large number of trials.

Let us now consider price moves on bonds. Unless something unusual is going on, a histogram of monthly price changes should produce a bell-shaped curve as in the illustration (non-bond-related) below (see note 1). This pattern is intuitive to seasoned market participants: In most months, prices go up or down by about an average amount. Once in a while the price change is much greater or much smaller than average. Even more infrequently the monthly price change is very much greater or smaller than average. (By the way, this same reasoning applies to total returns. We confirmed that our key result holds for total returns as well as price changes.)

The notion that price moves should follow a normal distribution is not a matter of academic theorizing with no connection to the real world. According to the math that formally describes the bell-shaped curve, price changes in 68.2% of all months in the sample of observations should fall in a range of plus/minus one standard deviation from the mean (simple average) return. Price changes between one and two standard deviations greater than the mean or between one and two standard deviations smaller than the mean should account for 27.2% of the months. That leaves 4.6% of all months in which to expect price moves more than two standard deviations above or below the mean. In the top panel of the table below, we apply those percentages to the 372 months in our 1987–2017 observation period to predict how many months will be found in each of those three ranges, if the monthly price changes are in fact normally distributed.

In the case of investment-grade corporates, the actual distribution among the three ranges almost perfectly matches the predicted distribution—253/101/18 versus 254/101/17. That result should lay to rest any notion that the normal distribution is just a theoretical model of how the things would work in an idealized world. Investment-grade corporate behavior demonstrates that following a bell-shaped curve with a specific mathematical description is how the real world works.

Government bonds (Treasuries and agencies) veer a bit more from the predicted distribution. All told, 125 months lie outside the range of plus/minus one standard deviation versus the predicted total of 118. Note that research has found that sort of pattern in equity returns, popularly referred to “fat tails.” That abnormality is commonly attributed to momentum trading. The notion is that instead of settling down to normal swings after major positive or negative news comes into the market, stocks continue to gyrate wildly without any additional, major news hitting the market, thanks to aggressive traders who jump on the recent trend and overpower value-oriented traders.

High-yield bonds display the opposite sort of abnormality. Instead of having too many extreme moves, the asset class has too few. Prices in just 83 months moved up or down by one standard deviation or more, some 30% less than the predicted count of 118. The “missing” months were all in the plus/minus 1–2 standard deviations range—just 64 actual versus 101 predicted. Extreme observations of plus/minus 2 standard deviations or more were about right—19 actual versus 17 predicted.

These are not inconsequential divergences from the standard bell curve. With the statistical technique known as the Jarque-Bera(JB) test we can confirm that the data shown for the ICE BAML High Yield Index do not represent a normal distribution. The calculation produces a very high JB value indicative of non-normality and a p-value far below 0.01. Similarly non-normal are the distributions shown, in the second panel of the table, for the BB, B, and CCC-C sub-indexes. Of the three, the BB sub-index is the most overconcentrated in the plus/minus 1 standard deviation range—202 actual months versus 172 predicted.

Why are high-yield price moves not normally distributed?
If a dice-rolling experiment fails to produce the predicted distribution of outcomes, as discussed above, we know that the dice are not fair. That information does not explain why the dice are not fair, that is, whether the manufacturing process was defective or whether somebody altered them. Similarly, the fact that price changes on the high-yield index are not normally distributed does not tell us why they are not normally distributed. We can, however, generate hypotheses and, to the extent feasible, test them.

One hypothesis we thought of is that the Federal Reserve has created an artificially stable financial environment through its quantitative easing (QE) policy. The third and fourth panels of the table display the results of our test of this hypothesis. They show far fewer months outside the plus/minus one standard deviation range in both the pre-QE era (42 actual versus 84 predicted) and the QE era (25 actual versus 35 predicted). The JB test confirms that in both sub-periods the distributions are non-normal. In short, we can reject the hypothesis that quantitative easing artificially stabilized the high-yield market, resulting in fewer extreme monthly price changes than ought to have been observed. If anything, high-yield price changes have been closer to normal during the QE period. (Note that the pre-QE versus QE testing is based on means and standard deviations within those sub-periods.)

