21 Dec, 2021
Fridson: Enhancing HY valuation with spread per point of volatility
This commentary is written by Martin Fridson, a high-yield market veteran who is chief investment officer of Lehmann Livian Fridson Advisors LLC as well as a contributing analyst to Leveraged Commentary & Data.
Spread per turn of leverage, or SPTL, has become a widely used high-yield valuation metric over the last 15 years. The concept is intuitively appealing, based on the following reasoning: The risk premium or spread-versus-Treasuries (of a bond, a sector or the high-yield index) is a function of its default risk (consisting of both default probability and expected recovery in the event of default). Default risk is in turn a function of leverage. Ergo, a high ratio of spread to "turns of leverage" (debt/EBITDA of 1.0x = one turn) implies that the asset is cheap. In the case of a bond or sector, the asset is cheap relative to its peers; in the case of the high-yield index, it is cheap relative to its historical average.
SPTL is certainly one useful metric among the many analytical tools available to high-yield analysts and portfolio managers. Like the others, though, a caveat must be attached to it: SPTL is not a panacea. Think about it. If calculating leverage and dividing spread by it were all there was to high-yield security selection, most high-yield analysts would be out of work, replaced by robots.
It is not even the case that financial ratios, including but not limited to SPTL, constitute a sufficient basis for assessing credit risk and determining relative value. Like some other popular metrics, SPTL incorporates backward-looking data; trailing-12-months EBITDA is the leverage ratio's denominator. Risk is a function of historical financial results only to the extent that the historical results are indicative of future results. That connection is sometimes quite loose, particularly when a company faces novel competitive pressure, technological disruption, or potential regulatory penalties, antitrust actions, or environmental damage judgments.
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Useful though SPTL can be on the path toward reaching a valuation conclusion, it should not be mistaken for a one-step solution. This is not to imply that anyone producing SPTL data contends that it is, but we humans have a natural tendency to seek the silver bullet. We crave the single spiritual principle that will enable us to live a good life, the one theory that explains all aspects of the physical universe, the unique trading rule that enables us to outperform the market (see note 1). SPTL cannot qualify as that third-named object of desire unless someone presents irrefutable evidence of alpha generation through sole reliance on it for security selection, sector rotation or high-yield market timing. Until then, analysts and portfolio managers must regard SPTL merely as a valuable adjunct to other items in their toolbox.
Another piece of the puzzle
To demonstrate that financial ratios such as SPTL are only part of the valuation story, we utilize a volatility measure discussed in "Rethinking industry allocation," namely, standard deviation of monthly total return over the preceding 10-year (120-month) span. Anyone who is familiar with Modern Portfolio Theory and sees frequent mentions of beta in equity research might guess that historical volatility has some bearing on high-yield risk premiums.
That surmise receives some validation from the table below, which covers the 20 largest high-yield industries by market value. The left-hand side ranks the industries by our volatility measure and displays the industries' respective risk premiums as measured by option-adjusted spread. Within this modest sample, to be sure, the correlation between volatility and spread is not particularly high, at 22.3%. Nevertheless, the notion that there is some connection between the two metrics is supported by the fact that the six widest-spread industries all rank in the top half by standard deviation of total return. With 95% confidence, we can describe that as a nonchance outcome.
Astute readers will ask, "Is your volatility measure not just a proxy for leverage? We would expect the most highly leveraged industries to experience the biggest swings in earnings. If leverage and volatility are one and the same, you have not truly identified an influence on spreads that is distinct from financial ratios." That is an excellent question, to which we have an answer.
Bloomberg ranks broad industry sectors by leverage, defined as total debt/EBITDA. The third-quarter rankings are displayed in the following table. Rankings by volatility of certain industries within these sectors diverge substantially from their rankings by leverage. For example, Communications is the most leveraged industry sector, yet Broadcasting (1.844%), Cable & Satellite TV (1.449%) and Telecommunications (1.806%) all rank in the bottom 55% by volatility. Similarly, the Consumer-Noncyclical sector is just below median by leverage, yet Food, Beverage & Tobacco (1.303%) ranks in the bottom 10% by volatility. Some other industries do rank similarly by leverage and volatility, but the exceptions support the case for regarding historical volatility as a spread determinant that is not exclusively a function of financial ratios.
