Dr. Christopher Long
BLACK SWAN THEORY
The concept of a black swan dates as far back as the second century A.D. when a Roman poet by the Name of Decimus Juvenal coined the phrase "rara avis in terris nigroque simillima cygno" ("a rare bird in the lands and very much like a black swan") (Alam, 2018). Black swans are events with world-shaping implications and consequential shocks (Taleb, 2010). While the concept of black swans dates back to the 2nd century, the theory is relatively new and widely contested (Alam, 2018).
(David Levenson/Getty Images)
Who is Nassim Nicholas Taleb?
Nassim Nicholas Taleb was born in Beirut, Lebanon, and finished an MBA at Wharton University and a PH.D. from the University of Paris (Langreth, 2009). Taleb became a stock trader and succeeded in 1985 when currency contracts he held skyrocketed in value (Langreth, 2009). In 1987, he gained $40 million in interest during the U.S. stock market's most significant single-day loss (Langreth, 2009). Taleb wrote his book on Black Swan theory in 2007, drawing inspiration from an age-old presumption that all swans were white (Kanungo, 2020).
What are Black Swans?
Black Swan's theory draws inspiration from the falsification theory, which states that a conclusion based on many observations can be undone entirely once an established core tenant or foundation is disproven (Alam, 2018). One event out of billion observations could disprove a theory (Alam, 2018). Taleb developed the ideas of Mediocristan and Extremistan to help explain this concept (Alam, 2018). Mediocristan is the idea that everyday things happen regularly, and their probability is easily computed. (Alam, 2018). Extremistan is an idea where nothing can indeed be accurately predicted, and seemingly impossible events will occur frequently and with significant impact. Black swans are an example of Extremistan (Alam, 2018). These events are outliers and go well beyond regular expectations, as there is typically no precedence or indication that the event could have been predicted or seen as a possibility (Lindaas & Pettersen, 2016). By definition, black swans are improbable and rare events that disrupt the global economy (Gary & Alles, 2021; Lindaas & Pettersen, 2016). Three principles help define a black swan: its unpredictability, the significant impact in size and scope, and post-event explanations and reflections to make the event seem less random (Gary & Alles, 2021).
Muller and Stewart (2016) redefine black swans as unpredictable events and, at the time of their occurrence, grab emotions and are embraced by most in society as a significant event. These emotion-grabbing events are typically seen as a harbinger of things to come when, in most cases, they are more of an aberration (Muller & Stewart, 2016). Black swans, by nature, are outliers, so by definition, they would most likely be an aberration (Muller & Stewart, 2016). While black swans are unpredictable, they do not affect all stakeholders equally. For example, while restaurants were hit especially hard during the COVID-19 Pandemic, some online retailers, such as Amazon, experienced record-high profits (Gary & Alles, 2021).
The unpredictable nature and the sporadic occurrences imply that accurately predicting would be highly improbable (Alam, 2018). The events would be practically impossible to compute as their rarity often goes beyond imagination (Alam, 2018). The current systems we use to detect and analyze risk do not consider events of this magnitude, as the extraordinary impact of these often goes beyond most worst-case scenarios (Gary & Alles, 2021; Lindaas & Pettersen, 2016). Taleb points out that only after these events do we seek to explain their nature and explain these occurrences as if there were indications that went ignored (Lindaas & Pettersen, 2016).
Black swans can go beyond historical dooms like events, and often inventions such as the Internet or even ideas such as democracy can be considered black swans, though this has faced much criticism (Alam, 2018; Mueller & Stewart, 2016). All life-changing events could be considered a black swan (Alam, 2018). From a psychological standpoint, black swans are simply events that a typical person, organization, or community was not expecting and was caught off guard (Lindaas & Pettersen, 2016). The surprising aspect is that most people always understand who and where the event was experienced (Lindaas & Pettersen, 2016). This notion brings out a critical point of black swans; the element of surprise associated with these events can be mitigated by sharing knowledge (Lindaas & Pettersen, 2016). While events like 9/11 seemed unfathomable to the general populace, it was not outside the realm of possibility for U.S. intelligence agencies or the airline industry (Lindaas & Pettersen, 2016). This concept expanded the theory to include the idea of grey swans, events that still have a significant impact but may be considered an unlikely but probable event (Swango, 2020). This idea relates to the concept of risk made famous by Donald Rumsfeld in 2012 when he discussed decision-making and classifying risk into four categories: known-knowns (certainty), known-unknowns (risk), unknown-knowns (Knightian uncertainty), and unknown-unknowns (genuine uncertainty) (Phan & Wood, 2020). Grey swans are studied for mitigation and prevention, as the events tend to have a probable occurrence (Swango, 2020).
