Breaking Bread with Beta – Harnessing Perceived Volatility to Improve Risk Adjusted Outcomes

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In our first paper, The Collapse of Common Sense we documented that firms with the lowest EBITDA/net-debt ratios and low free-cash-flow yields and poor returns on assets were showing up as being less risky than firms with the highest levels of EBITDA relative to net-debt which had atypically high FCF yields and returns on assets. In our follow-up paper The Collapse of Common Sense II we worked on how best to utilize the Kailash ranking methods to try and exploit the best and worst of these “Durable” and “Fragile” firms.1 https://kailashconcepts.com/wp-content/uploads/2021/11/icons8-vertical-line-50.pnghttps://kailashconcepts.com/wp-content/uploads/2021/11/icons8-lock-50.png Subscribe now to unlock the full content 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YOU ARE NOW READING PREMIUM MEMBER LEVEL CONTENT Revisiting The Collapse Figure 1 below shows that since writing The Collapse of Common Sense, the most Economically Fragile names have seen their Betas plunge to never-before-seen lows. At the time of our original report, Economically Fragile firms had uncommonly low Betas around 1 which have since fallen to 0.79, a ~2 sigma standard deviation variance from history. We have republished Fig. 2 to remind our Kailash partners of the incredible dispersion in fundamental health between the Economically Fragile and Economically Durable Firms. Looking at the spreads in free-cash flows, returns on assets and levels of net debt to price, the idea that these Economically Fragile firms could sport risk statistics a solid 20% lower than their Durable peers seems difficult to believe. At no time in history have investors ascribed such calm price performance to a group of stocks so encumbered with net-debt while producing such meagre cash-flow yields and returns. We continue to believe that Beta may have risk “backwards” and that this represents opportunity for those willing to hew to fundamentals rather than price action. https://kailashconcepts.com/wp-content/uploads/2021/11/icons8-vertical-line-50.pnghttps://kailashconcepts.com/wp-content/uploads/2021/11/icons8-lock-50.pngUpgrade your subscription now to unlock the full content Revisiting The Collapse Figure 1 below shows that since writing The Collapse of Common Sense, the most Economically Fragile names have seen their Betas plunge to never-before-seen lows. At the time of our original report, Economically Fragile firms had uncommonly low Betas around 1 which have since fallen to 0.79, a ~2 sigma standard deviation variance from history. We have republished Fig. 2 to remind our Kailash partners of the incredible dispersion in fundamental health between the Economically Fragile and Economically Durable Firms. Looking at the spreads in free-cash flows, returns on assets and levels of net debt to price, the idea that these Economically Fragile firms could sport risk statistics a solid 20% lower than their Durable peers seems difficult to believe. At no time in history have investors ascribed such calm price performance to a group of stocks so encumbered with net-debt while producing such meagre cash-flow yields and returns. We continue to believe that Beta may have risk “backwards” and that this represents opportunity for those willing to hew to fundamentals rather than price action. While we feel our work on Economically Fragile and Durable firms in those two prior papers served their purpose we began to wonder about Beta as a stand-alone factor. If Beta could “look” at firms with precarious capital structures, poor returns and nominal free cash flows and deem them less risky than highly profitable firms with conservative capital structures and ample free cash flows, we wondered if Beta might be a valuable input in helping us identify mispricings within our own models. In this paper we decided to begin this line of research using the most simple interactions we could think of: 1. We took the 10 Highest Beta firms in the Top 100 Aggregate Rankings each month to composite and build portfolios of “Top Rank, High Beta Firms” 2. We took the 10 Lowest Beta firms in the Top 100 Aggregate Rankings each month to composite and build portfolios of “Top Rank, Low Beta Firms” Doing this we were able to view the excess returns that flowed off the most and least volatile firms with Top Ranks in Kailash’s fundamentally driven ranking engines. With those conclusions in hand we replicated the exercise among High and Low Beta firms at the bottom of our Rankings in a manner that mirrored the above: 1. We took the 10 Highest Beta firms in the Bottom 100 Aggregate Rankings each month to composite and build portfolios of “Bottom Rank, High Beta Firms” 2. We took the 10 Lowest Beta firms in the Bottom 100 Aggregate Rankings each month to composite and build portfolios of “Bottom Rank, Low Beta Firms” In brief, the conclusions are as follows: 1. On the long side: Historically there has been substantial value in volatility when seeking winners in our long-book. Fundamental investors who tackle the ranks’ most volatile firms would have received outsized payoffs even when adjusting for risk. 2. On the short side: Our analysis shows that when shorting, fundamental investors have better risk adjusted outcomes targeting Low Beta firms at the bottom of our ranks. As folks who are all too familiar with the unforgiving math of a shortgone-wrong however, we believe practitioners would be wise to heed the risk adjusted results which show the value of humility. The Long of It: The Value of Volatility Looking at Figs. 3 and 4 below we show the batting averages and