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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.

Fridson Monthly 2018-02-28 table 1a

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.

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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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After Big Inflow, US High Yield Funds See $3.1B Investor Cash Withdrawal

high yield bond flows

U.S. high-yield funds recorded an outflow of roughly $3.1 billion for the week ended Jan. 17, according to weekly reporters to Lipper only.

This week’s outflow follows an inflow of $2.65 billion last week, and puts the total outflow so far this year at about $238 million.

ETFs accounted for roughly $2 billion of this week’s outflow, while $1.1 billion exited mutual funds.

The four-week trailing average swung to negative $120 million, from positive $371 million last week.

The change due to market conditions this past week was an increase of $316 million. Total assets at the end of the observation period were $208.8 billion. ETFs account for about 24% of the total, at $50.8 billion. — James Passeri

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US High Yield Funds See Massive $4.4 Billion Cash Withdrawal

US high yield fund flows

U.S. high-yield funds recorded an outflow of $4.4 billion for the week ended Nov. 15, according to weekly reporters to Lipper only.

Mutual funds made up the bulk of this week’s outflow, at $2.6 billion, while $1.8 billion exited ETFs.

The year-to-date total outflow is now roughly $13 billion, with a $14.7 billion outflow from mutual funds outweighing a roughly $1.7 billion inflow to ETFs.

The four-week trailing average is in the red for the third straight week, widening to negative $1.5 billion from negative $536 million last week.

The change due to market conditions this past week was a decrease of $1.9 billion. Total assets at the end of the observation period were $206.6 billion. ETFs account for about 24% of the total, at roughly $50 billion. — James Passeri

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US High Yield Funds See Cash Inflow Courtesy Big ETF Gain

high yield fund flows

U.S. high-yield funds recorded an inflow of $122 million for the week ended Oct. 25, according to weekly reporters to Lipper only. This comes on the heels of last week’s outflow of $450 million.

ETFs drove the action this week with an inflow of $530 million, while $407 million exited mutual funds.

The four-week trailing average fell to positive $321 million from positive $399 million last week, and has now remained in the black for six consecutive weeks.

The year-to-date total outflow is now $6.7 billion, with an $11.6 billion outflow from mutual funds outweighing a $4.9 billion inflow to ETFs.

The change due to market conditions this past week was a decrease of $216 million, snapping a streak of eight consecutive weeks of increases. Total assets at the end of the observation period were $215 billion. ETFs account for about 25% of the total, at $53.8 billion. — James Passeri

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Fridson: High Yield Bond Covenant Quality Hits All-Time Low

The covenant quality of high-yield new issues reached an all-time low in the third quarter, as measured by the FridsonVision series. Moody’s series, of which ours is a refinement, bottomed out in the second quarter. For reasons detailed below, the two series diverged in September, with FridsonVision’s showing minor improvement versus August’s reading, while the Moody’s series deteriorated slightly, month over month.

cov quality 3

To provide background, “Covenant quality decline reexamined” ($) describes how we modify the Moody’s CQ Index to remove noise arising from month-to-month changes in the calendar’s ratings mix. On average, covenants are stronger on triple-Cs than on single-Bs, and stronger on single-Bs than on double-Bs. Therefore, for example, if issuance shifts downward in ratings mix in a given month, without covenant quality changing within any of the rating categories, the Moody’s CQ Index will show a spurious improvement. We eliminate such false signals by holding the ratings mix constant at an average calculated over a historical observation period.

cov quality

The opposite of the effect described just above occurred in September (see chart below). As the double-B component expanded from 24.0% of all issues in August, to 38.2% in September, Moody’s series worsened from 4.54, to 4.59 (1 = Strongest, 5 = Weakest). Filtering out the impact of monthly variations in ratings mix, the FridsonVision series showed a similarly sized improvement from 4.59, to 4.55.

On a quarterly basis, though, the pattern was reversed. The FridsonVision series deteriorated from 4.37 in August to an all-time low of 4.44 in 3Q17. This series’ previous worst score was 4.38 in 1Q15. Meanwhile, the Moody’s series improved slightly from its all-time quarterly worst 4.49 in 2Q17 to 4.47 in 3Q17 (see chart below). That seeming improvement in covenant quality reflected an unusually heavy concentration of issuance in the double-B category in 2Q17 and a return to about an average concentration in 3Q17. – Martin Fridson

This analysis was excerpted from Marty’s regular weekly column, available to LCD News subscribers. 

