The term Black Swan comes from the fact that before the discovery of Australia in the early decades of the 17th century, all scholars of Ornithology and the inhabitants of the world were convinced, as confirmed by any empirical evidence, that all swans were white. In fact, no one had ever found a black one. The discovery of the first black swan proved this belief wrong. Another theory states that this term comes from Latin poet Juvenal, who wrote: “rara avis in terris nigroque simillima cygno” – “a rare bird in the lands and very much like a black swan”). Both cases the key features of a Black Swan: rarity, extreme ‘impact’, and retrospective (though not prospective) predictability.
How the Highly Improbable Governs Our Lives
“What we call here a Black Swan […] is an event with the following three attributes. First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme ‘impact’. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable. […] A small number of Black Swans explains almost everything in our world, from the success of ideas and religions, to the dynamics of historical events, to elements of our own personal lives.” N.N. Taleb, author of ‘The Black Swan: the Impact of the Highly Improbable’
Our whole life is dominated by the unlikely: like the molecules that make up a cloud, people are a nonlinear dynamical system and just a flutter of a butterfly can change completely the course of their lives. Everyone thinks they can predict their trajectory in life, and this trajectory would take place if not disturbed by other molecules (i.e. from other people) which they cross every day.
The trajectory of people’s lives is, therefore, approximately nonlinear, or unpredictable. Despite proof of their inability to predict the future, some believe in horoscopes, some play at casinos – and yet some criticise geologists for not having predicted an earthquake. Each forecast is based on a model representing the phenomenon under investigation. In physics, chemistry, engineering science and in many other situations, patterns, although they are always a simplification of reality, capture new knowledge and predict future behaviors.
In some situations the level of approximation is sufficient to interpret these laws, in other is far less so. Statisticians have devoted much energy to the development of appropriate methodologies for the analysis of time series using the necessary mathematical tools. The complex patterns of social sciences, however, requires extra touches that do not allow neutral transpositions of methodologies that had proved very effective in other situations.
The behavior of subatomic particles is not comparable to the one of human beings or to the stock markets. In social sciences, Black Swans are always lurking and there is no ‘road map’ when it comes to facing them. For instance, a simple model of daily stock market returns may anticipate disruptive events such as the Black Monday crash of 1987, but might not foresee the breakdown of markets following the 9/11 attacks.
The Black Swan Theory
The ‘Black Swan theory’ refers only to unexpected events of large magnitude and their consequent dominant role in history. Such events, considered unique, play an important and wide role collectively, in contrast to the normal flow of normal events.
Speculating about the reaction of the world after the impact of a relatively unlikely but potentially disruptive event – such as the Brexit vote or the victory of Donald Trump in the United States presidential election – is the daily bread of commentators. The more people discuss about an unlikely scenario the more it seems plausible (though it is not so). Daniel Kahneman and Amos Tversky have demonstrated that people think that implausible events are more likely to occur if people argue about them: the more you talk about it, the less that scenery seems impossible.
Taleb, who developed the Black Swan theory, states that there are two types of rare events: the ones which people talk about (those that are part of the public debate and are likely to be heard of on television) and the ones that nobody talks about (because they escape from models and people are ashamed to speak about them in public, since they do not seem to be plausible).
There are many examples of the first type of Black Swans: in September 2014 everyone was talking about the possibility of a Scotland referendum on independence. At the end of July 2014, some statistical models gave the possibility of secession up to 50%, but less than a month later, for the same models, that possibility had dropped to 5%.
Two years ago the topic on everyone’s lips was the exit of Greece from the European Union: the probability that this historic event would occur had reached 50% after Greek citizens had decided – in a referendum – to reject the EU reform proposals, which was conditioning the provision of additional financial aid. Then, when the Greek Government surrendered and accepted a deal – which provided for a series of reforms in exchange for another loan – it was clear that the referendum had been a simple and ineffective tool of negotiation, and the likelihood of Greece to abandon the EU came down to 10%.
Risk and Reality
Of course, every Black Swan is strictly linked with risk – of every nature, not only financial but, obviously, also political as one can see from the graph below – even in the case of Black Swans of which everyone talks about such as the Brexit vote and the victory of Donald J. Trump.
However, the real Black Swans occur for issues on which public debate is largely absent or even useless: such as Leicester FC winning the Premier League, despite the initial odds in betting shops being 1 in 5,000, or as the Ukraine revolution between 2013 and 2014. But, of course, newspapers and anyone involved in the news are interested in attributing probability to more disruptive but unlikely events. Why? Because it makes headlines.