payoff structures of our Top Ranked portfolios built from the 10 Highest and Lowest Beta stocks within the top 100 ranked names in both the Russell 1000 and Small & Mid Cap Universes. Looking at the left two bars in Fig. 3 shows that in the R1000 Universe, no matter if we build the portfolios from High or Low Beta stocks we have historically generated powerful 12 month rolling batting averages approaching 70%. Looking at the R1000 Universe bars in Fig. 4 (left four bars) one can see the beneficial asymmetry in payoffs for these Top Rank High and Low Beta portfolios. What is notable is that the Top Rank High Beta portfolios outperform by nearly 2.5x the amount by which they underperform. Not only do they tend to win at very high rates, but when they do so the payoff is much greater than the underperformance they suffer when they lose, a highly attractive risk/reward. Looking at the right two bars in Fig. 3 we can see a similar outcome in our Small & Mid Cap Universe. Both Top Rank High and Low Beta portfolios have historically generated strong monthly batting averages. We would be remiss however if we failed to draw your eye to the fact that in Small & Mid Cap, the case for pursuing high volatility longs is further enhanced by the fact that the Top Rank High Beta portfolios have a 16% advantage in batting average over Kailash’s SMID Top Rank Low Beta portfolios. Looking at the right four bars in Fig. 4 we can see this substantial advantage to batting averages is further amplified by the payoff structures for Top Rank High Beta vs. those for Top Rank Low Beta. The outperformance for Top Rank High Beta when the portfolios win approaches 19% or nearly 2x when they lose in the Small & Mid Cap Universe. Figure 5 shows the aggregate excess returns to our Top Ranked High and Low Beta portfolios in both the R1000 and Small & Mid Cap Universes. The difference is startling in its magnitude. Our Top Rank High Beta longs outperform our Top Rank Low Beta peers by over 2x (8.4% vs. 3.7%) in the R1000. In Small & Mid Cap, the effect is even more pronounced with the Top Rank High Beta portfolios outperforming our Top Rank Low Beta counterparts by over 4x (10.5% vs. 2.4%). Looking at Fig. 6 we can see that even adjusting for risk, in both the R1000 and Small & Mid Universes, the rewards to shoping Top Rank High Beta names has historically generated substantively higher information ratios than Top Rank Low Beta peers. For fundamental practitioners using the Kailash Ranking engines to help prioritize work-flow, there is solid empirical evidence endorsing the pursuit of higher Beta names when researching long candidates for portfolio inclusion. Conclusion: In looking at our longs in the R1000 or the Small & Mid Cap Universes we find the following to be true 1) Batting averages are strong for portfolios built out of the 10 Highest and Lowest Beta firms within our top 100 ranks 2) The payoff structures to both groups are asymmetrical further enhancing the benefit of those batting averages 3) Most importantly: historically the excess returns flowing off our Top Rank High Beta names have substantially exceeded the incremental realized risk and that, for investors seeking winning stocks in our top 100, they should have a bias to higher rather than lower beta picks. FOR OUR ANALYSIS OF POTENTIAL SHORT CANDIDATES PLEASE TURN TO THE NEXT PAGE The Short of It: The Value of Humility Looking at Figs. 7 and 8 below we show the batting averages and payoff structures of our Bottom Ranked portfolios built from the 10 Highest and Lowest Beta stocks within the bottom 100 ranked stocks in the Russell 1000 and Small & Mid Cap Universes. For the sake of clarity we would like to note that we view this side of the analysis from the lens of being short. That means in Fig. 7 low batting averages are good as the portfolios overwhelmingly underperformed the index. That also means that in Fig. 8, positive excess returns imply poor performance from the shorts while negative excess returns are desirable as that means the portfolios are falling faster than the market. Directionally then we have left the signs in the exhibits below the same as in the analysis for the longs but it is critical the reader remembers that the out or underperformance is at the portfolio level and needs to be inverted if thinking about it as a short seller. The left two bars in Fig. 7 show that in the R1000 Universe, the Bottom Rank High Beta stocks (first left bar) has a far better batting average when compared to the still poor batting average of the Bottom Rank Low Beta stocks (second bar on the left). In the R1000 Universe our Bottom Rank High Beta shorts bat a staggering 26%, meaning 74% of the time they underperform, giving you a 13% advantage over their Bottom Rank Low Beta peers. These Bottom Rank High Beta stocks offer a truly rich pond to seek shorts. Unfortunately, looking at the R1000 Universe bars in Fig. 8 we see the danger inherent in our Bottom Rank High Beta shorts. While these short portfolios overwhelmingly underperform to the benefit of short-sellers, when they fail to do so the penalty can be severe with short portfolios outperforming 27.2% on an excess basis to the detriment of short-sellers. For Bottom Rank Low Beta shorts, when they outperform to the detriment of short-sellers, you lose 8.7% on average while when the picks underperform to the benefit of short-sellers, they tend to underperform the market by 12.9%. The Bottom Rank Low Beta cohort has historically provided fundamental researchers both superb batting averages as well as highly beneficial payoff structures. Simplistically, our Bottom Rank Low Beta picks provide the best of both worlds to researchers and show the