The right-hand side of the first table details the volatility-spread relationship with a new metric, spread per point of volatility, or SPPV. By this analysis, Metals & Mining and Energy currently offer the very worst risk-reward tradeoff among all major high-yield industries. That conclusion contrasts starkly with the key finding of a Dec. 16 Bloomberg Intelligence report that miners and energy were among the leverage-adjusted spread outliers (see note 2). The report makes clear that those two industries are outliers in the direction of providing more, rather than less, spread per turn of leverage than their peers.
In no way do we dispute the correctness of Bloomberg Intelligence's findings. Nor do we suggest that Bloomberg Intelligence or any other purveyor of SPTL data has encouraged investors to regard SPTL as a solely sufficient basis for selecting securities. Our results simply highlight the importance of drawing on a variety of analytical methods. Suppose an industry is temporarily out of line with its historical experience according to metric A. If the industry then reverts to its mean by that metric, an unchanged or reduced ranking by metric B may prevent it from delivering the total return advantage it would be expected to provide if metric A were the sole arbiter of valuation.
High-yield divergence from fair value almost unchanged in November
Our fair value estimate for the ICE BofA US High Yield Index's option-adjusted spread barely budged last month. On Nov. 30, it stood at +446 basis points, down just 1 basis point from +447 bps on Oct. 31. The index's OAS was even more stationary, standing pat at +315 bps. Consequently, the actual-minus-estimated differential decreased by a single basis point, from -132 bps to -131 bps (see the following chart). That left the asset class at an extreme overvaluation, defined by one standard error in our regression model, or -124.5 bps. As of Dec. 16, the index's actual OAS had widened to +332 bps, pushing the divergence inside the extreme overvaluation boundary at -114 bps.
We attribute the wide gap to "quantitative easing on steroids," i.e., monetary policy more accommodative than even the extraordinary stance implemented in the wake of the 2008-2009 global financial crisis. Our model includes a dummy variable for "ordinary" QE. It remained at 1 for "QE in force" in November. Also unchanged, at -18.2%, was our credit availability measure, which is reported only once a quarter. The current reading indicates that more banks are currently easing their standards for companies to qualify for loans than are tightening the standards. Capacity utilization rose nominally from 76.5% to 76.8%, while industrial production dropped from 1.7% to 0.5%. The default rate, a backward-looking indicator with a modest impact on the spread, dipped from 2.1% to 1.8%. Meanwhile, the five-year Treasury yield, which is inversely correlated with the spread, declined a smidgen from 1.18% to 1.15%.
These conclusions are drawn from the updated methodology presented in "Fair Value update and methodology review." In brief, we find that 80% of the historical variance in the ICE BofAML U.S. High Yield Index's OAS is explained by six variables:
* Credit availability, derived from the Federal Reserve's quarterly survey of senior loan officers.
* Capacity utilization.
* Industrial production.
* Current speculative-grade default rate.
* Five-year Treasury yield.
* A dummy variable for the period covered by quantitative easing.
Each month we calculate a Fair Value spread based on the levels of these six variables. The extent of high-yield overvaluation or undervaluation is determined by the difference between the actual OAS and the Fair Value number ("estimated"). We define material under- or overvaluation as a divergence of one standard deviation (124.5 bps) or more from Fair Value. The monthly difference between the actual and estimated OAS is tracked in the chart above.
Research assistance by Manuj Parekh and Weiyi Zhang.
ICE BofA Index System data is used by permission. Copyright © 2021 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.
Notes
2. Noel Hebert, "High yield fundamentals vs. spread: 3Q21," Bloomberg Intelligence (Dec. 16, 2021).