According to NASDAQ, the Coefficient of Variation is a measure of investment risk that defines risk as to the standard deviation per unit of expected return. Coefficient of Variation, though believed by many, is not a perfect measure of forecastability.

However, if used in the true sense, it may surely add value to a business’s forecasting process. In the world of forecasting, one of the key questions to consider is the forecastability of a particular set of data.

For example, a salesman might consistently be better at forecasting compared to his or her colleague.

The question that strikes me is: Is it because the data assigned to them is more forecastable, or is the salesman in question more skilled at forecasting?

One of the ways demand planners have tried to answer this question is through the use of a calculation called the Coefficient of Variation (CV).

Some people call it a standardized or normalized standard deviation (StdDev). In this discussion on the Coefficient of Variation, we will try to answer that question as well.

**Coefficient of Variation and MPT**

According to Modern Portfolio Theory (MPT), investment risk is defined and measured largely by volatility. MPT further expresses that all investors are rational and operate with perfect knowledge in a perfectly efficient marketplace.

Such investors will not accept a known level of risk/volatility unless they receive a return that precisely rewards them for that risk. This means that all securities sell at a market price that is always equal to fair or intrinsic value.

In this post, we will explain the Coefficient of Variation in details. We will start with the definition of Coefficient of Variation, its purpose, and then move on to discuss the Coefficient of Variation formula in use, and lastly, Coefficient of Variation examples. I aim to cover the following in the discussion on the Coefficient of Variation:

(i) What is the Coefficient of Variation

(ii) Coefficient of Variation formula

(iii) How to Calculate the Coefficient of Variation Formula

(iv) Coefficient of Variation examples

**What is the Coefficient of Variation: ****Coefficient of Variation Definition**

The Coefficient of variation (CV) may be defined as a statistical measure of the dispersion of data points in a data series around the mean. In other words, the Coefficient of Variation represents the ratio of the standard deviation to the mean.

Coefficient of Variation is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from one another.

The Coefficient of Variation helps you find out the extent of variability of data in the sample in relation to the mean of the population. In the finance domain, the coefficient of variation allows investors to determine the extent of volatility, or risk, to be assumed in comparison to the amount of return expected from investments.

It is to be noted that the lower the ratio of the standard deviation to mean return, the better risk-return trade-off. Note that if the expected return in the denominator is negative or zero, the coefficient of variation could be misleading.

The Coefficient of Variation is also useful when using the risk/reward ratio to select investments. For example, an investor who is risk-averse may want to consider assets with a historically low degree of volatility and a high degree of return, in relation to the overall market or its industry.

Conversely, risk-seeking investors may look to invest in assets with a historically high degree of volatility.

While most often used to analyze dispersion around the mean, quartile, quintile, or decile Coefficient of Variation can also be used to understand variation around the median or 10th percentile, for example.

After knowing what is the Coefficient of Variation let us move forward to its examples.

**Coefficient of Variation Example**

We may explain with the following example of Coefficient of Variation. Ronny wants to find new investment for his portfolio. He is looking for a safe investment that provides stable returns. He considers the following options for investment:

Stocks: Ronny was offered stocks of XYZ Corp. It is an established company with strong operational and financial performance. The volatility of the stock is 10% and the expected return is 14%.

ETFs: Another option is the Exchange-Traded Fund (ETF), which tracks the performance of the S&P 500 index. The ETF offers an expected return of 13% with a volatility of 7%.

Bonds: Bonds with excellent credit ratings offer an expected return of 3% with 2% volatility.

In order to select the most suitable investment opportunity, Ronny decided to calculate the coefficient of variation of each option. Using the formula above, he obtained the following results:

**More of the Coefficient of Variation Examples**

Based on the calculations above, Ronny wants to invest in ETF because it offers the lowest coefficient (of variation) and the most optimal risk-to-reward ratio.

**Coefficient of Variation Examples to take into account**

Video on the Coefficient of Variation example:

**Coefficient of Variation Formula**

Mathematically, the standard formula for the Coefficient of Variation may be represented in the as follows:

Where:

σ – the standard deviation

μ – the mean

In the context of finance, the Coefficient of Variation Formula** **may be re-interpreted as follows:

**How to Calculate the ****Coefficient of Variation Formula**

**How to find a Coefficient of Variation in Exce**l

To calculate the coefficient of variation in Excel use the formulas for standard deviation and mean. For a given column of data (i.e. A1:A10), you could enter: “=stdev(A1:A10)/average(A1:A10)) then multiply by 100.

**How to Find a Coefficient of Variation by hand**

You may use the following formula to calculate the Coefficient of Variation by hand for a population or a sample.

**How to find a Coefficient of Variation**

Where σ is the standard deviation for a population, which is the same as “s” for the sample.

Where μ is the mean for the population, which is the same as XBar in the sample.

In other words, to find the coefficient of variation, you may divide the standard deviation by the mean and multiply by 100.

**Easy Steps to create a Coefficient of Variation by hand**

Sample question: Two versions of a test are given to students. One test has pre-set answers and a second test has randomized answers. Find the coefficient of variation.

Step 1: Divide the standard deviation by the mean for the first sample:

11.2 / 50.1 = 0.22355

Step 2: Multiply Step 1 by 100:

0.22355 * 100 = 22.355%

Step 3: Divide the standard deviation by the mean for the second sample:

12.9 / 45.8 = 0.28166

Step 4: Multiply Step 3 by 100:

0.28166 * 100 = 28.266%

Now you can compare the two results using the Coefficient of Variation.

**Looking beyond the Coefficient of Variation**

Does the Coefficient of Variation interest you? Look up for reference matters on Coefficient of Variation examples, to know more latest researches on Coefficient of Variation.

Also, read about the different approaches followed psychologists for applying Coefficient of Variation formula. Data Science, which has a lot common with Coefficient of Variation has also gained immense importance in the recent past.

Watch out more insightful writings and discussions on Coefficient of Variation, take part in debates and intellectual discourses to enhance your knowledge.

Check out informative videos that take you through Coefficient of Variation formula and Coefficient of Variation examples. Be a part of the well-known Coefficient of Variation communities for regular knowledge updates and share your views on the Coefficient of Variation trends.

**Future of Coefficient of Variation**

Has my post sparked an interest in you about Coefficient of Variation? Does researching about Coefficient of Variation intrigue you? Then you should go for a career in the Coefficient of Variation research. Coefficient of Variation has immense prospects.

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Know more about the future of Data Analytics in India. However, I believe with adequate training and regular study, you can also become a skilled resource and aim for a high-flyer data science professional role.

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**Coefficient of Variation and Data Analytics**

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