Expected return is calculated as the weighted average of the likely profits of the assets in the portfolio, weighted by the likely profits of each asset class. Expected return is calculated by using the following formula:

Written another way, the same formula is as follows: E(R) = w1R1 + w2Rq + ...+ wnRn

Example: Expected Return
For a simple portfolio of two mutual funds, one investing in stocks and the other in bonds, if we expect the stock fund to return 10% and the bond fund to return 6% and our allocation is 50% to each asset class, we have the following:

Expected return (portfolio) = (0.1)*(0.5) + (0.06)*(0.5) = 0.08, or 8%

Expected return is by no means a guaranteed rate of return. However, it can be used to forecast the future value of a portfolio, and it also provides a guide from which to measure actual returns.

Let's look at another example. Assume an investment manager has created a portfolio with Stock A and Stock B. Stock A has an expected return of 20% and a weight of 30% in the portfolio. Stock B has an expected return of 15% and a weight of 70%. What is the expected return of the portfolio?

E(R) = (0.30)(0.20) + (0.70)(0.15)
= 6% + 10.5% = 16.5%

The expected return of the portfolio is 16.5%.

Now, let's build on our knowledge of expected returns with the concept of variance.

Variance 2) is a measure of the dispersion of a set of data points around their mean value. In other words, variance is a mathematical expectation of the average squared deviations from the mean. It is computed by finding the probability-weighted average of squared deviations from the expected value. Variance measures the variability from an average (volatility). Volatility is a measure of risk, so this statistic can help determine the risk an investor might take on when purchasing a specific security.

Example: Variance
Assume that an analyst writes a report on a company and, based on the research, assigns the following probabilities to next year's sales:

Sales ($ Millions)

The analyst's expected value for next year's sales is (0.1)*(16.0) + (0.3)*(15.0) + (0.3)*(14.0) + (0.3)*(13.0) = $14.2 million.

Calculating variance starts by computing the difference in each potential sales outcome from $14.2 million, then squaring:

Deviation from Expected Value
(16.0 - 14.2) = 1.8
(15.0 - 14.2) = 0.8
(14.0 - 14.2) = - 0.2
(13.0 - 14.2) = - 1.2

Variance then weights each squared deviation by its probability, giving us the following calculation:

(0.1)*(3.24) + (0.3)*(0.64) + (0.3)*(0.04) + (0.3)*(1.44) = 0.96

Portfolio Variance

Now that we've gone over a simple example of how to calculate variance, let's look at portfolio variance.

The variance of a portfolio's return is a function of the variance of the component assets as well as the covariance between each of them. Covariance is a measure of the degree to which returns on two risky assets move in tandem. A positive covariance means that asset returns move together. A negative covariance means returns move inversely. Covariance is closely related to "correlation," wherein the difference between the two is that the latter factors in the standard deviation.

Modern portfolio theory says that portfolio variance can be reduced by choosing asset classes with a low or negative covariance, such as stocks and bonds. This type of diversification is used to reduce risk.

Portfolio variance looks at the covariance or correlation coefficient for the securities in the portfolio. Portfolio variance is calculated by multiplying the squared weight of each security by its corresponding variance and adding two times the weighted average weight multiplied by the covariance of all individual security pairs. Thus, we get the following formula to calculate portfolio variance in a simple two-asset portfolio:

(weight(1)^2*variance(1) + weight(2)^2*variance(2) + 2*weight(1)*weight(2)*covariance(1,2)

Here is the formula stated another way:

Portfolio Variance = w2A2(RA) + w2B2(RB) + 2*(wA)*(wB)*Cov(RA, RB)
Where: wA and wB are portfolio weights, σ2(RA) and σ2(RB) are variances and
Cov(RA, RB) is the covariance

Example: Portfolio Variance
Data on both variance and covariance may be displayed in a covariance matrix. Assume the following covariance matrix for our two-asset case:


From this matrix, we know that the variance on stocks is 350 (the covariance of any asset to itself equals its variance), the variance on bonds is 150 and the covariance between stocks and bonds is 80. Given our portfolio weights of 0.5 for both stocks and bonds, we have all the terms needed to solve for portfolio variance.

