Standard deviation might sound like a concept pulled straight from a statistics textbook, but it has real value in your day-to-day work as a financial advisor. When you talk to your clients about risk, volatility, and expected returns, you are already working with ideas that standard deviation can quantify.
In this article, Wealth Professional Canada will explore everything you need to know about standard deviation. We'll discuss how to calculate this measure and how it relates to the risk your clients accept in their portfolios. You can also find the latest news on standard deviation when you scroll to the bottom!
Standard deviation is a statistical measure that describes how spread out the values in a dataset are from their average. In investing, that dataset is usually a series of returns over time, such as monthly or yearly returns for a stock, fund, or portfolio.
When returns sit close to their average result, the standard deviation is low. When returns swing widely around that average, the standard deviation is high. In other words, it is a way to put a number on volatility.
From a statistical viewpoint, standard deviation focuses on how far each observation sits from the mean of the dataset. From a financial viewpoint, this same idea can help you estimate how unstable returns have been and how uncertain future outcomes might be.
For a financial advisor, standard deviation is useful because:
Watch this video to learn more about standard deviation:
Want to be credible and successful like those who made it to our Best Financial Advisors in Canada list? Encourage your clients to talk about risk and use standard deviation to show how much their investments have moved around their average returns.
In practice, volatile stocks often show high standard deviations, while stable blue chip stocks show lower ones. The same idea applies across asset classes. Equity funds that hold smaller, fast-moving companies will often show higher values than conservative bond funds.
Standard deviation is also closely linked to variance. Variance measures how far each value in a dataset sits from the mean, but it squares these differences. That squaring changes the unit of measure, which can make variance harder to interpret.
Standard deviation is simply the square root of variance. This step brings the result back into the original unit of the data. This makes it easier to relate to price and return movements.
You do not need to be a statistician to use standard deviation in your work. The calculation follows a series of simple steps. First, here's the general formula for standard deviation (click the arrow to see what each symbol means):
Where:
Now, let's go over these four steps in calculating the standard deviation:
Standard deviation can be calculated in slightly different ways, but for investment returns the most common approach is the sample standard deviation formula above. You apply it to a series of returns over a specific period. You might work with:
What's important is that you stay consistent within your calculations.
The mean is simply the arithmetic average of the returns in your dataset. To find it, add up all the returns and divide by the number of returns.
Variance is the first part of the standard deviation calculation. It measures how spread out the returns are around the mean. To compute variance:
The final step is to bring the measure back into the original unit. To do that, you take the square root of the variance. The square root reverses the squaring you did earlier.
As such, the standard deviation is expressed in the same terms as the original returns. If your returns are in percentages, your standard deviation will also be in percentage terms.
At this point you have a single number that summarizes how much the returns in your dataset have tended to move away from their average value.
There is no single ideal number that fits every investment or every client. Standard deviation always needs to be read in context. Plus, it depends on factors such as:
You also need to consider their comfort with volatility and how much fluctuation they can tolerate while staying invested. A level that feels acceptable for a growth-focused portfolio might feel too high for someone who relies on their investments for current income.
In investment analysis, a smaller standard deviation points to returns that have stayed within a narrow band around their average. That means less volatility and usually less risk.
A larger standard deviation points to returns that have moved more sharply up and down. This suggests more volatility and more risk. You can see this when you compare:
Even though standard deviation is closely linked to volatility, it does not tell you whether an investment is attractive in other ways. This is a critical point when you are guiding your clients. Here are five other limitations that can come into play:
Let's discuss these five one by one:
Standard deviation does not say whether returns are positive or negative overall. An asset can show a low standard deviation but still deliver poor performance if those stable returns sit below inflation or your clients' required return.
Another asset can show high standard deviation but still be profitable if strong positive returns outweigh the down periods. In these cases, higher volatility reflects a bumpier path to an outcome that could still meet or exceed your clients' long-term goals.
Standard deviation does not speak to the:
You still need to conduct a separate fundamental analysis to judge whether the investment itself makes sense for your clients.
Standard deviation also rests on some statistical assumptions. It is often treated as if returns follow a normal distribution. Real markets can behave very differently, with more extreme moves than a normal distribution would suggest.
Another concern is sensitivity to outliers. A few extreme returns, such as a very sharp drop or sudden spike, can have a large effect on the calculated standard deviation. This can make the measure less reliable if you look at it in isolation.
Given these limits, standard deviation works best as one part of your risk assessment, not as the only measure. In conversations with your clients, you can use standard deviation to:
Remind your clients that standard deviation measures dispersion of returns, not quality of the investment. It is only one piece of the risk puzzle. Standard deviation must still be combined with intrinsic value analysis and an understanding of cash flows.
Standard deviation can feel abstract at first, but once you become comfortable with it, the measure can fit smoothly into how you guide your clients. When you compare options for your clients' portfolios, standard deviation encourages thoughtful risk discussions.
A higher standard deviation is not automatically bad, just as a lower value is not automatically good. What matters is how that volatility lines up with the client's willingness to accept swings in value and the returns needed to meet their goals.
It is also vital to stay aware of the limits. Standard deviation assumes a certain pattern in return data and reacts strongly to unusual results. For this reason, it should sit alongside, not replace, your wider review of each investment and your assessment of value.
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