A wise man observed that figures don’t lie, but liars figure. Failing to embrace the importance of that idiom makes idiots of the unwary. In our reflection on uncertainty, study our figures (and those of others) skeptically, even if you trust us. As for the charts in this post, go figure.
Let’s begin with a figure familiar to virtually everyone. Frequency distributions are a way of summarizing unorganized data (Figure 1). Risk managers are concerned about adverse investment outcomes, at the far-left end of a distribution. In terms of
the ubiquitous bell curve shown, only three in 1,000 events occur outside three standard deviations to the left of the mean (the reciprocal of 99.7%, which, while admittedly splitting hairs, includes the area to the right of the mean as well). That’s not very many data points. Why, therefore, should we worry about them?
Well, despite its broad application, the bell curve in Figure 1 does not serve well those who wish to mitigate risk when applied to complex, man-made systems, such as the economy or capital markets. To be sure, it’s good for graphing, say, the height or weight variance of a group of randomly selected men/women or the outcome of 1,000 coin tosses. For most readers, it’s intuitive that the mathematical average (mean) will always be close to the median (the midpoint between the high and the low) in these normal distributions. The bell curve provides misleading inferences, however, when applied to distributions where the mean and median differ significantly. Footnote 1 offers an example of the inappropriate use of the bell curve to show the distribution of the net worth among 1,000 men and women when there are outliers among them.[1]
On rare occasions the market, and with much greater frequency for industry-group subsets, has imploded catastrophically. These are black swans or outlier events. If a long history of positive investment returns could turn into catastrophic losses because of the market or a sector meltdown, if a bad year could wipe out most of what you’ve gained, that’s something we worry about. Are we liars figuring, or might it be worth your while to read on?
Assuming you have figured us out, we might propose that a line be drawn in the sand. To wit: We believe uncertainty is risk that can’t be quantified. We don’t simply want to outlast uncertainty, to just squeeze by. We want to overcome the unseen, the hazy, the seemingly inexplicable. We aspire to profit from extreme uncertainty. That’s what Part II will address in a few weeks.
Part I gets to the core issue, uncertainty. Webster’s synonym: unpredictable. The market and economic environment have become progressively—as well as insidiously—less predictable and thus more fragile, a synonym popularized by Black Swan and Antifragile author, Nassim Taleb. Fragile complex systems, asserts Taleb, are susceptible to catastrophic failure under conditions of uncertainty.
The behavior of complex systems is inherently difficult to model because of the dynamic interaction of its component parts. Complex systems have perplexing properties that arise out of those relationships, often described with esoteric words such as nonlinearity, emergence, spontaneous order, adaptation, and feedback loops. Nature is full of complex systems, from headline-grabbing climate change to the human body itself. Human-made systems include the economy, capital markets, multiuser social online networks, power grids, etc.
Both natural and human-made complex systems tend to become fragile when not regularly stressed. The human body is a perfect example, and one doesn’t need to know the technical terms above to get the gist. A sedentary life and poor diet quite obviously take their toll on the body. Moving to the family construct, think of overprotective parents. In trying to spare their idiosyncratic children from short-term misfortune, they are failing to develop their children’s mechanisms for coping with bigger challenges as they become immersed in the adult world. The children, young-adults-to-be, are thus ironically more vulnerable to random negative events in their lives.
On the national scale, governmental policy increasingly looks like shortsighted sedentary lifestyles fueled by fast-food diets or overprotective parenting. In terms of the macro policy tools of fiscal and monetary management, it’s a disturbing fact that the longest economic expansion ever was followed by the shortest recession ever. Paradoxically, it’s a phenomenon that’s actually being celebrated. This all-gain-but-no-pain policy is communicating a dangerous message, becoming the latest in a progression of macro, policy-induced moral hazards.
Top-down policies, particularly ones imposed by those with extraordinary power and no real personal downside and/or accountability—non-risk-takers with no skin in the game—is endemic and is surreptitiously fostering fragility in both the economy and capital markets.
Policymaking, when not regularly held to account, devolves eventually into socially destructive behavior. When the economy or the capital markets—human-made complex systems, we remind the reader—are distorted by ongoing macro policy interventions that subdue volatility, become increasingly perceived as low-risk, high-return mechanisms for wealth creation. Without periodic recessions to weed out weak companies or fearsome bear markets to act as guardrails against speculative excesses, the properties of these complex systems foment malinvestment or overvaluation extremes like we are currently experiencing. This breeds increased uncertainty, which, while it can’t be quantified, certainly is anecdotally present.
Analogously, the recent spate of California wildfires is evidence of short-term thinking about complex systems. Although the tip-of-the-iceberg issue is rampant residential development in poorly managed forested areas, the overarching and likely unstoppable threat is climate change. An example of short-term thinking that’s yet to make the headlines: How does one reconcile the rapid population growth along Florida’s coasts?
Human-made complex systems, particularly when upset by volatility, tend to develop cascades and runaway chain reactions that decrease, even eliminate, predictability and cause outsized events. Black-swan episodes, though rare, often lead to disproportionately catastrophic outcomes, e.g., California’s Dixie fire. Worse yet, when a complex system like the capital markets is in a critical state, waiting to seek a financial safe harbor until one observes a likely catalyst looming is more than an exercise in futility. It’s downright dangerous. The second largest California wildfire on record, the Dixie fire, may have been sparked by something as seemingly benign as a tree falling on a PG&E power line. Though financial behemoths deemed stable until they weren’t, Bear Stearns and Lehman Brothers proved that catalysts are not size-dependent. Almost no one recognized that the once estimable institutions might fall until it was too late. That’s the way complex systems tend to implode.[2]
What Inferences Might You Make from These Figures?