We have come up with only one other hypothesis to explain the shortfall of extreme price changes in the high-yield market. Other market participants may find it unpalatable and propose other possible explanations. We encourage discussion and debate on this topic.

Our remaining hypothesis is that reported prices understate the high-yield market’s true volatility. This does not necessarily imply that traders are deliberately understating the magnitude of price declines during major market declines, although we cannot readily disprove that possibility.

Understatement of price declines could also result from good-faith efforts to mark to market the many issues that do not trade in any given month.
Note that if price declines are understated in downturns, price rises will be understated in subsequent upturns. This can explain why in the underlying data we find a shortfall of large up moves as well as a shortfall of large down moves. Also, as noted above, the shortfall of extreme high-yield price changes was entirely in the plus/minus 1 to 2 standard deviations range. We might reasonably infer that the flaw in high-yield pricing, whatever it turns out to be, understates price changes in “somewhat extreme” market declines, but cannot hide the most massive sell-offs.

Our finding of a non-normal distribution of high-yield price changes, with fewer extreme changes than are expected to occur in a properly functioning market, has important implications for asset allocation. It implies that the index-derived standard deviations and, by extension, Sharpe ratios that institutional investors are using to evaluate the high-yield asset class paint too rosy a picture of its risk-reward ratio. Further exploration of this important question seems warranted.

Indexes used in this report:
ICE BofA Merrill Lynch BB US High Yield Index
ICE BofA Merrill Lynch B US High Yield Index
ICE BofA Merrill Lynch CCC-C US High Yield Index
ICE BofA Merrill Lynch US Corporate Index
ICE BofA Merrill Lynch US High Yield Index
ICE BofA Merrill Lynch US Treasury & Agency Index

Thanks to John Finnerty and Yuewu Xu for their kind assistance in this analysis. Any errors or omissions are the author’s.

Marty Fridson, Chief Investment Officer of Lehmann Livian Fridson Advisors LLC, is a contributing analyst to S&P Global Market Intelligence. His weekly leveraged finance commentary appears exclusively on LCD, an offering of S&P Global Market Intelligence. Marty can be reached at [email protected]

Research assistance provided by Kai Zhao and Yaxian Li.

ICE BofAML Index System data is used by permission. Copyright © 2018 ICE Data Services. The use of the above in no way implies that ICE Data Services or any of its affiliates endorses the views or interpretation or the use of such information or acts as any endorsement of Lehmann, Livian, Fridson Advisors, LLC’s use of such information. The information is provided “as is” and none of ICE Data Services or any of its affiliates warrants the accuracy or completeness of the information.

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# HG Bonds: Boeing Shops \$1.4B, Four-Part Deal Ahead of Two Maturities

Boeing Co. (NYSE: BA) is in market with a \$1.4 billion, four-part public offering across five-year notes due 2023, a 10-year issue due 2028, a 20-year tranche due 2038, and 30-year notes due 2048, all with a March 1 maturity date, sources said. The “no-grow” issue is guided to an A/A2/A profile.

Goldman Sachs is a bookrunner across all the tranches. Additionally, Citigroup and J.P. Morgan are marketing the 2023 issue, Barclays and BAML are bookrunners for the 2028 notes, SMBC and Wells Fargo are bookrunners for the 2038 tranche, and Deutsche Bank and Mizuho are marketing the long bonds.

The deal will carry make-whole call provisions and par calls from one and three months prior to maturity for the five- and 10-year notes, and from six months prior to maturity for the 20- and 30-year notes.

According to regulatory filings, the proceeds from the offering will be used for general corporate purposes. Of note, Boeing has two long-term maturities due this year, starting with \$350 million of 0.95% notes due on May 15, followed by \$250 million of 2.9% notes due Aug. 15, which was issued by subsidiary Boeing Capital Corp., according to S&P Global Market Intelligence.

