On February 28th 2001, the Finance Minster presented what was thought to be one of the most market savvy budgets of the decade. Not in his wildest dreams would the FM have thought that post budget all hell will break loose. It did. And when the carnage ended it left behind a trail of bodies. No not of the bulls or the bears, but of the ‘small investors’ as always.
Not to say that the individual investors were not at fault but they could have never guessed the corruption in the exchanges and complacency of the regulators. Their undoing was greed, to make money as quickly as possible. Somehow it was imbibed into them that stocks exchanges were the quickest route to riches. There objective was simply to maximize their returns. But they forgot one fundamental principle ‘more the risk, more the return’. In other words, if there are unrealistic returns expected, the risks too are unimaginable.
It is very interesting to note that there is far greater interest in the returns rather than the risk involved. If there was no risk why would any return be more than that the return to government securities? Therefore, investors must appreciate one very simple fact that if they are looking at returns, they need to look at risk.
Then the question is what is risk? Risk can be explained as a quantification of the outcome not meeting expectations. To put it very simply, things going wrong. Now the problem is how do we measure risk?
Let us look at a few measures of risk used and how they have evolved over time.
Volatility
The simplest measure of risk, which says what is the standard (average) deviation of the values (stock price) from the mean. A measure of how much the returns have missed the mark by. Greater the deviation greater the risk. As this is a statistical concept it is very difficult to interpret. The unit of measurement is percentage (%). The returns should be seen in combination with the standard deviation.

Standard Deviation 
Mean returns 
Infosys 
3.4% 
0.40% 
HLL 
2.2% 
0.10% 
Sensex 
1.8% 
0.03% 
Based on daily returns for the period from 15 Dec,1995 to 4th Apr, 2001
One of the most prolific users of standard deviation was Harry Markowitz. He came out with his portfolio theory and revolutionized the way people selected stocks. He introduced the concept of portfolio risk diversification i.e don’t put your all your eggs in one basket. According to him the combined risk arising from two stocks depended not only on the risk of the individual stocks but also on the how closely their price move together (correlation). Therefore, if two stocks have almost no correlation then the combined risk of the two stocks can be lower than the lowest of both the stocks involved.
The premise was that same factors would not affect the stocks and therefore, risk would be lower. Probably losses on one of the stocks would be offset by gains on the other stock. Therefore risk was broadly categorized into diversifiable and nondiversifiable risk. The diversifiable risk can be made to disappear by a combination of stocks but the nondiversifiable risk has to be borne by the investor.
The lesson here is very simple. Don’t put all your money into one sector. What if the sector has a derating? Obviously all the stocks of the sector are going to take a hit. The IT sector was recently derated as its largest market (the US) was facing tough economic environment and therefore, the IT spend in the US dropped. This meant lower revenue growth for the industry.
William Sharpe extended Markowitz diversification principle. According to Sharpe, if there was a part of the risk that could be done away with, why would there be returns for it? Therefore, only that part of the risk that cannot be diversified would be rewarded. He decomposed risk into two components. Systematic risk (nondiversifiable) that affects all stocks and is more macro in nature and unsystematic risk (diversifiable) that comprises of the stock specific risks.
Beta
Beta is the measure of the nondiversifiable or market risk. It gives a measure of how much would the stock price change if the market moved by a particular amount i.e. the sensitivity of the stock to market movements. Therefore, it quantifies the extent of dependence of the stocks price on the macro or market factors. Suppose a stock has a Beta of 0.5, if the market moves by a certain amount the stock is likely to move by half the amount, as its sensitivity to the market is low. Again if a stock has a beta of 2, then the impact of market movement on the stock is likely to be magnified and be of twice the amount.

Beta 
HLL 
0.68 
Infosys 
1.04 
Based on daily returns for the period from 15 Dec,1995 to 4th Apr, 2001 
Both these measures do quantify risk but what have one major flaw that is they do not say what will be the loss in rupee terms and what is the probability of this happening. For example volatility tells us that Infosys is more volatile than HLL and more risky. Also Beta tells us that Infosys is more likely to be affected if the markets move as compared to HLL. But it still doesn’t answer the question that what amount of money does the investor stand to lose. This answer is provided by a measure known as value at risk (VaR).
Value at Risk
VaR is generically defined as the maximum possible loss (in Rs terms) for given position or portfolio within a known confidence interval or a specific time interval. There are number ways to measure VaR.
But basically VaR can be thought of comprising two components.
 The sensitivity of a portfolio or positions to the change in markets
 The probability distribution of the markets over the desired reporting period horizon.
If we combine both the components above then we are able to say with a certain level of confidence what will be the stock price movement in a day.
Confidence Level 
68% 
90% 
95% 
99% 
HLL (VaR) 
2.2% 
3.6% 
4.3% 
5.7% 
Var for Rs 10,000 
220.0 
363.0 
431.2 
567.6 
Infosys (VaR) 
3.2% 
5.3% 
6.3% 
8.3% 
Var Rs 10,000 
320.0 
528.0 
627.2 
825.6 
Based on daily returns for period from 15th Dec, 1995 to April 4, 2001
Here for 68% confidence level that can be interpreted as on 68 days out of hundred the VaR is 2.2% (1 times std deviation) of the portfolio value. For an investment of Rs 10,000, the value at risk works out to be Rs 220. Therefore on 68 days out 100 an investor will not lose more than Rs 220 in a day for an investment of Rs 10,000 on HLL.
Similarly for Infosys at a confidence level of 99% can be interpreted as 99 days out of hundred the VaR is 8.3%. Therefore, in 99 days out 100 an investor will not lose more than Rs 826 in a day on an investment of Rs 10,000 on Infosys.
These are just a few methods used to quantify risk. The purpose of this article was to introduce risk as a measure and stress the importance of valuing risk while investing in volatile markets.
Investing in stock market is not a gamble. But again it’s certainly not a sure way to make money nor is it a sure way to lose it either. Investing calls for lot of research and thinking before you put in your money. Luck can sometimes help you get some easy money but in more than a few cases luck runs out. Therefore, look before you leap. And leap because you know the risk involved not because you got a hot ‘tip’.
There is no easy way to make money, at least not on the stock markets; not that we know of. If you still think otherwise, best of luck to you. You are going to need tonnes of it.
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