As commodity pricing resets to $50.00/$3.00 ($49.62 WTI/$2.99 NYMEX as of the close on March 9, 2017), I think now is an appropriate time to break out a data workbook I've been analyzing for several months in anticipation of oil pricing/volatility reflating spatial risk. Since Q3 2016, I've tracked a general, indiscriminate risk-deflation taking place across all risk tranches of the Energy Complex, inclusive of the stressed space (i.e., inclusive of CCC-rated credit).
The general risk-deflation, which refers to valuation-multiple expansion across a space on a widespread, generally indiscriminate basis (also referred to as a "valuation-reflation"), has been most apparent in the high-yield bond markets. That's where even CCC-rated credits have seen junior debt (referring to debt without apparent value-recovery claim in a restructuring) trading to par (i.e., 100 cents on the dollar). Sometimes, although not entirely accurate in all instances, the above dynamic can be generally described as a "bubble."
This dynamic has taken place, by the way, not into a progressively higher oil and/or natural gas pricing deck - but into one that has simply stabilized. It should be noted that the described "oil pricing stabilization" has taken place at pricing levels that still call for many Energy Complex names to routinely financially engineer obligation-tethered cash outflows, general liquidity, and/or capex balances.
Given the circumstances providing the foundation of the overall dynamic - the indiscriminate risk-deflation - there could be a capital destructive unwind to the valuation-reflation having taken place. Some names - notably (but not exclusively) Marcellus and Utica-based E&Ps - have already experienced dramatic drawdowns to debt and equity pricing. All names modeled and all stress-data modeled can be viewed on a point-by-point basis - for specificity - via a link provided below. For brevity (and due to platform constraints), I'll refrain from addressing any particular name within this article.
Based on my observations, presented below, we're likely looking at a dynamic of winners and losers on a go-forward basis - at least for the midterm. Put another way, if an investor is exposed to an Energy Complex enterprise that has seen a valuation-reflation simply as a result of valuation-multiple expansion, the investor is going to find this out quickly and in a punitive fashion.
I would recommend market participants assess 1) the defensive capacity of each Energy Complex investment (i.e., how tethered the enterprise is to commodity pricing), 2) what optionality the investment's capital structure has in destressing efforts, should it come to that, and 3) what distance the investment is to covenants and/or any other default triggers (as well as what remedies the enterprise has for curing any deficiency). I would highly recommend a top-down (i.e., capital-structure-down) analysis, given the uncertainty into future pricing decks.
Notes on the Charts Below
- Data points are comprised of the 95 Energy Complex names within the ATLAS SandDance Index.
- Data points are clustered into a uniform grid of hexagons that effectively structure statistical bins expressing risk.
- Rather than relying on size to indicate differences in values like a bubble chart, the hexbin plot instead uses variation in bin color, like a heat map (a higher saturation - or darker color - indicates higher density when working with measures).
- The x-axis represents either actual or implied credit default swap scoring/pricing.
- The y-axis represents 12-month default probabilities.
- Dots within hexagons represent individual enterprises within the ATLAS SandDance Index, and can be identified by scrolling over the dots within the live model (linked below).
- All data via the following: ATLAS Consulting, Kamakura Corp., TRACE, FINRA, Market Axess, Exchange Data International, and/or public filings via EDGAR.
- A live workbook has been published online.
Note how starting in September 2016, and continuing to the most recent period of February 2017, the data points cluster tighter and tighter into the intercept of the x-axis and y-axis (i.e., to the lowest point of expressed risk). This is made clearer in that the saturation of the bins (or statistically significant data ranges), near the intercept of the x-axis and y-axis, becomes more and more exaggerated over time. This indiscriminate risk-deflation - regardless of credit rating, commodity exposure, covenant distance, leverage profile, etc. - is what I've described in the introduction above.
Again, this could make for a historically capital destructive unwind (i.e., risk-reflation).
Disclosure: I/we 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 (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.