Initial whispers for today’s proposed offering surfaced at T+55–60 for the 2023 notes, at T+75–80 for the 2028 issue, in the T+85 area for the 2038 notes, and at T+100–105 for the long bonds, indicating reoffer yields near 3.20%, 3.64%, 4%, and 4.15%, based on the tight end of talk.

The Chicago-based company last tapped the market a year ago, when it placed a \$900 million, three-part offering, evenly split across 2.125% five-year notes due March 2022 at T+42, or 2.38%; 2.8% 10-year notes due March 2027 at T+60, or 3.07%; and 3.65% 30-year notes due March 2047 at T+85, or 3.91%. For reference, the 2022 issue traded yesterday at T+18 (or at a G-spread equivalent of 30 bps), the 2027 notes changed hands last month at T+49 (at a G-spread of 51 bps), and the 2047 notes traded late last month at T+75 (or at a G-spread of 77 bps), according to MarketAxess.

Since December, Boeing and Brazilian aircraft manufacturer Embraer S.A. have been in discussions about a possible transaction involving a possible merger. According to S&P Global Ratings neither company has specified what form such a deal may take, though it could be a joint venture, a full acquisition of Embraer, or some other deal structure. The government of Brazil maintains a “golden share” in Embraer, which it could use to put pressure on or block the deal.

S&P Global Ratings said Boeing’s ratings will likely not be affected by a possible transaction between the two companies. “We believe that Boeing has flexibility at the current rating to undertake a large multi-billion dollar transaction because the company’s funds from operations (FFO)-to-debt ratio is currently well above our downgrade trigger of 40% (Boeing’s FFO-to-debt ratio was 62% for the 12 months ended Sept. 30, 2017),” the agency, which maintains an A rating and stable outlook, said on Dec. 22, 2017.

“The company currently has about \$10 billion in cash and short-term investments and we expect it to generate at least \$10 billion of free cash flow over the next 12 months. However, our current forecast assumes that management will use all of the company’s free cash flow and some of its cash on hand for dividends and share repurchases,” analysts added.

Last April, Fitch affirmed its A rating and stable outlook on Boeing. “Large acquisitions, although not anticipated, also could affect the ratings, as could debt-funded share repurchases. Sustained consolidated FFO-adjusted leverage approaching 2.0x could lead to a negative action,” Fitch said at the time. — Gayatri Iyer

LCD comps is an offering of S&P Global Market Intelligence. LCD’s subscription site offers complete news, analysis and data covering the global leveraged loan and high yield bond markets. You can learn more about LCD here.

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# US High Yield Bond Funds See \$2.7B Retail Cash Withdrawal

U.S. high-yield funds recorded an outflow of roughly \$2.7 billion for the week ended Feb. 7, according to weekly reporters to Lipper only. This follows last week’s exit of about \$1.7 billion and marks the fourth consecutive week of outflows, for a total of \$8.7 billion over that span.

This week’s exit was fairly evenly split with a \$1.4 billion outflow from mutual funds, while \$1.3 billion exited ETFs.

The year-to-date total outflow from high-yield funds is now at about \$5.9 billion.

The four-week trailing average declined to negative \$2.2 billion for the period, from negative \$825 million last week, and the change due to market conditions this past week was a decrease of \$1.7 billion.

Total assets at the end of the observation period were \$202.2 billion, indicating the lowest point since November 2016. ETFs account for about 23.5% of the total, at \$47.6 billion. — James Passeri

LCD comps is an offering of S&P Global Market Intelligence. LCD’s subscription site offers complete news, analysis and data covering the global leveraged loan and high yield bond markets. You can learn more about LCD here.

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# Amid Yesterday’s Market Rout, JW Aluminum Scraps \$300M High Yield Deal

JW Aluminum has postponed its \$300 million offering of eight-year secured notes, citing “adverse market conditions.” The decision comes amid the brutal equity sell-off, with the DJIA losing 7.9% yesterday and opening another 2% in the red this morning, before rebounding sharply.