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Investors Pour $967M into US High Yield Funds

US high yield fund flows

U.S. high-yield funds recorded an inflow of $966.8 million for the week ended Oct. 11, according to weekly reporters to Lipper only. This week’s result is the largest of the current four-week inflow streak and brings the total over that span to $2.9 billion.

ETFs led the way with $847 million, accounting for 88% of the total, while mutual funds recorded their largest inflow in five weeks, at $119.7 million.

With this result, the four-week trailing average rises to $727.7 million, the highest level since the first week of the year, from $462 million last week.

The year-to-date total outflow is $6.37 billion. A $4.52 billion year-to-date inflow for ETFs is far outweighed by $10.9 billion leaving mutual funds so far in 2017.

The change due to market conditions last week was positive $135.3 million, marking the seventh straight increase, the longest run since February. Total assets at the end of the observation period were $215.4 billion. ETFs account for about 25% of the total, at $53.4 billion. — Jon Hemingway

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US High Yield Bond Funds See $433M Investor Cash Inflow

high yield fund flows

U.S. high-yield funds this week saw a $433 million cash inflow from retail investors, following up on an $866 million inflow last week, according to Lipper.

This week’s gain puts the four-week moving average at $461 million, up from $284 million a week ago. ETFs are entirely responsible for the inflow, as investors poured $590 million into those entities this week, while withdrawing $157 million from high-yield funds proper.

The change due to market conditions was $137 million to the upside (a 0.7% increase); it is the fifth-straight gain from market conditions, totaling $2.2 billion over that span.

High-yield fund assets now total $213 billion, with $51.7 billion via ETFs, or 24% of the total. — Staff reports

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Investors Return to US High Yield Bond Mart with $641M Cash Infusion

high yield bond flows

U.S. high-yield funds recorded an inflow of $641 million for the week ended Sept. 6, according to weekly reporters to Lipper only. This comes on the heels of three consecutive weekly exits that combined for a total outflow of $3.5 billion over that span.

ETFs made up $364 million of the inflow this week, while $277 million flowed into mutual funds.

The four-week trailing average stayed in the red for a fourth straight week, narrowing to negative $709 million, from negative $838 million last week.

The year-to-date total outflow is $9.2 billion, with a $11 billion outflow from mutual funds offset by a $1.8 billion inflow to ETFs.

The change due to market conditions this past week was an increase of $1.1 billion. Total assets at the end of the observation period were $209 billion. ETFs account for about 24% of the total, at $50.1 billion. — James Passeri

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US High Yield Funds See $2.2B Investor Cash Inflow

U.S. high-yield funds recorded an inflow of $2.2 billion for the week ended July 19, the largest such inflow since the week ended April 5, when the total was $2.4 billion, according to weekly reporters to Lipper only.

US high yield fund flows

This inflow snaps four straight weeks of outflows from the asset class for a total outflow of $4.2 billion over that period.

ETFs made up the bulk of the inflow this week, at $2 billion. The $200 million inflow to mutual funds follows last week’s exit of $1.4 billion.

The four-week trailing average remains in negative territory for the fourth consecutive week, rising to negative $453 million, from negative $1 billion last week.

The year-to-date total outflow is $6.6 billion, with a $8.3 million outflow from mutual funds outweighing a $1.7 billion inflow to ETFs.

The change due to market conditions this past week was an increase of $1.3 billion. Total assets were $210 billion at the end of the observation period. ETFs represent about 24% of the total, at $49.7 billion. — James Passeri

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US High Yield Funds See Yet Another $1B-plus Investor Withdrawal


US high yield fund flows

U.S. high-yield funds recorded an outflow of $1.1 billion for the week ended July 12, according to weekly reporters to Lipper only. This is the fourth straight week of outflows from the asset class for a total of $4.2 billion over that period.

Mutual funds led the exit this week, with outflows of $1.4 billion outweighing inflows into ETFs of $276 million, following last week’s exit of $184 million from ETFs.

The four-week trailing average remains in negative territory for the third consecutive week, dipping to $1 billion, from $705 million last week.

The year-to-date total outflow is $8.9 billion, with an $8.5 billion outflow from mutual funds and a $319 million exit from ETFs.

The change due to market conditions this past week was an decrease of $33.9 million. Total assets at the end of the observation period were $206.3 billion. ETFs account for about 23% of the total, at $47.3 billion. — James Passeri

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