Making More from M&A – Finding Fundamental Fodder Among Economic Folly

Introduction: Traction in the tails, using fundamental facts to avoid promises and pain Exploiting the procyclical nature of M&A In the Anti-Algebra Index-Era, M&A makes the case that fundamental facts are worth figuring Exhibits Appendix: Small and Mid Cap Figures Introduction: Traction in the tails, using fundamental facts to avoid promises and pain Over the last seven years the Kailash ranking methodology has faced intermittent criticism for failing to see the “potential” in a recent acquisition after partners of the product have seen some of their favorite stocks show up at the bottom of the rankings file—and on one of the numerous short lists—after a deal. We are frequently told the numbers do not catch the revenue, cost or capex synergies that will soon rain down on the pro forma income statements, or that the vagaries of acquisition accounting are distorting the numbers and the calculation of fundamental metrics rendering them meaningless. We have often been told that we should make adjustments or just remove M&A from the rankings because there is no merit to the analysis. In this paper we will avoid delving into the maze of academic research which we believe does a superb job outlining a number of plausible explanations for the often abysmal post-deal performance of companies. With that said, we would be happy to hear your thoughts and supply relevant research that may help shed light on your particular view from the sea of academic research published on the topic. We will start with intuition painfully learned over decades in the industry that not all mergers or acquisitions are created equal, that oftentimes synergies and cost savings are not realized and that the quality of a firm as an investment candidate can best be seen through the dispassionate lens of post-acquisition data. The money managers at Kailash carry deep scars from younger days when they were more easily enamored by the purported skill of a management team’s acumen and the ability to assess such things. In hindsight we have learned to recognize that, like all human beings, we are prone to both overconfidence and overoptimism when it suits our predispositions. In order to examine whether or not the anecdotal observations would prove out under the merciless lens of historical review, we decided to analyze the constituents of the Kailash monthly short and long picks on the basis of whether or not they had completed an acquisition within the last year and how these firms performed over the next 12 months. We used four buckets for the purposes of our analysis: Short Acquirers: stocks in the monthly short picks that had closed an acquisition in the last year Short Non-Acquirers: stocks in the monthly short picks that had NOT closed an acquisition in the last year Long Non-Acquirers: stocks in the monthly long picks that had NOT closed an acquisition in the last year Long Acquirers: stocks in the monthly long picks that had closed an acquisition in the last year As Figure 1 shows, when the model does not like a post-acquisition equity (first bar) the results are catastrophic with the average Large Cap Short Acquirer underperforming by over 9%. This is a powerful finding as can be seen by the second bar to the right which is how the rest of the stocks in our short book perform. While 6% underperformance is a powerful number for a short, the 9%+ underperformance of the post-acquisition shorts would imply that these firms are an even more fertile area for short-sellers to hunt and an even more precarious group for long-only managers to traffick in. While somewhat less compelling, we would also note that acquirers in the long picks go on to outperform the market by modestly larger amounts than other long picks. 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 Figure 2 looks at the composition of those returns by taking the monthly post-acquisition picks and looking at how often and by how much they outperformed and underperformed the market. For the purposes of our paper, “outperformance” is always GOOD for a long pick and BAD for a short pick, and “underperformance” is BAD for a long pick and GOOD for a short pick. In the first row you can see that when an acquirer is a long pick, it historically outperforms 57% of the time by an average of 27% and underperforms only 43% of the time by 18%. So when a pick is a post-acquisition firm on the long side we get the benefit of both batting averages and assymetrical payoffs (you win more often and when you win you win by more than when you lose). 