Most researchers argue that with black swans, more effort should be spent trying to find better means to cope with the impact of these events rather than look at predictability (Lindaas & Pettersen, 2016). Taleb argues that while prediction is impossible, there is an opportunity to profit from them, and as such, efforts are better concentrated on reaction rather than prediction (Alam, 2018). Grey swans are studied for mitigation and prevention, as the events tend to have a probable occurrence (Swango, 2020). For example, humans have two sets of lungs and two kidneys, so we have backups if one fails (Taleb, 2010). This idea contradicts most business principles, prioritizing optimization and efficiency over redundancy, a significant point of contention with Taleb and his views on black swan (Taleb, 2010).
Major Black Swan Events
There are several instances of black swan events. Some notable events include the Black Death in Europe (1347–1351), European Flu Pandemic (1889–1890), World War I (1914–1918), Stock Market Crash of 1929 and Great Depression, Collapse of the Soviet Union, 1991, 9/11 Terrorist Attacks, Great Recession and Financial Crisis (2008–2011), Fukushima Nuclear Disaster (2011), and COVID-19 (2020-2021?) (Swango, 2020; Desjardins, 2016; Phandis, Joshi, & Sharma, 2021). Also included are events such as the evolution of democracy, capitalism, or the introduction of the Internet (Alam, 2018).
9/11
9/11 was a black swan event that's impact was felt immediately and resulted in thousands of deaths and over a trillion in investment losses. Unlike the 2008 financial crisis and COVID-19, the impact was immediate and left no time to adapt. The event reshaped how the U.S. viewed security and airlines and resulted in numerous reforms (Phadnis et al., 2021).
2008 Financial Crisis
The 2008 crisis is among the most widely researched and disputed black swan events (Phadnis et al., 2021). In 2007, the housing market began to crash, leading to a chain reaction that created a global economic crisis (Phadnis et al., 2021). The crisis resulted in massive bailouts of banks deemed "too big to fail" funded by taxpayers, which gave way to new legislation to help protect the U.S. economy from future disasters in the banking industry (Gary & Alles, 2021). The Dodd-Frank Act laid out several reforms, including a mandatory stress test for a bank designed to test a bank's threshold and capacity to stave off another similar disaster (Gary & Alles, 2021).
Fukushima 2011
On March 11th, 2011, a significant earthquake and tsunami caused a critical inside the Fukushima Reactor (Kanungo, 2020). The earthquake was extremely rare and powerful and registered at 9.0 on the Richter scale (Kanungo, 2020). However, Japan experiences high earthquakes so that the event would fall under the grey swan category (Kanungo, 2020; Swango, 2020). The Japanese government and the Tokyo Electric Power company went under intense scrutiny for their lack of preparedness for the event (Kanungo, 2020). As a result of losing four reactors, the nuclear industry suffered massive losses as repairs and investigations kept the power plant out of commission for an extended amount of time, requiring local municipalities to seek alternative means of power (Kanungo, 2020).
COVID-19
The COVID-19 Pandemic had wide-ranging implications across a variety of industries and even economies around the world. The S&P 500 recorded eight of the most significant single-day drops in value in 2020 (Phadnis et al., 2021). In the U.S., over 100,000 businesses closed permanently; some included major brands such as Hertz, GNC, Avis, Gold's Gym, and JCPenney, to name a few (Gary & Alles, 2021). The medical implication and response had wide-ranging impacts on their own as temporary regulations and mandates forced many industries to close doors or significantly reduce their capacity (Swango, 2020).
From the accounting side, the Pandemic presented numerous challenges as a few of these failing businesses received an adverse concern opinion on their 2019 10-K reports (Gary & Alles, 2021). The Pandemic was the final nail in the coffin for many businesses that were competing against new online alternatives. AMC, for example, already had to compete against new streaming services, and brick-and-mortar retail had to compete against giants such as Amazon (Gary & Alles, 2021). As a result, new trends with video streaming and online shopping received a significant boost and were able to push more traditional alternatives (Gary & Alles, 2021). The full magnitude of the impacts of this event has yet to be determined as the crisis is still ongoing (Phadnis et al., 2021).