We’ve referenced the market cap to GDP ratio in recent posts—here and here—which we encourage you to review. Let us know if you think Figure 2 below misrepresents reality. Investors’ speculative impulses were stressed, being displaced by risk aversion, for 13 consecutive quarters during the dot.com bust in the early 2000s and again for six quarters throughout the Financial Crisis. The pandemic-induced speculative stress test of 2020, lasting less than a month, is not even discernible on the chart. Is it unreasonable to wonder if investors are inadequately prepared for the flipside of a market that’s been ascending for 12 years? Even the popular concept of stress testing is absurd on its face: Worst-case scenarios invariably look backward to the archives, not to the future what-ifs.
Absent bear markets, which allow risk aversion to displace speculation as part of the ebb and flow of the speculative/risk-averse tides of sentiment, the market’s ship of financial destiny will eventually slip its anchor chain, becoming untethered from any moorings to valuation. If, and it’s an if worthy of debate, the valuation mooring for all U.S.-based, publicly traded equities is GDP, then today’s ratio of 2.45 times seems disturbingly high compared with 1.9 times during the peak of the first dot.com bubble. In terms of what-ifs, it appears frighteningly high compared with the Financial Crisis low of .57 in the second quarter of 2009. Even the flying Wallendas wouldn’t walk a high wire as frayed as this one.
Given that one of the precipitating causes of today’s downside-risk indifferent speculative mania is the unprecedentedly loose monetary policy, it’s not surprising that residential real estate has followed a trajectory similar to that of the equity and low-quality debt markets.
The Storm-Warning Flag Is Flying, But Is Anyone Looking?
It’s not that speculators haven’t been warned from on high. In May 2021 a comprehensive report was issued by the Fed about systemic vulnerabilities in the financial system and, more specifically, in the equity markets. The report distinguishes between shocks and vulnerabilities, noting the latter “tend to build up over time and are the aspects of the financial system that are most expected to cause widespread problems in times of stress.” It identified market valuation as a top financial vulnerability:
“Elevated valuation pressures are signaled by asset prices that are high relative to economic fundamentals or historical norms and are often driven by an increased willingness of investors to take on risk. As such, elevated valuation pressures imply a greater possibility of outsized drops in asset prices.”[3]
The report has never been mentioned during the Fed’s frequent press conferences.
Stepping Back to Take in the Panoramic View
From that vantage point, it appears we live in an age of unreasonable rationalization. The idea that society is controllable has gained credibility in economics despite the obvious, recent, and relevant proof to the contrary in every other social science.[4] Economically, we delude ourselves into believing that our systems can be designed to avoid the unknown and unintended consequences in the future. Statistical theory and the bell curve, lacking a mechanism for considering the yet-to-occur, aid and abet the inclination. So does linear science and the notion of efficiency or optimization.
Perversely, although the stated intention of political leaders and economic policymakers and managers is to stabilize human-made complex systems by inhibiting fluctuations, the results do not demonstrate much success. These artificially constrained systems become prone to the aforementioned black swans. Such environments eventually experience massive blowups, catching everyone off guard and undoing years of stability or, in almost all cases, ending up far worse than they were in their initial volatile state. Indeed, the longer it takes for the blowup to occur, the worse the resulting harm to both economic and political systems.
Moreover, our economy and capital markets are not like clouds that magically form in an otherwise blue sky. They are path-dependent, that is, historical. Unlike the towering cumulonimbus, they are not simply a manifestation of current conditions (i.e., the pandemic and the governmental response), markets have been formed by a sequence of past actions culminating in the ever-mutating present. Because of path dependence, one cannot separate growth in the economy from risks of recession, financial returns from risks of terminal losses, and “efficiency” from danger of a financial or economic accident. Efficiency in the moment is meaningless if a gambler has to risk losing everything tomorrow. Even conventional diversification may not afford perfection as, in climatic meltdowns, correlation coefficients can approach 1.0. Potential returns, under that scenario, are of no consequence. Thus, Figure 1’s irrelevance in a nutshell …
Part II will turn to how we propose to not only survive uncertainty but to capitalize on it.
[1] As of 2019 the average net worth for all U.S. households was $746,820, and the median net worth was $121,760, according to the Federal Reserve. If the average of a distribution is much greater than the median (the midpoint), the bell curve does not adequately describe the data. Using a normal distribution sample size of 1,000 individuals in Figure 1, if three standard-deviation outliers are included in the data—say, Jeff Bezos ($185 billion), Elon Musk ($179 billion), and Warren Buffett ($104 billion)—the median will be essentially unchanged, while the average net worth jumps to the absurd $469 million, or 775 times the average net worth before the three extraordinarily wealthy men were added. Rather than the 80-20 rule, think of this as the 997-3 rule.
[2] See A Decade of Delusions, pages 342–345.
[3] The Federal Reserve’s semi-annual Financial Stability Report (link), May 6, 2021.
[4] We are excluding Afghanistan, even though some commentators, including Daron Acemoglu, Why Nations Fail, argue that the U.S. involvement there was a top-down failure.