However, according to Taleb, in terms of global finance, the centralisation of banking institutions and the centralisation of monetary policy represses the unpredictability of market values and may lead to financial crises (as it happened in the 1930s with the Great Depression and in 2008 with the subprime crisis). Currency manipulation creates distortions in the macro-systems of nations and whole continents. Therefore, control is not necessarily an advantage and the inclination of the public authorities to suppress the volatility makes reality and finance less reliable and more dangerous.
Nine Black Swans Which Changed the World
Keeping in mind what a Black Swan is, it is worth taking a look at the nine that changed the world:
1) The financial crisis that swept Asia in 1997, sparking a collapse of the global stock of 60%. A series of currencies devaluations beginning July 1997 spread through east and southeast Asia. The Thai baht collapsed as a result of the government’s decision not to peg anymore the local currency to the US dollar. In the graph below, the trend of rupiah (against the US dollar) between July 2, 1997 (day of the devaluation of baht) and May 21, 1998 (day of the resignation of Suharto). 2) The bursting of the dot-com bubble, which saw the Nasdaq going from 5,046.86 points to 1,114.11 (-78%). Stock prices rallied at unreal speeds, several leading tech giants placed huge sell orders on the stock markets’ peak leading to a wave of panic selling. By the end of 2001, the bubble of dot-com companies was deflated and trillions of dollars of investment capital vanished. Looking at the graph below one can see that the NASDAQ Composite index spiked in the late 1990s and then fell sharply as a result of the dot-com bubble. 3) The sales that crippled Wall Street on the 11 September 2001 (with the Dow massacred with a loss -14%) On September 11th, 2001, the twin towers of New York’s Word Trade Center were hit by two hijacked airliners. The first trading week after 9/11 saw the greatest losses in NYSE history. An estimated $1.4trn in value was lost in those 5 days (NYSE -7.11%, S&P -11,6%).
4) The global financial crisis exploded in September 2008, which blew $10trn from global equity, and saw the end of Lehman Brothers and of its 25,000 employees’ jobs. The graph below shows the sub-prime market between 1997 and 2007: Bear Stearns collapsed and was bailed out by the NY Fed and eventually sold to JPMorgan Chase. The subprime-mortgage-induced financial crisis of 2008 has been considered the worst financial crisis since the Great Depression. The graph below shows credit default swap prices of some European countries from January 2010 to March 2014. 5) The sovereign debt crisis in Europe. After the 2008 global financial crisis, financially stronger countries like Germany were unable to fund weaker countries out of their debts. Financially weaker countries like Greece (146.2% Debt to GDP), Portugal (96.2% Debt to GDP), Ireland (86.8% Debt to GDP) and Spain (60.1% debt to GDP) thus needed a bailout. There were fears that if one or more Eurozone country members were to leave the Euro and default on their debts, this would bring down the entire world banking system. In the graph below, the credit default swap prices of some European countries from June 2010 to September 2011 are shown. The y-axis indicates the points: a level of 1000 costs $1m of debt for 5 years. 6) The Fukushima nuclear disaster in Japan, followed by the tsunami of March 11, 2011. The Fukushima Daiichi nuclear disaster was an energy accident initiated by the tsunami following the Tohoku earthquake (magnitude 8.9) on March 11, 2011. The DowJ lost 2.4%, the Topix 9.5%, the DAX 4% and the Nikkei 16%.
7) The oil crisis, which started in June 2014, brought oil prices to more than half their value from $110 to $50. It officially started on 22 June, with 1.3 million barrels that were loaded into two tankers in a port in Libya.
8) The Black Monday crash of 8 August 2015, when the Shanghai Stock Exchange collapsed in one session of 8.5% and 30% three weeks later. The low oil prices wreaked havoc with the commodity-exporting nations, including exporters of manufactured good like China. Since these oil exporters were enormous importers, global trade withered. Since borrowing was cheap in China, over-speculation on Chinese companies using borrowed money to play the stock market was widespread. The amount of money invested soon exceeded the rate at which the companies could grow, so the Chinese government decided to devalue the yuan but the plan backfired, causing a wave of panic selling, leading up to the Black Monday. 9) And, of course, the Brexit vote. As markets woke up on the 24th of June to the news of the British referendum to leave EU, the pound tumbled to a 31-year low against the dollar. In terms of the long-term economic impact of Brexit however, economists are still divided and uncertain. In a poll by Bloomberg, almost three-quarters of the economists said that Britain is headed for a recession either immediately on in the near future.
Our world “is dominated by what is extreme, unknown or very unlikely, so we have to use the rare event as a starting point, not as an exception to hide under the rug”. To predict a Black Swan is almost impossible, because if one could predict it, then it would not be a Black Swan at all. So – as Taleb said – the next time you hear an important economist speaking about predictions, do not waste your time trying to convince him of the reality of the Black Swan.