Occasionally in the investment advisory business I have a discussion with a prospective client where the individual is looking for justification on the merits of working with an investment professional. Typically, it begins with an inquiry as to what my client portfolio returns have been over the past year. As an example, if I respond with, “Well, in 2021 my growth-orientated portfolio returned 14%” a confident response often is, “Well I returned 26% and I don’t even have an MBA or CFA or any investment designations, so I really don’t need your services”.
It’s at this point where I question the individual as to how much risk they are taking in their investment portfolio to achieve these lofty returns. “Why does risk matter, my returns were much higher than yours?” is at times, the response. Here is where the conversation gets even more interesting as I inquire as to what type of risk measures he/she uses to quantify their portfolio risk: measures such as standard deviation, beta, Sharpe Ratio, upside/downside capture ratio, along with a host of others, can all be used to quantify portfolio risk.
What I am attempting to get at is when a client’s investment returns are much higher than benchmark or index averages an individual must be taking additional risk to achieve such returns. When the markets are flying high (stocks markets, for example, over the past 10 years) and individuals are over-weight in riskier equities, substantially higher returns can be achieved. On the other hand, as we shortly begin the final quarter of 2022 and equity markets are down sharply across the globe, those same individuals are likely seeing higher, even substantially higher losses.
As an example, these losses are even more prevalent with investors who have been holding concentrated, undiversified portfolios of previous high-flying technology shares, Covid-era stocks and small and mid-cap companies who don’t have any positive earnings in the foreseeable future (and thus have been hit exceptionally hard with the rising interest rates we have experienced so far into 2022). Just a few of these high-risk names that have hammered investor returns this year are Upstart Technologies (-81%), Shopify (-74%), Twilio (-72%), Sea (-72%) and Okta (-71%).
There are a variety of ways to measure the amount of risk an individual is taking in their investment portfolio, with the most universally accepted being ‘standard deviation’. Although impossible for investors to calculate on their own without any software assistance, standard deviation can quickly show an investor how much risk their portfolio holds. Standard deviation essentially measures how much an investor’s portfolio can stray from its longer term average if market volatility does pick up (it’s variability over time). Standard deviation is what is called an ‘absolute’ measure of risk as it is not measured in related to other assets or market returns, but rather to its own historical volatility.
Portfolio standard deviation is measured in percentage terms. As an example of inferring what standard deviation means, if an investor is solely invested in large cap stocks they could use the standard deviation of the S&P 500 as a benchmark to compare their portfolio risk to. The S&P 500’s long-term standard deviation (volatility) is around 15%. If the investor is fairly aggressive in their portfolio composition, their own portfolio could have a standard deviation much higher, as high as 30% or even more. The higher the figure unfortunately results in higher uncertainty in annual returns.
Standard deviation is based on a set of investment returns being ‘normal’, in other words, consistent over time (which is rare). Based on mathematical properties in terms of returns, 68% of a portfolio return’s data lies with 3 standard deviations of its long-term average, 95% of the returns lie within 2 standard deviations of its long-term average and 99% of a portfolio’s long-term return will lie within 1 standard deviation of its long-term average.
As such, it is very easy to see how an aggressive investor who returns 25% one year, say versus an S&P 500 return of 15%, can have wild swings in return when markets perform poorly. In our example above, if an investor’s standard deviation of returns is 40% (significantly higher than the S&P standard deviation of 15%) and the markets decline, the investor’s return in a bear market (like we are seeing in 2022) could be -15% vs an index return of 0%, based on a one standard deviation measurement.
Another popular risk measure with individual common stocks is what is called ‘beta’. In contrast to standard deviation, beta is a stock’s ‘relative’ measure of risk, meaning that beta is measured in relation to a particular stock index. Numerically, beta measures how much the stock’s returns move with returns in a particular index over time (typically measured monthly over a 5-year period). For example, if a stock has a beta of 1, its historical returns exactly mirror the returns on its comparative index.
Excessive exposure to high beta stocks can really hurt investor portfolio returns when market declines, such as what we have witnessed so far in 2022. For example, at time of writing, Facebook stock has a beta of 1.33. This means that for every 1% move (up or down) in the S&P 500 index, Facebook shares should move an additional 33%. With the S&P 500 down 15% this year, we see Facebook down even more substantially than its beta would predict, off 50% on the year.
Maybe not as common in the financial press, but still a popular measure of risk, is the Sharpe Ratio. The Sharpe Ratio measures how much excess return an investor earns per unit of risk (in this case, per unit of standard deviation). What’s interesting is that the Sharpe ratio can indicate that the excess returns an investor earns over time might actually be due to taking on a much higher level of risk, rather than his/her investing skills.
Most investors are what are called ‘rational’ and ‘selfish utility maximizers’ and will attempt to get the highest possible returns from their investments while taking the least amount of risk: this is exactly what the Sharpe Ratio measures, risk-adjusted returns. When comparing two portfolios, the portfolio with the higher Sharpe Ratio is returning more for the risk it is taking. A portfolio with a Sharpe Ratio of 1.5 would be preferred to a comparable portfolio with a Sharpe Ratio of 1 in terms of risk.
In finance, everything is quantified with numbers, and the formulas for calculating standard deviation, beta and the Sharpe Ratio are listed below. I have also attached a brief video from the Corporate Finance Institute that goes into a little more detail on the Sharpe Ratio and how it can be used to measure portfolio risk.
Beta = Covariance/Variance
Standard Deviation = (Wa X SDa)^2 + (Wb X SDb)^2 + 2 X Wa X Wb X SDa X SDb X cor (A,B)
Sharpe Ratio = Rp−Rf /σp