Standard Deviation
 Reviewed by SOMER ANDERSON on April 15, 2021
What Is Standard Deviation?
Understanding the Standard Deviation
Example of Standard Deviation
What Is Standard Deviation?
A standard deviation is a statistic that measures the dispersion of a dataset relative to its mean. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
KEY TAKEAWAYS:
• Standard deviation measures the dispersion of a dataset relative to its mean.
• A volatile stock has a high standard deviation, while the deviation of a stable blue-chip stock is usually rather low.
• As a downside, the standard deviation calculates all uncertainty as risk, even when it’s in the investor's favor—such as above-average returns.
Play video on original page

1:52
Standard Deviation
Understanding the Standard Deviation
Standard deviation is a statistical measurement in finance that, when applied to the annual rate of return of an investment, sheds light on that investment's historical volatility. The greater the standard deviation of securities, the greater the variance between each price and the mean, which shows a larger price range. For example, a volatile stock has a high standard deviation, while the deviation of a stable blue-chip stock is usually rather low.
The Formula for Standard Deviation
Standard Deviation=
n−1
i=1
n
 (xi− x )
2
where:
x
i
=Value of the i
th
point in the data set
 x =The mean value of the data set
n=The number of data points in the data set
Calculating the Standard Deviation
Standard deviation is calculated as follows:
The mean value is calculated by adding all the data points and dividing by the number of data points.
The variance for each data point is calculated by subtracting the mean from the value of the data point. Each of those resulting values is then squared and the results summed. The result is then divided by the number of data points less one.
The square root of the variance—result from no. 2—is then used to find the standard deviation.
Using the Standard Deviation
Standard deviation is an especially useful tool in investing and trading strategies as it helps measure market and security volatility—and predict performance trends. As it relates to investing, for example, an index fund is likely to have a low standard deviation versus its benchmark index, as the fund's goal is to replicate the index.
On the other hand, one can expect aggressive growth funds to have a high standard deviation from relative stock indices, as their portfolio managers make aggressive bets to generate higher-than-average returns.
A lower standard deviation isn't necessarily preferable. It all depends on the investments and the investor's willingness to assume risk. When dealing with the amount of deviation in their portfolios, investors should consider their tolerance for volatility and their overall investment objectives. More aggressive investors may be comfortable with an investment strategy that opts for vehicles with higher-than-average volatility, while more conservative investors may not.
Standard deviation is one of the key fundamental risk measures that analysts, portfolio managers, advisors use. Investment firms report the standard deviation of their mutual funds and other products. A large dispersion shows how much the return on the fund is deviating from the expected normal returns. Because it is easy to understand, this statistic is regularly reported to the end clients and investors.
Standard Deviation vs. Variance
Variance is derived by taking the mean of the data points, subtracting the mean from each data point individually, squaring each of these results, and then taking another mean of these squares. Standard deviation is the square root of the variance.
The variance helps determine the data's spread size when compared to the mean value. As the variance gets bigger, more variation in data values occurs, and there may be a larger gap between one data value and another. If the data values are all close together, the variance will be smaller. However, this is more difficult to grasp than the standard deviation because variances represent a squared result that may not be meaningfully expressed on the same graph as the original dataset.
Standard deviations are usually easier to picture and apply. The standard deviation is expressed in the same unit of measurement as the data, which isn't necessarily the case with the variance. Using the standard deviation, statisticians may determine if the data has a normal curve or other mathematical relationship. If the data behaves in a normal curve, then 68% of the data points will fall within one standard deviation of the average, or mean, data point. Larger variances cause more data points to fall outside the standard deviation. Smaller variances result in more data that is close to average.
A Big Drawback
The biggest drawback of using standard deviation is that it can be impacted by outliers and extreme values. Standard deviation assumes a normal distribution and calculates all uncertainty as risk, even when it’s in the investor's favor—such as above-average returns.
Example of Standard Deviation
Say we have the data points 5, 7, 3, and 7, which total 22. You would then divide 22 by the number of data points, in this case, four—resulting in a mean of 5.5. This leads to the following determinations: x̄ = 5.5 and N = 4.
The variance is determined by subtracting the mean's value from each data point, resulting in -0.5, 1.5, -2.5, and 1.5. Each of those values is then squared, resulting in 0.25, 2.25, 6.25, and 2.25. The square values are then added together, giving a total of 11, which is then divided by the value of N minus 1, which is 3, resulting in a variance of approximately 3.67.
The square root of the variance is then calculated, which results in a standard deviation measure of approximately 1.915.
Or consider shares of Apple (AAPL) for the last five years. Returns for Apple’s stock were 12.49% for 2016, 48.45% for 2017, -5.39% for 2018, 88.98% for 2019 and, as of Sep., 60.91% for 2020. The average return over the five years using a geometric mean calculated was 36.88%.1
The absolute value of each year's return minus the mean is thus 24.39%, 11.57%, 42.27%, 52.1%, and 24.03%, respectively. All those values are then squared to yield 0.059, 0.013, 0.179, 0.271, and 0.058. The sample variance is the average of the squared difference, or 0.145, where the squared values are added together and divided by 4 (N minus 1). The square root of the variance is taken to obtain the standard deviation of 38.08%.
ARTICLE SOURCES
Related Terms
Volatility
Volatility measures how much the price of a security, derivative, or index fluctuates. more
How the Residual Sum of Squares (RSS) Works
The residual sum of squares (RSS) is a statistical technique used to measure the variance in a data set that is not explained by the regression model. more
Using the Variance Equation
Variance is a measurement of the spread between numbers in a data set. Investors use the variance equation to evaluate a portfolio’s asset allocation.more
T-Test Definition
A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. more
Descriptive Statistics Definition
Descriptive statistics is a set of brief descriptive coefficients that summarize a given data set representative of an entire or sample population.more
How the Sum of Squares Statistical Technique Works
Sum of squares is a statistical technique used in regression analysis to determine the dispersion of data points from their mean value. In a regression analysis, the goal is to determine how well a data series can be fitted to a function that might help to explain how the data series was generated. more
Related Articles