One of the best ways that I have found to get acquainted with the strategies of some of the top hedge fund managers and traders in the world is to read the classic Market Wizards series of books. Author and trader Jack Schwager has interviewed some of the best traders and investors in the word, and written up summaries in three or four different books. They are absolute classics in the field, and every time I read an interview, I get something new out of it.
One of the most persistent themes in the books is the singular obsession with risk that all traders who are able to survive in the long-term seem to have. The top traders seem to focus foremost on how much money they could lose in any investment, and only then start to think about what their likely returns could be.
It is unfortunate then, that in many investing articles and books, risk so frequently takes a backseat to returns. In fact, risk itself is a highly rich topic that is worthy of much study. While its intuitive definition is, well, intuitive to most ("how much money could I lose by buying this"?) it turns out that attempts to make the concept a bit more concrete and measurable can get pretty complicated pretty fast.
In this tutorial, we will take a unique view of risk, starting with why it is so important to be able to measure it.
Why try to measure risk?
If you are just getting started learning about investing, you might wonder why it is worth going to so much trouble to measure a concept that seems intuitively obvious. The answer is that when we start building real-money portfolios by combining all different kinds of investments, we can pretty quickly lose all intuition as to what the overall level of risk we are taking is. So it really helps to have some logical or mathematical principles to fall back on.
Answering the kind of real life questions that come up annoyingly often in real-life portfolio management can also require a rigorous system to measure risk. Questions like "We have a combined household income of $130,000, a six year old, and a four year old and would like to retire in 30 years. What is the appropriate mix of investments for us to hold right now?" Going on vague feelings here is not enough: we need a systematic way to assess the chances that you will meet your retirement goals under various different scenarios, and what will happen if you do not.
There are several different approaches to getting there.
Approaches to measuring risk
When economists and finance professors were busy constructing their grand theories of the financial markets in the 20th century, they defined risk mathematically as something called the "volatility" of returns. Simply put, volatility is a measure of the degree to which investment returns "swing around" their long-term average. This is a pretty intuitive once you see an example: an investment returning 5% one year, 30% the next, and then losing 20% the year after that would be pretty volatile. One returning 3% one year 4% the next and 2% the year after that is not very volatile. While the two investments actually get to the same place -- their three year returns are the same -- the second gets their in a much smoother ride. We will go over the concept of risk as volatility in the second article in this tutorial.
Volatility can be an important measure for both psychological reasons and real-life reasons, since volatile investments can "keep you up at night" and can also be more likely to lose money in the short and intermediate-terms. But volatility is not the whole picture in risk management. What most people really tend to worry about when they are going to sleep at night is the chance of sudden and dramatic events - like a tornado or earthquake. How investments will perform during the financial equivalent of a natural disaster is not always well predicted by their volatility during more "normal" conditions. These kinds of events have been referred to as "black swans" and the next article in our series on "Black Swans and Investment Risks" analyzes them more.
Approaches to Managing Risk
With our two concepts of risk now defined (volatility, and "the black swan"), we next jump into looking at some techniques to manage risk. The first of the risk management articles looks at how to manage volatility risks. Since volatility is something that can be measured, this should be easy to do in theory. In practice however, the difficulty is that the volatility of different investments tends not to be constant, rather it changes a lot based on the market environment. In some markets, for instance, holding a portfolio of 60% stocks and 40% bonds can feel like a tranquil day on the lake; in other market environments, it can feel like taking a canoe out on the ocean in the midst of a storm. Taking a more active approach to volatility management can reduce the chance of getting an "unwelcome surprise" at a time when you really do not want it.
Managing black swan tail risks can be a bit more difficult of an endeavor, since by definition these are almost impossible to measure and may be impossible to eliminate completely. However there are a couple techniques that may be promising in mitigating them, which we feature in the next article on managing black swan risks. One technique is to diversify your portfolio even beyond what academic theories like Modern Portfolio Theory - which exclusively on past data taken under "normal" circumstances - might suggest. We call this "intelligent diversification." The other is to use something called "tactical asset allocation" to systematically reduce exposure to falling markets, under the theory that "black swan events" do not get priced in immediately, but usually occur over a "bear market" that may take months or even years to run its course.
Using Monte Carlo Simulations to Make Risk More Real
The problem with the methods discussed thus far is that they all look foremost at the chance of losing money today, or in the next few years, and rate the risk relative to the amount of money that you have invested right now. But if you are saving for retirement over many years, the risk that you should actually be most concerned about is that many years in the future when you need access to your life savings, they won't be there, or they won't be large enough to cover all of your spending needs. This is not an absolute risk, but a risk of not meeting your goal.
Monte Carlo analysis is a purely statistical method that can shed some light on this kind of risk. It works by running thousands of different computer simulations on various different "paths" of what investment returns might look in the future, and then looking at how you fared in each of them.
While this kind of analysis has many shortcomings, it can be very useful if it is evaluated in the right context and taken with some grain of salt. The final article overviews Monte Carlo Analysis, its shortcomings and uses.
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