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As Coronavirus spreads throughout the states, we should be prepared for continued market swings and disruptions to our daily lives. We may well be on the verge of a generational shift in markets. All of those who have been used to buying the dip over the past 7-10 years have (so far) found that strategy wanting. It is common to get big rallies during bear markets with the combination of short covering and market players fearing they will miss the bottom. This has been pronounced over the past week. Those buying intensely are miscalculating the risk.
Oddly enough, the trade war may be softening the blow of what would have been a massive shock to global supply chains. Many complained about supply chain disruptions, a trivial matter now. A "smoother" 2018-19 trade scenario would have provided a false sense of certainty. This is an example of why volatility is not always bad. Volatility is bad when hidden (which is what most seem to prefer) and when one is completely exposed to its downside.
Barring a 180 degree shift in current trajectory (i.e. unless we get a vaccine in the next few weeks), the slow and reluctant shift in the supply chain will shift to a mandatory sprint. This will be good in the long-term, boosting investment and bringing jobs to the midwestern and southern United States. But it will be painful in the short-term. The needed capital expenditures will be massive. Earnings and cash flows will suffer. As we have said, this will create significant opportunity for the patient, well-positioned investor (or the few who are left!).
A few notes (and links)
Revenue and margin declines could easily catalyze a downward spiral in credit, something Ed Yardeni has been posting about recently. 🔗 Noted bond investor Jeffrey Gundlach says it will be hard for jobless claims to remain low if the travel situation continues. 🔗 Wharton professor Jeremy Siegel sees this as a "one-year severe shock" and thinks 2020 is going to be pretty ugly. 🔗 Buying the dip doesn't always work. In fact, historically it has been a losing strategy. 🔗
The legendary venture capital firm Sequoia sent out a memo to its founders and CEOs warning it may take several quarters "before we can be confident that the virus has been contained" and "even longer for the global economy to recover its footing." 🔗
Having weathered every business downturn for nearly fifty years, we’ve learned an important lesson — nobody ever regrets making fast and decisive adjustments to changing circumstances. In downturns, revenue and cash levels always fall faster than expenses. In some ways, business mirrors biology. As Darwin surmised, those who survive “are not the strongest or the most intelligent, but the most adaptable to change.” A distinctive feature of enduring companies is the way their leaders react to moments like these. (Sequoia - March 2020 memo)
Sequoia also made a presentation is 2008 entitled "R.I.P. Good Times." 🔗
A note about normally distributed vs. fat-tailed systems
The argument that certain things cause more deaths than COVID-19 does not hold-up under even mild scrutiny. Deaths from what we are aware of and understand (e.g. common cold, flu, car crash, cancer) are linear, non self-replicating, non-multiplicative events; or they are understood well enough to be contained. They cannot be spread unexpectedly and rapidly from one or a small number of people to an extremely large group. Pandemics, on the other hand, can do far more damage virtually instantaneously because they are self-replicating, multiplicative, and spread involuntarily. Taleb calls the difference "Mediocristan" vs. "Extremistan." 🔗 In the first system, outcomes are known (within a range) and constrained.
Figure 1 below is an example of the first system—the normally distributed, non self-replicating category of events. Figures 2 and 3 are examples of the second system—the non-normal, fat-tailed category. In the normally distributed system, the risk of an extreme outlier event (> 3 standard deviation move) is virtually zero. This risk is known.
In the non-normally distributed system (Figures 2 and 3), we have simulated a 1/75 chance of an extreme event. Even though the single year probability of an event is low, the cumulative probability over the entire period is extremely high. When it does occur it is devastating—far worse than what could have been expected given estimates from other years. The risk is unknown. Any attempt to characterize the risk, say in Figure 2 over the first 45-years, only provides a false sense of security. Markets fall into this category.