Portfolio variance = w2A2(RA) + w2B2(RB) + 2*(wA)*(wB)*Cov(RA, RB) =(0.5)2*(350) + (0.5)2*(150) + 2*(0.5)*(0.5)*(80) = 87.5 + 37.5 + 40 = 165.

Standard Deviation
Standard deviation can be defined in two ways:

1. A measure of the dispersion of a set of data from its mean. The more spread apart the data, the higher the deviation. Standard deviation is calculated as the square root of variance.

2. In finance, standard deviation is applied to the annual rate of return of an investment to measure the investment's volatility. Standard deviation is also known as historical volatility and is used by investors as a gauge for the amount of expected volatility.

Standard deviation is a statistical measurement that sheds light on historical volatility. For example, a volatile stock will have a high standard deviation while a stable blue chip stock will have a lower standard deviation. A large dispersion tells us how much the fund's return is deviating from the expected normal returns.

Example: Standard Deviation
Standard deviation (σ) is found by taking the square root of variance:

(165)1/2 = 12.85%.

We used a two-asset portfolio to illustrate this principle, but most portfolios contain far more than two assets. The formula for variance becomes more complicated for multi-asset portfolios. All terms in a covariance matrix need to be added to the calculation.

Let's look at a second example that puts the concepts of variance and standard deviation together.

Example: Variance and Standard Deviation of an Investment
Given the following data for Newco's stock, calculate the stock's variance and standard deviation. The expected return based on the data is 14%.

Expected Return
Worst Case
Base Case
Best Case

σ2 = (0.10)(0.10 - 0.14)2 + (0.80)(0.14 - 0.14)2 + (0.10)(0.18 - 0.14)2
= 0.0003

The variance for Newco's stock is 0.0003.

Given that the standard deviation of Newco's stock is simply the square root of the variance, the standard deviation is 0.0179, or 1.79%.

(For further reading, see Investing Myths That Need To Go and 5 Reasons You Should Love Stocks.)


Related Articles
  1. Investing

    Calculating Portfolio Variance

    Portfolio variance is a measure of a portfolio’s volatility, and is a function of two variables.
  2. Investing

    Explaining Variance

    Variance is a measurement of the spread between numbers in a data set.
  3. Investing

    Using Historical Volatility To Gauge Future Risk

    Use these calculations to uncover the risk involved in your investments.
  4. Trading

    Exploring The Exponentially Weighted Moving Average

    Learn how to calculate a metric that improves on simple variance.
  5. Investing

    Calculating Covariance For Stocks

    Learn how to figure out how two stocks might move together in the future by calculating covariance.
  6. Investing

    What is Covariance?

    Covariance is a concept used in statistics and probability theory to describe how two variables change when compared to one another. In business and investing, covariance is used to determine ...
  7. Investing

    Find The Highest Returns With The Sharpe Ratio

    Learn how to follow the efficient frontier to increase your chances of successful investing.
  8. Managing Wealth

    Standard Deviation

    Learn about how standard deviation is applied to the annual rate of return of an investment to measure the its volatility.
  9. Investing

    Computing Historical Volatility in Excel

    We examine how annualized historical volatility is computed from daily log returns, variance and standard deviation.
  10. Trading

    Trading With Gaussian Models Of Statistics

    The entire study of statistics originated from Gauss and allowed us to understand markets, prices and probabilities, among other applications.
Frequently Asked Questions
  1. Depreciation Can Shield Taxes, Bolster Cash Flow

    Depreciation can be used as a tax-deductible expense to reduce tax costs, bolstering cash flow
  2. What schools did Warren Buffett attend on his way to getting his science and economics degrees?

    Learn how Warren Buffett became so successful through his attendance at multiple prestigious schools and his real-world experiences.
  3. How many attempts at each CFA exam is a candidate permitted?

    The CFA Institute allows an individual an unlimited amount of attempts at each examination.Although you can attempt the examination ...
  4. What's the average salary of a market research analyst?

    Learn about average stock market analyst salaries in the U.S. and different factors that affect salaries and overall levels ...
Trading Center