Charvaka, Hume and Modern Financial Markets

by Nikhil Gavankar

The Greek philosopher Sextus Empiricus divided philosophers into three categories, those thinkers who have claimed to discover the truth, i.e. dogmatists, those thinkers who claim that truth cannot be discovered, i.e. academicians, and those thinkers who do not make either of these claims, i.e. skeptics or Pyrrhonists. The word skeptic means ‘inquirer’, and it is indeed relentless inquiring and questioning that defines skepticism as it originated in Ancient Greece.

There are a number of interesting parallels between the ancient school of Indian materialism formulated by Charvaka and the British Western philosopher David Hume in terms of skeptical thinking. Skepticism amounts to the doubting or questioning of all knowledge claims. Skeptics, like David Hume, make the claim that it is not possible for human beings to know reality as it is in itself apart from the sensations or experiences that we receive from the external world. This is a form of limited or partial skepticism as opposed to total skepticism, which is actually a contradiction. If a total skeptic negates all knowledge claims then in the act of negation they are negating or refuting their own skeptical position as well, thus demonstrating that their position is untenable.

Contrary to David Hume’s writings, which are available in abundance, there is a lack of literature from the Charvaka school also known as Lokayata. Our knowledge of Charvaka is derived from the writings of his opponents through refutations and criticisms. Based on what can be derived from these sources we can say that Charvaka was the closest in Indian thought to what can be called skeptical, hence the parallel between Hume and Charvaka. The school of Charvaka was against the supremacy of the Vedas and the Brahmins, the law of karma and propagated an ethic of hedonism. Only Kama and Artha were believed in. Dharma and Moksha were rejected.

Charvaka only believed in knowledge through the five sense organs or perception. He rejected the other three sources of knowledge in Indian thought which were inference, testimony and comparison. Inference was rejected as a valid source of knowledge since there had to be an unconditional concomitance between the middle and the major premise through the existence of the minor premise. This unconditional concomitance needs a universal connection between the major and the minor premise, which can never be proved by perception, since perception is only about particulars and not about universals. Nor can we argue that we have examined a large number of cases in the past that displayed the universal connection and project that into the future. It is not necessary that the future will conform to the past. Hence inference as a source of knowledge and the ability to project the past onto the future is questionable.

Charvaka’s stance on inference is very similar to the view expressed by David Hume centuries later in his criticism of the law of causality. Hume says our idea of causality is borne out of certain experiences. When we say that A causes B what we mean is that, based on a certain number of finite experiences, we have noted that A and B are either temporally or spatially related to each other. Hume rejects the notion that just because two things have been noted together it means that they are necessarily connected to each other. Hume and Charvaka are hence in complete agreement with each other with respect to inference and the law of causality.

It was David Hume, the great skeptic, who spoke about the problem of induction in his work Treatise on Human Nature, posing the following issue: – no number of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.

Hume tried to balance the swing between the scholasticism based entirely on deductive reasoning and a naïve, unstructured empiricism. There are plenty of examples of naïve empiricism that we see among the practitioners of the stock market globally. The statement that the market never goes down by 20% in a given 3 month period can be tested but is meaningless if verified. Even if the above statement is true one can never make the logical leap from ‘the market has never gone down’ to ‘never goes or will never go down’. The past trends in the financial markets can never be an accurate barometer for future trends in the markets. The data can be used more safely to reject than to confirm the hypothesis.

Consider the following two statements:

Statement A: No Swan is Black, because I looked at four thousand swans and found none that were black.

Statement B: Not all swans are white.

We can never justifiably hold the first statement to be 100% accurate, no matter how many successive white swans we may have observed in our lives and may observe in the future. Indeed statement A was disproved by the discovery of a black swan in Australia. At the same time, the observation is sufficient to prove statement B to be true. We simply assume that just because a large event has never happened in the past it will never happen in the future in the stock markets. Why do financial analysts consider the worst scenario that happened in the past as the worst possible case? If it has been shown in the past, that the past is an unreliable factor to determine the present/near future, then why should the present be considered an accurate prediction of the future?

This is a very basic question that is ignored by the technical financial analysts and even economists in the stock markets, leading to disastrous results.

Before the discovery of Australia, people in the old world were convinced that all swans were white, an unassailable belief as it seemed completely confirmed by empirical evidence. The sighting of the first black swan might have been an interesting surprise for a few ornithologists but that is not where the significance of the story lies. It illustrates a severe limitation to our learning from observations or experience and highlights the fragility of our knowledge. One single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans. All you need is one single black swan.

What we call here a Black Swan is an event with the following three attributes:

  1. It is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to the possibility of its existence.
  2. It carries an extreme impact.
  3. In spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.

