CFM QUARTERLY
OCTOBER 2025
PART A : ARTICLE
BLACK SWAN
DR. PRASANNA CHANDRA
What is a Black Swan
An event qualifies as a black swan if three conditions are fulfilled:
Some examples of black swan are the outbreak of World War 1, 9/11 and Black Monday.
The black swan problem is also referred to as “the problem of induction”. The basic idea is that there are great uncertainties in forecasting the future on the basis of our knowledge of the past . How can we figure out something which is infinite and unknown , based on something that is finite and known ?
The Implication of Black Swan Blindness
Nassim Taleb talks about five issues related to the Black Swan that stem from our blindness to it:
Mediocristan Vs Extremistan
Mediocristan is a land devoid of black swans. Mediocristan is the land of the average where the first 100 observations of a variable will give a fairly reliable basis for expecting what you might see, for say, the next 500 observations. The fundamental law of Mediocristan is that when your sample size is large, no single instance will significantly change the average value. For example, weight, height and road accidents are matters that belong to Mediocristan.
In Extremistan, Black Swans are present. So, the first 100 observations of a variable may not be a fairly reliable bases for expecting what you might see, say for the next 500 observations. Most human made or social matters such as wealth and income belong to Extremistan. For example, the average wealth of a group of 100 persons or even 1000 persons would be significantly influenced by the inclusion or exclusion of the wealth of a person like Bill Gates.
In Mediocristan, it is possible to make predictions reliably.In Extremistan, it is much more difficult, or well nigh impossible. The problem arises because in general we think we are living in Mediocristan but almost all of the matters that we try to forecast are matters of Extremistan.
In the table given below Nassim Taleb contrasts extremistan with mediocristan.

Gaussian Schmaussian
The bell curve or normal distribution, also called the Gussian distribution, is commonly used tool for risk management by regulators and central banker among others. In a normal distribution, the farther the value of a variable from its expected value, the smaller is its probability of occurrence. The normal distribution is an excellent tool in the land of Mediocristan. However, it does not work with variables from Extremistan. For example, if stock market returns are modeled as normally distributed, an occurrence like the Black Monday of 1987, when the market fell by nearly 23% in a single day, would clearly be an outlier because according to the normal distribution such an event would happen once in several billion lifetimes. Many investors were bankrupted.
So using normal distribution can be misleading in making decisions in matters from Extremistan. And this seems to apply pretty much to all human-made social matters.
What can we do then? We can use what Nassim Taleb calls Mandelbrotian randomness instead of assuming that everything conforms to normal distribution. Mandelbrotian randomness does not assume that deviations from the mean become progressively much rarer. Instead, it suggests, if we talk about the returns in the stock market, that the probability of seeing a return of -10% not significantly lower than the probability of seeing a return of -5% . If we had made that assumption on the Friday before the Black Monday, a decline of -22.6% would have been considered a possibility and some protective measure against that could have been taken. By replacing normal distribution with Mandelbrotian distribution, we can turn some Black Swans into Grey Swans. Grey Swans are known unknowns rather than unknown unknows (Black Swans). They are better to deal with because we can least adapt our decision-making to something that we don’t know about.
How to Act as an Investor in an Environment of Black Swans:
In a world dominated by Black Swans where we are vulnerable to the error of confirmation, the narrative fallacy and tunnelling we have to be wary in using the normal distribution.
What then should we do? Taleb suggests two different approaches :
PART B : SNIPPETS
Peter Lynch on the Advantage of Being a Small Investor
“I have been hearing that the small investor has no chance in this dangerous environment where there are 50,000 professional stock pickers who dominate the show and the small investor ought to get out. From where I sit, I’d say that the 50,000 stock pickers are usually right, but only for the last 20 percent of a typical stock move. It’s that last 20 percent that Wall Street studies for, clamours for, and then lines up for – all the while with a sharp eye on the exits. The idea is to make a quick gain and then stampede out of the door. Small investors don’t have to fight this mob. They can calmly walk in the entrance when there’s a crowd at the exit, and walk out the exit when there’s crowd at the entrance.”
The Prisoner’s Dilemma
The prisoner’s dilemma represents the difficulty of achieving the best outcome when there are obstacles to cooperation. Consider two prisoners A and B who have been arrested for a crime. They are kept apart from each other and have been offered the same deal as shown in the following matrix:
|
B |
|||
|
|
Incriminates |
Does not Incriminate |
|
|
A |
Incriminates |
A & B receive light punishment |
A receives no punishment but B receives severe punishment |
|
Does not Incriminate |
A receives severe punishment but B receives no punishment |
A & B receive no punishment |
|
Clearly the best course of action for both A and B is to not to incriminate each other. But if they cannot cooporate the choice becomes difficult.
Reversion to the Mean
According to Galton, regression or reversion to the mean “is the tendency of the ideal filial type to depart from the parent type, reverting to what may be roughly and perhaps fairly described as the average ancestral type.”
Why is forecasting so difficult, if Galton’s Law is immutable. The difficulty stems from three sources:
PART C : WIT AND WISDOM
HUMOUR
Alaska
A man living at the border of Canada and Alaska asked a surveyor to find out where his house is located actually. The surveyor, after finishing his work, declared that the house was in Canada. Relieved, the man said, “Thank God, I will no longer have to endure the terrible Alaskan winters.”
Transfer
A weatherman working in London had great difficulty in predicting the London weather. So he applied for transfer saying, “The weather in London does not suit him.”
WISE SAWS