As reported, the company roadshowed the deal all of last week via bookrunners Goldman Sachs (B&D), and Deutsche Bank. Whispers for the debt were in the 8% area, sources said.

According to sources, proceeds were earmarked to refurbish and expand the company’s capabilities at its manufacturing operations. Funds raised would also have been used to repay a \$151.4 million secured term loan, as part of a refinancing effort that was expected to include an amendment to the company’s existing asset-based revolving credit facility to extend the maturity of that facility to 2023.

The borrower was also expected to fund the transaction with \$35 million of shareholder equity.

S&P Global Ratings assigned a B– rating to the borrower’s proposed bond offering, with a 3 recovery rating. S&P Global analysts “expect adjusted debt to EBITDA of about 6x and adjusted EBITDA margins of about 10% over the next 12 months,” the Jan. 25 report notes.

The borrower is a wholly owned subsidiary of Goose Creek, S.C.–based JW Aluminum Holding Corp., which manufactures specialty flat-rolled aluminum products. — Luke Millar/Jakema Lewis

LCD comps is an offering of S&P Global Market Intelligence. LCD’s subscription site offers complete news, analysis and data covering the global leveraged loan and high yield bond markets. You can learn more about LCD here.

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# S&P: As Oil & Gas Rebounds, US Distress Ratio Sinks to Lowest Level Since 2014

The U.S. distress ratio has dropped to its lowest level since September 2014, tightening to 6.5% in January, from 7.4%, amid strengthening commodity prices, according to S&P Global Fixed Income Research.

“The oil and gas sector continued to improve throughout 2017 as hydrocarbon prices recovered and stabilized,” noted Diane Vazza, head of the S&P Global Fixed Income Research group, in a Feb. 1 report titled “Distressed Debt Monitor: Strengthening Commodities Sectors Compress The Distress Ratio To Its Lowest Level Since 2014.”

“Accordingly, since their highs in February 2016, the distress ratios for the oil and gas and metals, mining and steel sectors have steadily decreased,” Vazza said.

Moreover, the oil and gas sector accounted for the highest month-over-month decrease in the number of distressed credits, moving to 15, from 23. As such, the oil and gas sector’s distress ratio decreased to 7.9% as of Jan. 15, from 88.5% as of Feb. 16, 2016.

The outlook for the oil and gas sector in 2018 is generally stable, reflecting a continued flattening of oil and natural gas pricing, but performance will depend heavily on potential OPEC production cuts and price volatility, S&P Global says.

The distress ratio for the metals, mining and steel sector decreased to 5.6%, from 82.3% over the same roughly two-year period referenced above.

Distressed credits are speculative-grade (rated BB+ and lower) issues with option-adjusted composite spreads of more than 1,000 basis points relative to U.S. Treasuries. The distress ratio (defined as the number of distressed credits divided by the total number of speculative-grade issues) indicates the level of risk the market has priced into bonds.

As of Jan. 15, the retail and restaurants sector had the highest distress ratio at 17%, followed by the telecommunications sector at 15.9%. — Rachelle Kakouris

LCD comps is an offering of S&P Global Market Intelligence. LCD’s subscription site offers complete news, analysis and data covering the global leveraged loan and high yield bond markets. You can learn more about LCD here.

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# US High Yield Bond Funds See \$1.1B Investor Cash Withdrawal

U.S. high-yield funds recorded an outflow of roughly \$1.1 billion for the week ended Jan. 24, according to weekly reporters to Lipper only. This week’s outflow follows last week’s exit of roughly \$3.1 billion, and brings the total outflow from high-yield funds so far this year to about \$1.4 billion.

ETFs made up the bulk of this week’s outflow, with an exit of roughly \$621 million, while \$510 million was pulled out of mutual funds.

The four-week trailing average widened to negative \$342 million, from negative \$119.5 million last week.

The change due to market conditions this past week was an increase of \$123.5 million. Total assets at the end of the observation period were \$207.8 billion. ETFs account for about 24% of the total, at \$50.3 billion. — James Passeri