In the financial markets one of the most frequently used tools is called technical analysis. Technical analysis is a security analysis methodology for forecasting the direction of prices through the study of past market data, primarily price and volume. This is again a tool that projects the future of stock prices on the basis of certain behavioral patterns in the past and is used by many analysts to recommend purchase or sale of stocks for investors. The assumption here is that the future will be an accurate reflection of the past, which may not always be true. Even Nobel prize-winning economists with mathematical models have failed to predict the future with their supposedly flawless mathematical models.

A brilliant, easy example of the Black Swan theory is the turkey mentioned by Nicholas Taleb in his book Fooled by Randomness. A turkey can be fed for a thousand days and be well taken care of, and anyone analyzing the treatment & threat to the safety of the turkey on the 1000th day would surmise that the turkey did not face any imminent threats. However, if the 1001st day is Thanksgiving and the turkey gets eaten for supper, there is nothing in the analysis of the first 1000 days that would have led this turkey, or the analyst, to be prepared for what was coming on day 1001. This analogy holds very true for the financial markets as well.

To summarize the Black Swan: rarity, extreme impact, and retrospective (though not prospective) predictability. A small number of Black Swans explain almost everything in our world of financial markets. Furthermore, they have an enormous impact on the success of ideas and religions, the dynamics of historical events, as well as elements of our own personal lives.

If we did look into our own existence and counted the significant events, the technological changes, and the inventions that have taken place in our environment since we were born and compare them to what was expected before their advent, how many of them came on a schedule? Look at the financial markets and their highs and lows, peaks and troughs, recessions and turnarounds. How often did these things occur according to plan?

This extends to all businesses. Think about the “secret recipe” to making a killing in the restaurant business. If it were known and obvious then someone next door would have already come up with the idea and it would have become generic. The next big thing in the restaurant industry needs to be an idea that is not easily conceived by the current population of restaurateurs. It has to be at some distance from expectations. The more unexpected the success of such a venture, the smaller the number of competitors, and the more successful is the entrepreneur who implements the idea. The same applies to the shoe and the book businesses, or any kind of entrepreneurship. The same also applies to scientific theories; nobody has interest in listening to trivialities. The payoff of a human venture is, in general, inversely proportional to what it is expected to be.

Consider the worldwide dot-com bubble in 1998 and the tsunami it caused on the global stock markets. Had it been expected, it would not have caused the damage it did and an early warning system would have been put in place. What you know cannot really hurt you.

The inability to predict outliers implies the inability to predict the course of history, given the share of these events in the dynamics of history. But we act as though we are able to predict historical events, or, even worse, as if we are able to change the course of history. We produce thirty-year projections of economic data without realizing that we cannot even predict those for next summer. Our cumulative prediction errors for political and economic events are so monstrous that if we seriously retrospect we should stop forecasting. What is surprising is not the magnitude of our forecast errors, but our unawareness of it. This is all the more worrisome when we engage in deadly military conflicts: wars are fundamentally unpredictable (and we do not know it).

Our inability to predict in environments like the global financial markets subjected to the Black Swan even with the most sophisticated mathematical tools, coupled with a general lack of the awareness of this state of affairs, means that certain professionals such as economists and financial analysts, while believing they are experts, are in fact not based on their empirical record. They do not know more about their subject matter than the general population, but they are much better at confusing and overwhelming you with complex mathematical theories.

Black Swans being unpredictable, we need to adjust to their existence (rather than naively try to predict them). There are so many things we can do if we focus on the unknowable as opposed to focusing only what we do not yet know. Another related human impediment comes from excessive focus on what we do know: we tend to study the precise, not the general.

Life is about uncertainty; and the rare event equals uncertainty. There are two possible ways to approach phenomena:

  • The first is to rule out the extraordinary and focus on the “normal”. The examiner leaves aside “outliers” and studies ordinary cases.
  • The second approach is to consider that in order to understand a phenomenon, one needs to first consider the extremes particularly if, like the Black Swan, they carry an extraordinary cumulative effect.

Almost everything in financial life is produced by rare but consequential shocks and jumps; all the while almost everything studied about social life focuses on the normal, particularly with “bell curve” mathematical methods of inference that tell you next to nothing. Why? Because the bell curve ignores large deviations; it cannot handle them, yet it makes us confident that we have tamed uncertainty. Man does not learn. He continues to mistake the map for reality. In his arrogance and conceit of scientific achievement he would like to believe that modern computers along with complex mathematical tools can help predict financial and non-financial variables which rarely happen. A little dose of philosophical skepticism and humility from Charvaka and Hume could be a welcome antidote to the testosterone-driven practitioners and overconfident academics of the global financial markets. At least it would have mitigated a few stock market crashes and their bloody aftermaths.


Chatterjee, Satishcahndra & Datta, Dhirendramohan. An Introduction to Indian Philosophy. New Delhi: Rupa Publications India, 2007

Dasgupta, Surendranath. History of Indian Philosophy (Vol. 3). Cambridge, 1922

Taleb, Nassim N. The Black Swan. New York: Random House 2007

Taleb, Nassim N. Fooled by Randomness. New York: Random House, 2004


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