The second half of July 2024 turned out to be quite difficult for traders. The reason for this was a number of unpredictable events in politics and business: the assassination attempt on Donald Trump, a global failure in the Microsoft system, Joe Biden’s withdrawal from the US presidential race. All this probably affected the nature of transactions and the behavior of traders.

But what if a completely unpredictable event occurs that has a global and long-term impact on the markets? This possibility always exists and on its basis a whole theory of “black swans” was developed.

Where did the theory come from?

Nassim Nicholas Taleb popularized the theory of the same name in his book “The Black Swan: The Unpredictability of the Future”. The term comes from an ancient Latin proverb that compares something true or probable to a “black swan.” Roman philosophers of the second century already used the phrase to describe the impossible.

Much later, in the 16th century, the “black swan” became a figure of speech symbolizing something that simply didn’t exist. In 1697, an expedition of Dutch adventurers led by Willem de Vlaminck set foot on Western Australia. To their surprise, they found something that, according to the conventional wisdom of the time, couldn’t exist: a black swan.

Since then, the “black swan” has become a symbol for an event that was initially considered impossible, but was later confirmed. It is a kind of designation for established beliefs that are subsequently disproved.

In the context of financial markets, a “black swan” is used to describe extremely unlikely events with far-reaching consequences. In the book, Taleb demonstrates that such events occur not only in finance, but in many other areas as well.

A brief explanation of the Black Swan Theory

In recent economic history, there are many events that were considered impossible until they happened. Most of the significant scientific and economic achievements of recent decades were just such “black swans”. Before their occurrence, they seemed unpredictable and unrealistic, but their implementation had a colossal impact.

Examples of “black swans”:

  • World War I;
  • the emergence of nuclear power;
  • the emergence and widespread use of the Internet;
  • the collapse of the Soviet Union;
  • the introduction of civilian GPS systems and the ubiquity of smartphones;
  • the 2008 mortgage crisis.

All of these events were previously perceived as unimaginable, and predictions about them seemed absurd. However, despite this, they happened and became part of reality.

What is characteristic of a “black swan”?

Nassim Taleb in his research focuses on the inadequacy of traditional statistical tools, such as the normal distribution, when applied to extremely rare events. He justifies this by the fact that such tools assume the presence of an extensive sample of historical data, which is simply impossible in the case of “black swans”. Extrapolation of past trends in such conditions isn’t only useless, but can also create an illusion of security, increasing the vulnerability of the system to unexpected shocks.

According to Taleb, the key feature of the “black swan” is its unpredictability, associated with potentially catastrophic consequences. This is why, the author argues, it is always necessary to take into account the probability of such events, regardless of their specific form. Systems that are isolated from risk and unable to adapt to unexpected changes are most vulnerable to “black swans”.

The author identifies three characteristic features of “black swans”:

  • Exceptional rarity;
  • Large-scale consequences;
  • Retrospective explainability.

The latter means the tendency of people to find a rational explanation for such an event after its occurrence, thus making it predictable in retrospect. However, such attempts don’t contribute to real forecasting, since the spectrum of possible “black swans” is practically limitless – from economic crises to global conflicts.

“Black Swans” as exemplified by specific events

The 2008 mortgage market crash is one of the most famous and frequently cited examples of a “black swan”, clearly demonstrating the relevance of Nassim Taleb’s theory. The event fully corresponded to the characteristics he described: the impossibility of adequately assessing the probability of a housing bubble, despite its large-scale and destabilizing consequences. After the bubble burst, many economists and analysts presented retrospective explanations that made the crash a predictable event. However, as Taleb notes, only a few experts were able to foresee such a large-scale crisis before it occurred, which once again underlines the unpredictability and surprise of “black swans”.

The hyperinflation in Zimbabwe in 2008, which reached an unprecedented level of several tens of billions of percent, serves as a clear example of a “black swan” in the economy. Such catastrophic inflation was almost impossible to predict and led to the deepest financial crisis in the country.