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 Figure 2 looks at the composition of those returns by taking the monthly post-acquisition picks and looking at how often and by how much they outperformed and underperformed the market. For the purposes of our paper, “outperformance” is always GOOD for a long pick and BAD for a short pick, and “underperformance” is BAD for a long pick and GOOD for a short pick. In the first row you can see that when an acquirer is a long pick, it historically outperforms 57% of the time by an average of 27% and underperforms only 43% of the time by 18%. So when a pick is a post-acquisition firm on the long side we get the benefit of both batting averages and assymetrical payoffs (you win more often and when you win you win by more than when you lose). In the second row you can see that when an acquirer ends up in the short picks, it historically outperforms only 40% of the time and underperforms (again, a good thing in a short) 60% of the time. The difference with the short acquirers is that the composition of the underperformance seen in Figure 1 is being driven by the batting average as the out/under-performance figures are roughly equivalent at ~30%. A member of Kailash is fond of noting that “Las Vegas was built on a one or two percent advantage in batting average” and would likely argue that trying to pick stocks from a pond where, historically, you have a 20 percentage point disadvantage is tough work that is better left to other, less discriminating investors such as index funds and ETFs. Exploiting the pro-cyclical nature of M&A While the academic literature is expansive and loaded with nuance, conceptually we are leery of acquisitions whose value is predicated on fundamental improvements and promises that have yet to be realized. Figure 3 shows the six-month rolling number of monthly picks in the long (blue line) and short picks (red line) and we would note that, consistent with our initial comment, acquirers are more likely to be among the short picks rather than the long picks. Over the last 30 years, there have been an average of six acquirers a month in the long picks and nine a month in the short picks. Both averages are susceptible to significant variance with our long and short picks having historical standard deviations around those means of three and six respectively based on the ebb and flow of M&A cycles. In Figure 4 we show the number of short picks (LHS) overlaid wither Shiller P/E (RHS). We are not sad to see that when M&A was immensely pro-cyclical at historic peaks at the height of the internet and housing bubbles, the short picks were not afraid to bet against the promises that would inevitably help sow the seeds of the ensuing market disasters. Similarly, looking at 2008-2009, we are pleased to find that the data held the necessary information to guide the short books away from acquirers, seemingly recognizing that deals made at 20-year lows in valuation might be more substantive and therefore more likely to succeed. With an 84% correlation, the short book does a nice job syncing up with the Shiller P/E despite its total absence from our model’s loadings. Figure 5 follows the same format as Figure 4, but places the number of acquirers in the long picks agains the Shiller P/E. Here too we can see that the model does a solid job determining when acquisitions are opportune rather than ill-timed pro-cylical behavior occurring around market peaks. Even more impressive in our minds is that the number of acquirers in the long picks hit two consecutive peaks as the Shiller P/E came crashing down in 2009. Again, despite the Shiller P/E having no weighting in the Kailash Rankings, the long book saw unusually large amounts of fundamental value in the deals being done at record lows during the 2008-2009 crash and the model was stepping in and upping exposure to acquirers with the discipline and resources to make deals at generational lows in market prices. In the Anti-Algebra Index-Era, M&A makes the case that fundamental facts are worth figuring Like many of our partners, we understand too well the stark implications of the seemingly endless appetite for passive products and a market that seems convinced equity risk premiums will remain forever suppressed. There are moments when momentum is nearly the only functioning behavioral anomaly and one gets the impression that even simple algebra has been dropped from the investment toolbox. While uncomfortable in the moment, we believe when the passive mania finally climaxes and/or simple algebra inflicts its merciless will upon capital-deficient operators, fundamental practicioners with discipline will reap rewards akin to those seen in the pre Reg FD era. We admit this may seem an odd tangent in a paper about our ability to create value from M&A on both the long and short sides. We raise the issue because in reviewing the fundamental underpinnings that drive the separation between acquirers that the kailash models like and acquirers that the models do not like, the data is so stark the mere fact that spreads like this can emerge is a testament to the ability of skilled narrators to sabotage even the most educated minds by pitching what is obviously very poor M&A activity. In the interests of optical clarity, for the ensuing eight charts we have condensed the groupings to just Stock Universe, Short Pick Acquirers and Long Pick Acquirers. Our hope is that this will minimize the noise and help better highlight the substantial separation that occurs between acquirers the models like and acquirers the models wish to avoid or be short. Looking at Figure 6 and Figure 7 you can see that the average dispersion in valuation is simply astonishing. Free cash flow and earnings yields of acquirers the models dislike are dramatically lower than the universe average while those acquirers the models like typically carry a tremendous advantage over the short acquirers and even beat the universe average. One of the most powerful drivers of the dispersion between acquirers in the short book and acquirers in the