Notable Studies
Lindaas and Pettersen (2016) argue against a primary point of black swan theory that these events are unpredictable. Industries can strengthen their awareness of this event through groupthink and collective research. Lindaas and Pettersen (2016) propose expanding the black swan to cover both "unknown unknowns" and "unknown knowns" and for organizations to broaden collective endeavors and communication related to these events to enhance better their ability to predict black swan events. The concept of "know-knows," "unknown knowns," and "unknown unknowns" is a concept borrowed from Donald Rumsfeld that was used to enhance U.S. intelligence capabilities post 9/11 regarding the analysis and prevention of security threats (Lindaas & Pettersen, 2016). The essence of their argument is that through knowledge sharing, organizations can better prepare themselves for black swan events by eliminating the "surprise" aspects of these events (Lindaas & Pettersen, 2016).
Payzan-LeNestour (2018) studies how people cope with tail risk and the connection between Bayesian learning and black swan events. Several studies connected Bayesian learning and black swan events, but there was a lack of research regarding how people cope with decisions regarding tail risk. The study found that subjects adopted Bayesian behaviors as expected when presented with a task; however, there was a significant departure from this thinking after learning about the tail risk.
Bonini, Pighin, Rettore, Savadori, Schena, Tonini, and Tosi (2019) study looks at how overconfidence plays a role in the departure of Bayesian thinking with black swan events. The study looks at alpine skiers and their overconfidence when selecting slopes that may be at high risk of an avalanche. The study's outcome found that the skiers with near-fatal experiences were due to overconfidence, as these people tended to select more rewarding routes instead of safer alternatives. The findings suggest that overconfidence can increase exposure when faced with black swan events. People or organizations must increase their awareness of their vulnerability when making critical decisions.
Phan and Wood (2020) discuss a primary contention of the black swan theory that not all black swans are genuinely unpredictable. Some black swan events can fall under a "know-unknown" category in that the event's timing may be unknown, but the scenario itself should be considered probable. For example, 9/11 was considered an unpredictable event by most people. However, the airline industry had prior experience with hijackings and terrorist activities. So, while the timing of 9/11 would have been improbable for the airline industry to predict, the scenario itself should not have been considered an unlikely event.
Another example is COVID-19; while most of the population had little experience adapting or reacting to a pandemic, the government knew about similar events and how to respond to those events. The primary point of the argument is that the prediction of these events is implausible. Still, organizations must understand their exposure to black swans and how the events have interconnected implications across industries and economies (Phan & Wood, 2020).
Phadnis, Joshi, and Sharma (2021) studied seven global events from 1997 to 2019 that significantly impacted stock markets to uncover potential predictors of black swan events. The difference between the maximum and minimum value of stocks affected by the event will be more significant than 10% of the maximum value during these events. The study found that a set of pre-shocks in index values before the maximum drop, assigning sensitivity scores helps to determine the effect of the world events on equity markets, and the study used an observational approach to analyze changes in stock values without questioning reasons and implications on a fundamental scale. The study helps to fill a gap in research related to predictive analysis and actionable insight into black swan events.
Gary and Alles (2021) argue that bankruptcy metrics and predictions used by banks considered too large to fail help determine the maximum revenue shock a company can withstand without filing for bankruptcy. Going Concern Survivability Index (GCSI) and One Month Resilience Index (OMRI) metrics work to determine the overall survivability by tracking the maximum reduction in revenue a company can absorb and the percentage of a business income fall if it loses revenues equal to the most successful month of sale (Gary & Alles, 2021). The study focuses more on the company's capability to withstand the impact of black swans rather than their ability to predict said events (Gary & Alles, 2021). This study aligns with Taleb's central conclusion that the efforts are better spent looking for ways to mitigate the impact of a black swan rather than prediction (Gary & Alles, 2021).
Methodologies
Most studies conducted on the Black Swan theory tend to rely on observation and case studies to analyze past events. This aligns with most opinions on the Black Swan theory that past events should be scrutinized to learn more about what steps can be taken to prepare better and adapt to these events (Lindaas & Pettersen, 2016). For now, case studies and observations are the best means to study these events.
