DEFINITION of 'Coefficient Of Variation  CV'
A statistical measure of the dispersion of data points in a data series around the mean. It is calculated as follows:
The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from each other.
INVESTOPEDIA EXPLAINS'Coefficient Of Variation  CV'
In the investing world, the coefficient of variation allows you to determine how much volatility (risk) you are assuming in comparison to the amount of return you can expect from your investment. In simple language, the lower the ratio of standard deviation to mean return, the better your riskreturn tradeoff.
Note that if the expected return in the denominator of the calculation is negative or zero, the ratio will not make sense.

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What are some of the uses of the coefficient of variation (COV)?
In statistics, the coefficient of variation (COV) is a simple measure of relative event dispersion. It is equal to the ratio ... Read Full Answer >> 
What are some of the advantages of using coefficient of variation (COV)?
There are several advantages associated with using coefficient of variation (COV). COV is a statistical measure that is normalized ... Read Full Answer >> 
What are some of the disadvantages of using coefficient of variation (COV)?
Among the disadvantages of using coefficient of variation (COV) is the inability to calculate it at all if the mean of the ... Read Full Answer >> 
What can the coefficient of variation (COV) tell investors about an investment's ...
The coefficient of variation (COV) can determine the volatility of an investment. The COV is a ratio between the standard ... Read Full Answer >> 
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The common assumptions made when doing a ttest include those regarding the scale of measurement, random sampling, normality ... Read Full Answer >> 
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The most common types of regression an investor can use are linear regressions and multiple linear regressions. Regressions ... Read Full Answer >>