long book is the almost unbelievable spread between the two groups’ asset efficiency. Looking at Figure 8, the Long Acquirers have an ROE advantage over the Short Acquirers of nearly 20%pts. In and of itself we would argue that, when combined with the huge discounts the Long Acquirers trade at as noted above, you are probably off to a good start from a research and investment standpoint. Loooking at Figure 9 serves to further drive that point home. That 20%pt advantage in ROE is achieved despite an astonishing 10%pt lower (gross) level of net debt to price. Quite simply, for the models to like an acquirer enough to make it a pick, it will typically trade at a radical discount to an acquirer disliked and a substantial discount to the Universe yet have a far more robust balance sheet than either the Short Acquirers or the Universe and still be capable of generating market beating ROEs. While we believe the combination of valuation and asset efficiency described above is more than enough to encourage fundamental investors to aggressively research acquirers that show up in both the long and short picks, remarkably, we find that the story simply continues to improve as we delve into assessing competitive moats. Looking at Figures 10 and 11 we show the differences in Short and Long Acquirers’ capex to sales and R&D to sales. These are just simple items the models use as proxies for how a firm attempts to build an economic moat and what sustains and maintains that moat. Similar to the data above, the dispersion is so obvious one almost wonders if it needs written description. Figure 10 shows that acquirers the models want to short tend to have capex requirements that eclipse the Universe average and dwarf the needs of acquirers we like. Figure 11 shows that while acquirers that are potential shorts tend to have Universe average levels of R&D spend relative to sales, those acquirers that the models pick are substantially higher. Figures 12 and 13 below seem to add insult to injury. Despite having negative free cash flows on average, the Short Acquirers still pay out roughly a third of the Universe average level of dividend yield. As we explained in our prior White Paper A Deeper Dive into Payments to Owners, spending money you do not have does not just clash with intuition, it augurs poorly for equity owners. Yet we believe there may be some deception at work here. Looking at Figure 13, the acquirers the model prefers to short pay their owners a taxable dividend that is sourced from non-trivial dilution of those same owners. This appears to us like a cardinal sin of capital allocation: you rob your equity owners via quiet dilution, distribute a small portion of the capital back to them via a dividend and then force them to pay taxes? Contrast this behavior with the Long Acquirers – they tend to pay just above the Universe level of dividends (~2%) to create cash coupon for owners and then return what they deem surplus capital tax efficiently through buybacks (~2.7%) for an average historical shareholder yield of 4.7%. That number seems both impressive yet conservative when we look back at Figure 6 and note these firms have typically have FCF yields in excess of 8%. Conclusion: Figure 14 demonstrates that deal activity is up nearly 150% from the nadir of 2009 and within 3% of the prior 2007 peak. It’s important, given this level of activity to have the right framework to evaluate deals. Our conclusions are as follows: M&A is populated by both good and bad deals and our models have done a good job of sorting the wheat from the chaff. On the long side: the Kailash ranking methodology does a good job identifying companies post-acquisition that merit significant fundamental research due to their tendancy to trade at sharp discounts to the market despite atypically healthy capital structures, impressive asset efficiency, capital lite economic moats whose defenses appear to be primarily based on technological investments and their responsible but meaningful payments to owners. On the short side: the Kailash ranking methodology does a good job identifying companies post-acquisition that merit significant fundamental research due to their unsustainable valuations, horrendous capital efficiency, capital-intensive businesses and their optically pernicious approach to equity owners where they create needless tax liabilities by returning a small amount of the money they generate by diluting those same owners via equity issuance. M&A is a space highly susceptible to story telling due to the human propensity to overestimate both their own forecasting abilities as well as investors’ appetite for well-told narrative. By eliminating the noise and promises our process dispenses with the narrator and use a dispassionate and data-driven lens to try and point investors in the right direction. What we find is that while the sample size is admittedly unstable, that instability is both justified and effective at identifying acquirers most likely to go up or blow up. Please find the comparable charts for Small & Mid Cap in the appendix below. While somewhat less compelling than the Large Cap universe; however, the outcomes and causes are largely similar. Exhibits Appendix: Small and Mid Cap 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. 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