A few notable studies relied on other methodologies. Gary and Alles (2021) used action design research to focus on designing a problem-solving artifact. The process looks at an ideal outcome and then works backward to design a solution to get to this ideal outcome (Gary & Alles, 2021). Payzan-LeNestour (2018) conducted an experimental study on risk and reward and how people cope with black swans, and the study was the first experimental study on tail risk. Bonini et al. (2019) employed a survey to gauge confidence regarding skier's attitudes toward avalanches. These findings or recommendations counter the Black Swan core tenant that research efforts should consider mitigation rather than prediction. The study was used to show how confidence plays a role in black swans (Bonini et al., 2019).
Findings
Taleb and Blyth (2011) argue that complex systems are more susceptible to randomness and use U.S. efforts to explain these vulnerabilities about the 2008 financial crisis and mid-east policy. They point out that with the U.S. financial system before the 2008 financial crisis, Greenspan set out to control the U.S. economy by controlling recessions and inflation through interest rates (Taleb & Blyth, 2011). With the mid-east policy, the U.S. set out to control the stability of the region through partnerships, bilateral military action, and soft power (Taleb & Blyth, 2011). The result of both efforts was ultimately increased volatility and eventually collapse (Taleb & Blyth, 2011). The more prominent theme at play is that, by nature, human beings fear randomness, and most efforts seek to eliminate this fear through control (Taleb & Blyth, 2011). However, these attempts are almost always doomed to fail, and efforts are better spent adapting and embracing the volatile rather than predicting and preventing (Taleb & Blyth, 2011).
Taleb and Treverton (2015) found with their study five indicators of instability within an organization that is more susceptible to the effects of a black swan event. First, centralized decision-making is problematic as it limits resources for adaptability. Second, the absence of economic diversity and prowess with a few sectors specialization makes a country more susceptible to volatility. Third, high leverage indicates that too much debt reduces the means to adapt. The fourth factor is a lack of political variability. Stable countries experience moderate political changes and are more adept at handling sudden political opinion and turmoil swings. Finally, those countries that have experienced significant turmoil and chaos are better prepared to adapt; the more recent, the better. Those without experience will lack the skill sets and preparedness to react to black swan events better.
Taleb and Treverton (2015) further argue how fragility affects dictator states like Syria. They point out that when comparing Lebanon and Syria, the recent Syrian civil war looks on its surface to be more stable (Taleb & Treverton, 2015). However, fundamental problems with its structure make it more susceptible to shock, and the fact that it has not had to withstand any significant turmoil in recent history does not bode well for the country either. Essentially, Taleb and Treverton (2015) argue that structural properties better indicate fragility and instability than historical data.
Lindaas and Pettersen (2016) contradict Taleb's original conclusion that people cannot predict black swan events. They argue that through communication, an organization can improve its capabilities to better predict and plan for black swan events (Lindaas & Pettersen, 2016). Their findings suggest that black swans are relative to one's knowledge and beliefs (Lindaas & Pettersen, 2016).
Payzan-LeNestour, E.'s (2018) study provides evidence that Bayesian learning assumptions made in prior finance studies regarding tail risk are uncertain. The research Bayesian research into cognitive thinking precisely states that the brain does not calculate probabilities but instead draws a sample from probability distributions. The findings suggest that performance and economic problem complexity are linked in that performance increases with task difficulty because people spend more effort on complex problems.
Bonin et al. (2019) conducted a study of alpine skiers and how their confidence plays a role in determining whether to go down a ski route that is at high risk of avalanche. Their study found that overconfidence played a role in selecting these routes, as skiers tended to make decisions based on factors unrelated to the situation. Overconfidence will lead some businesses to take on more risk to pursue greater rewards during the potential threat of black swan.
Gary and Alles (2021) propose that GCSI and OMRI metrics should be added to a company's 10k report to provide stakeholders with visibility and insight into the company's exposure and survivability regarding black swan events. These findings or recommendations align with the Black Swan core tenant that research efforts should focus on mitigation rather than prediction.
Phandis et al.'s (2021) study examined stock indexes during seven major black swan events. It showed that there are severe pre-shock indicators signifying a downturn in equity markets. Though their scale can predict a severe industry downturn, it in no way helps predict the black swan events themselves, just the potential economic fallout.