value of humility in the space. Looking at the batting averages for the Small & Mid Cap Universe in Fig. 7 (right two bars) one can see a similar yet more consistent outcome. In both Bottom Rank High and Low Beta, the portfolios have historically lost frequently batting a superb 21% and 24%. This means over 75% of the time they have gone on to historically underperform to the benefit of short-sellers! This advantage for short sellers is further amplified by the payoff structures, shown in Fig. 8, where the losses to the shorts are equal to the gains in the case of the Bottom Rank High Beta group. With near even payoffs, this means short-sellers working in our Bottom Rank High Beta portfolios are working to exploit the batting average alone. With that said the Bottom Rank Low Beta group in Small & Mid provides the best of both worlds. As can be seen in Fig. 8, not only do you get the substantial benefits of the group’s batting average you also end up with payoff structures where short sellers win by 2x as much as when they historically have infrequently lost (15.1% vs. 7.2%). Figure 9 shows the aggregate excess returns to our Bottom Rank High and Low Beta short portfolios in both the R1000 and Small & Mid Cap Universes. The difference is startling in its magnitude. In the R1000 Universe, our Bottom Rank High Beta shorts underperform their Bottom Rank Low Beta peers by 1.5x (-6.6% vs. -4.5%). In Small & Mid Cap the separation is less dramatic as the Bottom Rank High Beta portfolios underperform by 12.3% vs 8.9% for Bottom Rank Low Beta portfolios. When you couple numbers like these with the batting averages above it is easy to be tempted to pursue the Bottom Rank High Beta shorts. As experienced short-sellers who understand the brutal pain of a short gone wrong we would encourage partners of our product to examine Fig. 10 which adjusts those excess returns for risk. You can see that in both the R1000 and Small & Mid Cap Universes, there is tremendous value to humility when pursuing shorts. In both cases we would note that the Bottom Rank Low Beta short portfolios consistently put up substantially higher risk adjusted returns than their Bottom Rank High Beta counterparts2. While few portfolios in the bottom Rank High Beta world go on to outperform, in the rare instances they do, the pain is non-trivial. Conclusion: In looking at our shorts in the R1000 or the Small & Mid Cap Universes we find the following to be true 1) Batting averages are strong for short portfolios built out of the 10 highest and lowest beta firms within our bottom 100 ranks 2) Unfortunately, the payoff structures on the short sort side are not as forgiving as those on the long side as the penalty for being wrong has been substantially higher 3) As a result of #2 above, we find that when shorting there is great value to pursuing Bottom Rank Low Beta shorts as they provide higher risk adjusted returns than their Bottom Rank High Beta peers Exhibits As a reminder for our Financial Advisors: our models are available on a continuous basis, and most have been in production for over a decade. If you are looking for simple, concentrated, low turnover, and tax efficient model portfolios we would like to talk with you. KCR also offers a wide range of easy-to-use but sophisticated tools. Our toolkits can help identify mispriced stocks with the best and worst risk/reward characteristics, estimate a stock’s duration and warn you when a company is engaging in low-quality accounting. Over the last 12 years, KCR has built and offers time-tested and class-leading products built by experienced and proven money managers for fixed to low prices. Kailash Capital’s sister company, L2 Asset Management, runs market neutral, long/short, large-cap, and mid-cap long-only portfolios with a value and quality bias. L2 employs a highly disciplined investment process characterized by moderate concentration, low turnover, high tax efficiency, and low fees. While nobody can predict the future, we believe the recent resurgence in risk-adjusted returns seen across all products is the beginning of what may be a long period where speculation is punished, and prudence and patience rewarded. The topics discussed in this article are aimed at seasoned professionals, as such, we have included some extra for anyone seeking out more information related to the topics above.Avoid Being EnronedLCID Message BoardSun Microsystems Historical Stock PriceTight Labor MarketEquity Position Meaning We categorized “Durable” firms as those companies in the quintile of firms with the highest levels of EBITDA relative to net debt and dubbed firms “Fragile” if they were in the quintile with the lowest levels of EBITDA to net debt. Please contact your Kailash Concepts salesperson or see our earlier work if you are interested in the conclusions. As a reminder, these are shorts so larger negative information ratios mean better risk-adjusted returns Disclaimer The information, data, analyses, and opinions presented herein (a) do not constitute investment advice, (b) are provided solely for informational purposes and therefore are not, individually or collectively, an offer to buy or sell a security, (c) are not warranted to be correct, complete or accurate, and (d) are subject to change without notice. Kailash Capital, LLC and its affiliates (collectively, “Kailash Capital”) shall not be responsible for any trading decisions, damages or other losses resulting from, or related to, the information, data, analyses or opinions or their use. The information herein may not be reproduced or retransmitted in any manner without the prior written consent of Kailash Capital. 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