CFA® Level I question of the day
30 October 2026 · Quantitative Methods
Mean-variance analysis, the basis of much of portfolio theory, uses only the expected return and the variance of returns. What is the best reason these two measures are so widely used?
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Answer: B. They give the expected outcome and its dispersion
The mean tells you what return to expect, and the variance tells you how widely actual returns scatter around it. Those are the two things an investor weighing reward against risk needs most. Formulas: mean = sum of (probability x return); variance = sum of probability x (return - mean)^2. For example, two funds that both expect 6% are very different if one has a standard deviation of 2% and the other 15%. These two measures do not capture everything, such as skewness (lopsidedness) or fat tails (extreme events).
- A. They are the only measures that can be calculated - Skewness and kurtosis can be calculated too, and sometimes matter. Mean and variance dominate because of what they are used for, not because nothing else exists.
- C. They capture every feature of a distribution's shape - They do not. Skewness and fat tails escape both measures, and that gap is the standing criticism of mean-variance analysis. Only for a normal distribution do the two fully describe the shape.
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