Major Contentions
Lindaas and Pettersen (2016) argue against Taleb's original conclusion that black swans can not be predicted but rather through a process called "de-blackening," collective information gathering can improve their ability to predict and plan for black swans (Lindaas & Pettersen, 2016). "De-blackening" refers to the process by which an "unknown unknown" turns into an "unknown-know," meaning possible scenarios are developed and simulated (Lindaas & Pettersen, 2016). Lindaas and Pettersen's (2016) ideas contradict Taleb in advocating for more research into prediction. Another point of contention is the study of past events to discover possible indicators of future events (Lindaas & Pettersen, 2016). Table argues that black swans are so rare that indicators found in hindsight would only apply to that particular event (Lindaas & Pettersen, 2016).
Phan and Wood (2020) argue that human beings, by nature, tend to oversimplify the meanings of their experiences. So, in essence, complex solutions regarding policies and black swan events will not work. Instead, policymakers should focus more on short-term solutions to mitigate the event.
Muller & Stewart (2016). Challenge a primary tenant of Taleb's theory regarding black swans and their impact. According to Taleb's admission, they argue that all black swans have an extreme impact and remain outside ordinary occurrences. However, events such as 9/11 and Pearl Harbor were not precisely unexpected or unprecedented. For example, with 9/11, the possibility of using a commercial aircraft as a weapon was unsuccessfully attempted in Parris in 1994. So, to those who worked in the industry or were able to have direct knowledge, these events were not unprecedented. This idea complicates the definition of a black swan as it can come down to the individual perception of the event. They further argue that some of the examples offered, such as the invention of the Internet and democracy, were more of a gradual process characterized by trial and error and were not part of some consequential shock typical of traditional black swan events.
Finally, Taleb (2010) argues against classifying the 2008 financial crisis as a black swan because he sees the event as a predictable catastrophe and compares it more to a scandal. Many authors argue against this notion, as most people outside the banking and mortgage industry had no idea of the magnitude of problems and consequences that were about to unfold (Taleb, 2010). This notion points to a bias on Taleb's part regarding his theory, in that black swans come down to individual perception (Muller & Stewart, 2016).
Gaps and Future Research
There is a gap in research regarding the prediction side of black swans, as most research looks more into survival. This idea is unsurprising as Taleb argues that research into predicting black swans is useless (Lindaas & Pettersen, 2016). Phadnis et al. (2021) point out two gaps in practice. The first gap is that there is not enough research into the predictive analysis of markets to identify black swans before the impact is felt. The second gap is a need for more research into how financial decision-makers cope with tail risk and the impact of a black swan. Linda and Pettersen (2016) advocate for more research into collective information gathering regarding "unknown unknowns" or events without precedence. This concept aligns with both their chief argument and Taleb's (2010) argument that through collective knowledge sharing, this event can be better mitigated (Lindaas & Pettersen, 2016).
Ethical Considerations and Mitigation
There is little to no research on ethical considerations regarding black swans, as most of these events are identified afterward. However, some events, such as the 2008 financial crisis, are heavily scrutinized for the mishandling of the situation by both the government and major banking institutions (Phandis et al., 2021). The 2008 crisis stands apart as years of mismanagement and fraudulent practices led to the event (Phandis et al., 2021). The U.S. Government responded with several banking reforms, but some researchers argue that these reforms needed to go farther (Phandis et al., 2021). While Taleb refers to the financial crisis as a scandal, he also argues against complex systems for control as they often lead to more severe consequences (Taleb, 2010; Taleb & Blyth, 2011; Muller & Stewart, 2016). The best path forward to mitigate these issues was argued by Lindaas and Pettersen (2016), who argue that organizations should focus more on collective knowledge sharing to increase awareness and identify potential scenarios through a process they call de-blackening.
Conclusion
Black swans are interesting events, and how society reacts to them closely aligns with individual human nature. Often, these events have significant impacts and appear to be completely unpredictable. However, under analysis after identification of the event, several indicators often needed to be noticed (Lindaas & Pettersen, 2016). This brings up a significant point: individual perception is often the key determining factor of a black swan, and these events are often labeled as an excuse for preparedness (Swango, 2020). Most of these studies advocate for planning to mitigate the impact of an extreme scenario, and too much focus on optimization and efficiency can negatively impact an organization's chance of survival (Taleb & Blythe, 2011).
References
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