The Survivorship Bias

The Survivorship Bias

The term "Survivorship Bias" is often used in finance classes across the B-Schools. In slightly technical terms, it means that while analysing the performance of the market, only those companies which survived are taken into account while the companies which failed are excluded. Therefore, the results are always skewed towards the higher end of the performance.

In very simple terms, the companies, which failed in due course of times, were obviously the worst performers. If you exclude these, out of the sample, naturally the average will be higher. This is the way, performance parameters are calculated and we tend to believe the results coming out of forged sample collected by young analysts being driven by extreme greed.

Lets' take some examples of the omnipresent "Survivorship Bias" in daily life:

 "If you had invested Rs. 10000 in INFOSYS stock during the IPO of this company in 1993, you could have made Rs. 1.5 Cr by the end of 2007."

Analysts tell such facts to justify their stock recommendations. Well, they don't tell us that the IPO of INFOSYS was undersubscribed by 10% at that time and the Finance Minister of India himself had to intervene to make way for INFY to issue shares at a premium. There might be hundreds, if not thousands, similar companies which went public between 1993 and 2001. How many of them survived? If I had invested Rs. 10000 in each of these companies at the time of their IPO, what would be my portfolio value today? At the time of inception of a company, how do I know that this company is going to be an INFOSYS or a Home Trade (Remember that???). Unfortunately, nobody can see the future and hence we go back to the EXPERTS with such questions. And what do the experts do? They only give the examples of INFY, Reliance, and WIPRO.

These experts are the living legends for Survivorship Bias.

"Traders don't make money, investors do. See Warren Buffet"

This is a classic example. We always quote Warren Buffet, because he is the most successful investor. There might have been million other investors who might have lost everything during the crash of 70's and 80's. But we exclude them from the sample and analyze investors like Warren Buffet and Benjamin Graham. On the other hand, there might be thousand of traders, who opt to exit the market at right time and made a fortune. Do we analyse them? No, because no one knows them. We know, either Warren Buffet or John Meriwether (LTCM founder).

 "Amitabh Bachchan was rejected by All India Radio because of his voice was not good enough. Later his voice became the most sought after in India."

No doubt about it. But All India Radio might have rejected hundred others also. What about them? People always give example of Shah Rukh Khan, Rajnikanth and Akshay Kumar who didn't have any godfather, struggled very hard and made it to the top at the end. They forget to tell us that there were thousand others, who might have been equally talented, worked hard enough but couldn't succeed. We always read and hear the examples of those people who succeeded in the end. We don't take into account those, who failed. Isn't it a clear cut case of Survivorship Bias?

 "Politicians are corrupt everywhere"

"Politics is supposed to be the second oldest profession. I have come to realize that it bears a very close resemblance to the first."- Ronald Reagan. This is a sad part. But the reality is there have been politicians who have saved their countries from doom. Be it, Churchill, Rossevelt, Reagan or Z.A. Bhutto. Even in India, where politics is supposed to be the dirtiest profession one can get into; there have been few politicians after Independence, who changed the face of the country. Indira Gandhi paved way for Green Revolution and White Revolution. She won the war against Pakistan in the most decisive way. When she started behaving like an autocrat, we had JP. When our relation with China was at its nadir, we had A.B. Vajpayee. When we were about to default on our loan, we had Manmohan Singh. The problem is that we always look at people having billions of dollars in Swiss banks and make a notion about politicians. We never look at the good politicians for analysis. Isn't it an example of Survivorship Bias?

But why exactly does this happen? Why we never realise that we are making the same universal mistake again and again.  The reason can be attributed to the human tendency. We always hear what we like to hear. This is a basic human tendency to find patterns out of a bunch of random numbers. In technical terms this is called "The Narrative Fallacy". The term made popular by Nasim Nicholas Taleb in his book, "The Black Swan".  What it means is that, human beings always listen to what suits us and our mind filters out the opposite views and something which doesn't fit into our notions. This is the basic reason behind all kinds of Survivorship Bias.