Volatility – The Short and Long of it!

In short: Let us say the price of a stock for five days are as follows:- Day 1- 1000 Day 2- 1020 Day 3- 1030 Day 4- 990 Day 5- 960

Then, the average price over the last five days has been (1000+1020+1030+990+960)/5 = 1000

Thus, volatility = Square Root of the average of the Squared sum of spot differences

=> Volatility= Sqrt [(1/5)*{((1000–1000)^2) + ((1020–1000)^2) + ((1030–1000)^2) + ((990–1000)^2) + ((960–1000)^2) }]

=> Volatility = 24.4949

In long:

Research in the field of stock prices and movements have shown that the price of stock and the return (for advanced readers, read log returns) is random in nature. Further inspection has revealed that even while the (log) returns from the stock prices are random, they follow a normal distribution. This is a monumental discovery. A random stochastic process, that was thought to be beyond the grasp of explanation, was now within some control of scientific analysis.

Now, from statistics, we know that most major distributions have parameters associated with them. In the case of the normal distribution, it is defined by its mean and standard deviation. Research has shown that the expected (read average) stock market return is 0%. So one of the parameters of the distribution is 0. But the market gives random returns from time to time, that are normally distributed around zero. The following graph will help you visualize it.

Here we see, that even though the mean is zero, the standard deviation is 1. What it means is that even when we expect a return of 0% in the long run there are periods in between when the market swing causes the returns to fluctuate at every instant, and that swing in 65% of the cases will not exceed one-unit standard deviation. This fluctuation in the return is caused by the massive number of trades that are taking place and changing the price of the stock. This standard deviation is then referred to as the volatility of the stock.

This has immense consequences. This actually grants to us some degree of prediction.

Let us say that the long term expected return is 0%, and the volatility is 5%. This means that that for 65% of all cases, we cannot earn more than 5% or lose more than 5%.

This also implies that no more that 5% cases will be there where we can hope to earn more than or equal to 10% (2*5%) or lose more than or equal to 10%.

With that knowledge we can now take better decisions and expect a more realistic return from our investments.

For more such analysis, head over to our YouTube channel.

I hope this article was able to help you learn both how to calculate volatility and use it for your investment decisions.

Feel free to reach out to us at insigniainvestments@gmail.com

Happy investing!

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