With the advent of “Modern Portfolio Theory” (MPT) introduced by Nobel Laureat Harry Markowitz in 1952, the investment industry has dedicated a MASSIVE amount of resources to managing risk over the past seventy-five years. Academic discussions about risk, including the sometimes esoteric statistical “language” used to describe risk, is most often inaccessible to the general investing public. Most individual investors simply don’t think of risk along the dimensions imbedded in statistical financial models. For example, describing risk as “standard deviation” or “mean variance” might be very useful in managing risk using computers equipped with mathematical software, but it isn’t useful in helping the average person understand risk or how risk is modeled and managed. The following missive is an effort to “bridge the gap” between what is happening inside of risk management software and the average person’s understanding of risk.

So, let’s begin with a description of risk that is the basis for all financial risk models and risk management software yet also will be accessible to almost everyone. Here goes:

Risk is exposure to unexpected bad outcomes

Let’s break this down.

The first thing, it has got to be exposure to something. If a fire burns down a neighborhood, but you don’t own a house in that neighborhood, then you’re not exposed to this specific risk; it’s someone else’s (this is akin to a stock you don’t own going down a lot. It’s not your risk). The potential for a fire to burn down YOUR neighborhood is a generalized risk to which a probability can be assigned. When you buy fire insurance for your home, you are paying a price monthly to protect you from the risk that your home might burn down. Every homeowner faces this risk. Renters do not face the same exposure (though loss of possessions is still a renter’s risk, just not real property value risks from fire.) So, when we say we have risk, the first thing is it involves exposure to something. One last thought: if you don’t own any stocks, you don’t face a risk like Black Monday which occurred on October 19, 1987, and on which day the stock market went down 22%. If you own stocks, then you face this generalized risk. What risk models attempt to do is to quantify your probabilistic exposure to a risk like Black Monday that may happen again.

The second term in our definition of risk that requires some thought is the conditional term “unexpected.” This is saying that if you expect that a specific risk will at some point occur or accept that the risk may occur with some assigned probability, then you can incorporate that potential outcome into your planning (and into your risk management model.) But if the risk happens at a level beyond your expectations (that is, it is unexpected) then is this is a risk that can be modeled or managed? For example, now that we know a day like Black Monday can occur, we can incorporate this into our thinking about exposure to the stock market. The once unexpected outcome (down 22% in a day) can now be incorporated into our statistical models. Or a COVID-like event can now be incorporated into our models. But what of something worse? What if we had a day in which the stock market went down 50% (more than twice as bad as Black Monday)? If this risk could happen, would you own as much stock as you do?  This is a very important question: what of the unexpected risks we face (or what Donald Rumsfeld once defined as “the unknown unknown”) are way bigger than anything we’ve ever seen or experienced? The so-called “unknown unknown.” Risk models cannot address these unless we “shock them” with some outcome that seems impossible or implausible.  Is it possible for stocks to be down 50% in a day, however small the probability? What about going down 90% in a day? Food for thought! Should we be attempting to incorporate these tiny probability (but still possible) outcomes into our thinking and risk modeling? Or do we want to ignore them because they likely will not happen in our lifetimes?

Sometimes metaphors or examples help us conceptualize the “unknown unknowns.”  For example, in 2016 a tragedy befell two twin brothers in Canada when they broke into a winter Olympic bobsled track with their sled. They were going to ride down the bobsled track and film it for YouTube. Unfortunately, they didn’t understand that the bobsled track was equipped with gates that could be closed when not in use for official events. They assumed the track would be wide open like they had seen on TV. Tragically, they collided with a gate at high velocity and died. This is an unknown unknown! If they had even thought that there could be a closed gate on the track, they almost assuredly would not have sledded down the track.

The final concept from our definition of risk that needs to be addressed is “bad outcome.” Some statistical measures assign risk as any outcome that is outside of expectations (good or bad). But, we have yet to meet an investor who would say “darn, I got a 100% return, and I only thought the best case was 50%!” All investors welcome upside or positive unexpected outcomes. Similarly, all investors eschew bad outcomes. And they tend to feel way worse about unexpected bad outcomes than they do bad outcomes that they knew in advance were possibilities. People don’t like any bad outcome but feel particularly bad about outcomes they didn’t understand in advance could happen (think of the twin brothers.) Another thing about how people tend to “feel” about bad outcomes is this: they tend to feel worse about losses than they feel good about gains. This is what academics call “loss aversion.” Understanding the wiring of humans when it comes to losses and bad outcomes is an important concept that should be discussed with clients. It needn’t be dwelt upon, but it should be discussed. It will help arrive at a portfolio that best suits people equipped with a more complete understanding of risk.

Now we repeat:

Risk is exposure to unexpected bad outcomes

As financial advisor practitioners, how do we deal with all this? For one, walking clients and prospective clients through this definition is a good starting place. Explaining to clients that risk is “Exposure to unexpected bad outcomes” and then walking through it with them is a good practice. Further, explaining that the risk models we use in our practices to manage risk are addressing these points is helpful. Finally, pointing out that unprecedentedly bad outcomes are possible is important. We don’t need to dwell on them, but we should acknowledge them. These unexpected bad outcomes are always a possibility lurking beneath the surface. Talking about them rather than ignoring them is an important discipline and it will result in better informed clients who are better equipped to deal with risk. The best relationships are those based on open, frank, and informative conversations. At WealthPlan Group, we approach risk with such frankness, and we welcome conversations about risk.

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The S&P 500 Index, or Standard & Poor’s 500 Index, is a market-capitalization-weighted index of 500 leading publicly traded companies in the U.S. The S&P 500 index is regarded as one of the best gauges of prominent American equities’ performance, and by extension, that of the stock market overall.

The NASDAQ Composite is a stock market index of the common stocks and similar securities listed on the NASDAQ stock market. The composition of the NASDAQ Composite is heavily weighted towards information technology companies.

The NASDAQ-100, whose components are a subset of the NASDAQ Composite’s, accounts for over 90% of the NASDAQ Composite’s movement, and there are many ETFs tracking its performance.

The Russell 2000 tracks the roughly 2000 securities that are US small cap companies. The Russell 2000 serves as an important benchmark when investors want to track their small cap performances versus other sized companies. The Russell 2000 tends to have a larger standard deviation in comparison to the S&P 500.

The Dow Jones Industrial Average, or simply the Dow, is a stock market index that indicates the value of 30 large, publicly owned companies based in the United States, and how they have traded in the stock market during various periods of time. These 30 companies are also included in the S&P 500 Index. The value of the Dow is not a weighted arithmetic mean and does not represent its component companies’ market capitalization, but rather the sum of the price of one share of stock for each component company. The sum is corrected by a factor which changes whenever one of the component stocks has a stock split or stock dividend, to generate a consistent value for the index.

The Bloomberg US Aggregate Bond Index (^BBUSATR) is used as a benchmark for investment grade bonds within the United States. This index is important as a benchmark for someone wanting to track their fixed income asset allocation.

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