We see a similar picture in the case of the dot-com bubble collapse in the early 2000s. The rapid growth of the Internet industry in the United States was accompanied by a rapid increase in the value of shares of technology companies, many of which had no real market support. When this “bubble” burst, investors suffered colossal losses. The uncertainty and newness of Internet technologies at that time made such a collapse a virtually unpredictable event.

Was the COVID-19 pandemic a ‘black swan’?

For years, virologists have been warning about the possibility of a global pandemic, while emphasizing society’s unpreparedness for such a challenge. But their words fell on deaf ears until the world was confronted with the harsh reality of COVID-19.

A look at human history clearly shows that pandemics are not uncommon. Outbreaks of deadly viruses and infections like the Spanish flu of 1918, the Black Death, and many other epidemics have plagued the world over the centuries. These events, repeating themselves cyclically, shouldn’t have come as a surprise.

Despite the obvious facts pointing to the inevitability of a new pandemic, humanity chose to ignore these warnings. Decades of unfettered globalization created an illusion of safety, a false belief that such catastrophes were a thing of the past.

So, as Nassim Taleb notes, COVID-19 was not a “black swan” – an unpredictable event. Instead, its emergence was a “white swan” – a predictable but ignored phenomenon. The clear evidence of the cyclical nature of pandemics was irretrievably forgotten, leaving the global economy defenseless against COVID-19, resulting in colossal destruction.

Why does a trader need to know about the black swan phenomenon?

The concept of “black swans” not only emphasizes the unpredictability of rare events with large-scale consequences, but also criticizes cognitive biases that prevent adequate perception of uncertainty. In particular, Taleb’s theory points to a widespread confirmation bias, when people tend to search for and interpret information in such a way that it confirms existing beliefs. This cognitive bias leads to individuals and groups overestimating their ability to predict the future and underestimating the likelihood of unexpected events. As a result, decisions are made that don’t take into account the possibility of extremely unlikely, but potentially catastrophic events, which can lead to serious negative consequences.

Since the world we live in is characterized by a high degree of uncertainty and complexity, humanity is faced with the question of how to adapt to these conditions and successfully function in them.

One way to address this challenge is to rethink our relationship with uncertainty. Instead of striving for complete certainty and clear boundaries, we need to learn to perceive the world as a space filled with many interconnected elements, the boundaries between which are blurred and constantly changing. Recognizing that there is a significant amount of unknown allows us to be more flexible and adaptive, ready for unexpected changes.

Chaos theory offers another approach to understanding complex systems. It argues that even in chaotic processes, certain laws and patterns can be found. Acquiring the skills to recognize these hidden structures allows us to better navigate a complex world and make more effective decisions.

Strategies during a “black swan” period

Remember: there are no 100% forecasts that clearly guarantee market movement. Therefore, the main thing that you need to be able to do in difficult situations is to be flexible:

Dynamic asset allocation. Let’s say that your investment strategy is largely focused on stocks. However, being aware of the potential risks associated with stock market volatility, you want to minimize potential losses in the event of a sharp collapse. To do this, you can implement an automated system that, upon the occurrence of certain pre-set market conditions, will redistribute the assets of your portfolio. In particular, the system can be programmed to sell stocks and simultaneously buy more conservative instruments, such as gold ETFs. Thus, the investment portfolio will be reoriented to protect capital until the market situation stabilizes.

Volatility management. Increased market volatility often precedes significant events that can significantly impact an investment portfolio. To minimize potential losses during such periods, you can develop a strategy that reacts to changes in the level of market volatility. For example, when the CBOE Volatility Index exceeds a predetermined threshold, the system can automatically transfer a significant portion of the portfolio to more defensive assets, such as government bonds or gold.

A change in prevailing market trends can also signal the approach of a “black swan”. Developing a strategy that can identify such changes and promptly adjust the portfolio structure will allow the investor to respond to emerging problems in a timely manner.

To create a more comprehensive investment portfolio protection, you can use a broad market index, such as the S&P 500. When the index reaches a certain level of decline, indicating the beginning of a systemic crisis, the system automatically reduces the share of risky assets and increases the share of defensive instruments.