Common sense suggests that “Buying low and selling high” is perhaps the best way to make money on your investments. But this is easier said than done, even for the most experienced investors. There are many factors at play when it comes to any market - debt or equity, and all of them are inextricably linked.

A simpler approach to long term investing is disciplining and committing to a fixed sum for a fixed period and sticking to this schedule regardless of the conditions of the market. Rupee cost averaging, as this practice is called, in a way ensures that you automatically buy more units when the NAV is low and fewer when the NAV is high This discipline in turn suppresses the natural instinct to stop investing in a falling or a depressed market or investing a lot when markets are rising and buoyant. However, you should remember that the Rupee cost averaging does not assure a profit, nor does it protect you against investment losses in declining markets. It ensures that by disciplining your entry into markets, you override sentiments therefore reducing the risk of committing a fixed sum when markets are higher.

**Returns**

There are many ways to calculate your returns, though the two most popular methods are Absolute returns and Annualised returns.

**Understanding Absolute returns**

Absolute return is the simple increase (or decrease) in your investment in terms of percentage. It does not take into account the time taken for this change. So if an investment’s current market value is Rs. 5,25,000 and your invested amount was Rs. 2,75,000 then your absolute return will be:

[(5,25,000-2,75,000)/2,75,000] = 90.9%

Notice how irrelevant the date of investment or date of redemption is. Ideally, you should use the absolute returns method if the tenure of your investment is less than 1 year.

For periods of more than 1 year, you need to annualise returns; which means you need to find out what the rate of return is per annum.

**Understanding Annualised returns**

A compound annual growth rate (CAGR) is what measures the rate of return over an investment period. It is a smoothened rate because it measures the growth of an investment as if it had grown at a steady rate, on an annually compounded basis.

Beta measures the volatility of a security relative to something, usually a benchmark index. A beta greater than one means the fund or stock is more volatile than the benchmark index, while a beta of less than one means the security is less volatile than the index.

If the market goes up by 10%, a fund with a beta of 1.0 should go up 10% and vice versa. While standard deviation determines the volatility of a fund according to the disparity of its returns over a period of time, beta, determines the volatility, or risk, of a fund in comparison to that of its index or benchmark.

Beta is based on the capital assets pricing model which states that there are two kinds of risk in investing in equities- systematic risk and non-systematic risk. Systematic risk is integral to investing in the market and cannot be avoided. Eg. risk arising out of inflation and interest rates. Non-systematic risk is unique to a company - can be minimised by diversification across companies. Since non-systematic risk can be diversified, investors need to be compensated for systematic risk which is measured by Beta.

**Volatility**

Usually denoted with the letter σ, Standard Deviation is defined as the square root of the variance.

It basically serves as a measure of uncertainty. Volatile securities that have a higher standard deviation are also considered a higher risk because their performance may change quickly, in either direction and at any moment. So the standard deviation of a fund measures this risk by measuring the degree to which the fund fluctuates in relation to its mean return i.e. the average return of a fund over a period.

For example, a fund that has a consistent four year return of 3%, would have a mean, or average of 3%. The standard deviation for this fund would then be zero because the fund's return in any given year does not differ from its four year mean of 3%.

The standard deviation of a set of data measures how "spread out" the data set is. In other words, it tells you whether all the data items bunch around close to the mean or if they are "all over the place."