Imagine coming home from work to find you are worth 22.6% less than you were when you left that morning. That is what happened to stock market investors on October 19, 1987, a date which shall forever after be known as "Black Monday."
According to risk models, the stock market crash that occurred was something like an 18 standard deviation event. The probability of an 18 standard deviation event happening on any given day has 73 zeros in front of it. In other words, an event like black Monday should statistically not happen once over the course of the entire universe.
Obviously, there is something wrong with the way the we measured that risk. And what is wrong is actually very common-sensical: sometimes models break down, and sometimes we cannot predict what is going to happen in the future because the future might be totally outside the realm of past experience.
Two kinds of risks
In a world of "black mondays", an effective risk management strategy should have two aims:
- Take on a level of "volatility risk" that is appropriate for your individual portfolio goals and risk tolerance, keeping in mind that risk and return go hand in hand.
- Do everything possible to reduce the odds of an "extreme negative event" impacting your portfolio, as this can take years to recover from.
Dividing a discussion of risk management into these two pieces is useful and insightful, because in some ways they represent two very different kinds of risks.
The first kind ("volatility risk") is the more normal everyday kind of risk that occurs when everything is humming along smoothly. We are familiar with the way that stocks and bonds go up and down unpredictably on a day-to-day basis, and we can model this behavior fairly well. In the now-famous language of Donald Rumsfeld, these are the "known unknowns."
The second kind of risk are the things that we cannot measure or anticipate because they do not happen on a daily or even monthly basis, or because they are totally unanticipated events. These are what former Defense Secretary Donald Rumsfeld would call "unknown unknowns" and what author Nassim Taleb would call "black swans." We will just call them tail events.
Managing Tail Risks
While managing the volatility of your portfolio is important, the reality is that is probably tail risks that are more likely to really "keep you up at night." Many volatility management techniques can miss these altogether. A good analogy is that volatility risk can be a slow cancer, but tail risks can be a steamroller.
While it is impossible to eliminate tail risks altogether, there are techniques that you may be able to use to ensure that you will live through them a little easier. Two approaches show promise - tactical asset allocation, and extreme diversification.
Tactical Asset Allocation
The wealth management establishment mostly practices what is called "strategic asset allocation", also known as "buy and hold" or "modern portfolio theory." This is a fancy and scientific-sounding way of saying that most advisors keep their clients invested in the same combination of assets pretty much all the time, only "acting" to rebalance back to target from time to time. While this strategy will assure you of a market-level of return, minus the sometimes substantial fees that you might pay to your advisor, it does little to protect you if and when things suddenly go off a cliff, like a 2008-style crash.
An alternative to strategic asset allocation is tactical asset allocation, or TAA. A TAA strategy shifts your asset allocation based on the market environment. For instance, you may be able to reduce the chance of a "tail-risk" event by systematically selling out of falling markets according to a pre-set quantitative formula (done systematically so you can be sure emotions do not enter the picture). After the model flashes an "all-clear", you can start start adding to positions again. While by necessity this kind of strategy will miss out on the first part of a market "rebound" since it purposefully avoids trying to "pick the bottom" of the market, it also all but assures you of missing the worst of a chronic bear market like 2008.
Just about everyone knows "diversification is good." It has practically become Wall Street's mantra by this point. But what most wealth managers mean by diversification is actually something called "mean-variance optimization." Simply put, this is a way to plug historical returns and volatilities into a computer and figure out the portfolio that offers the highest return for a given level of volatility. Mean variance optimization can ensure the predicted volatility of your portfolio matches a desired level.
A different way to view diversification is to construct a portfolio that will perform equally well under various different economic scenarios and under various different "tail events." So, for instance, if gold will perform well if the monetary system shuts down and bonds will perform well in an environment of deflation (deflation is when the price of goods decrease over time), we can combine gold and bonds to at least partly insulate our portfolio from two types of tail risks. We might choose to include these assets even if they appear to "hurt" our performance from a mean-variance optimization point of view, because we know that mean-variance optimization has its limits.
Practically, using diversification to mitigate tail risks instead of just volatility risk might lead you to include a greater number of asset classes in your portfolio than most of the cookie-cutter financial superstores would recommend. For instance, gold, commodities, real estate, and inflation-protected bonds are all the kinds of assets that are often left out of portfolios that are manufactured by the equity-pushing establishment. Yet in the context of mitigating tail risks, each of these may add considerable value by performing well in environments when stocks and bonds do not.
If you had used tactical asset allocation and intelligent diversification in 1987, you would not have missed "Black Monday" altogether, but you could have insulated yourself from the worst of the damage.
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