Yesterday I began a series of ruminations on risk by examining risk in the sense of probability. In that sense, risk is neither good nor bad inherently. It is simply risky. We can dial it up or down (to an extent) in order to try to increase our chances of achieving certain goals. In this sense, increasing risk merely means maximizing variance of outcome. For most of us, however, increasing risk means increasing danger. For investors, it necessarily relates to losing money.
As I have noted before, England sent regular bombing raids into Germany during World War II. Obviously, many of those planes never returned and those that did were often riddled with damage from German anti-aircraft guns and fighters. Wanting to improve survivability, the English Air Ministry examined the locations of the bullet holes on the returned aircraft and proposed that reinforcement be added to those areas that showed the most damage. The mathematician Abraham Wald, however, suggested otherwise (see his research here or read about it here).
Wald’s unique insight was that the holes from flak and bullets on the bombers that returned represented the areas where planes were able to absorb damage and survive. Since the data showed that there were similar areas on each returning B-29 showing no damage from enemy fire, Wald concluded that those areas (around the main cockpit and the fuel tanks) were the real weak spots and that they must be reinforced. This creative and obvious (but only in hindsight!) way of looking at the presented problem provided a simple but profound solution.
Investing is a loser’s game much of the time (as I have also noted before) – with outcomes dominated by luck rather than skill and high transaction costs. If we avoid mistakes we will generally win. By examining risk more closely, we’ll have a better chance of doing precisely that. Since risk defies simple definition, today I am going to try to describe some of its elements, allowing us to approach it from different angles and perspectives, with the hope of ascertaining a more complete understanding.
- Volatility. In quantitative finance, standard deviation is applied to the annual rate of return of an investment to measure the investment’s volatility, which is used as a stand-in for risk. Standard deviation is also known as historical volatility and is often erroneously used as a gauge for the amount of expected volatility. As if. The equations are elegant though.
- The likelihood of Permanent Loss of Capital. For most investors, risk is about losing money. For a value investor particularly, this is the key to understanding risk. Ultimately, financial risk is about what stands in the way of achieving your financial goals and losing money is a leading indicator of failure.
- Risk Appetite. Risk appetite is about the pursuit of risk (in the probabilistic sense I discussed yesterday). If I am at or near retirement and have saved what I need for it, my risk appetite should be small. If I am 22 and likely have a long investment life ahead of me, it should be far larger. Every investor should regularly and routinely ask what success will look like and then go about figuring out how best to get there. That process will necessarily include careful analysis of risk appetite (or need). It begins with the neglected art/science of estimating expected returns for prospective portfolios.
- Risk Tolerance. This concept relates to the degree of uncertainty that an investor can handle with respect to a negative change in the value of his or her portfolio. Sadly, our risk tolerance seems to be highest when things are going best and lowest when things are at their worst (see #10 below). I don’t need liquidity (until I do). The concept of risk capacity is largely a joining of appetite and tolerance. It is a function of capability (how much – as objectively as possible – you can carry) and maturity (your ability to cope with risk). This maturity relates to emotions (will you panic and sell at the wrong time?) but also to control (how do you deal with uncertain outcomes?).
- Tail Risk. Broadly, tail risk is merely the risk (or probability) of rare events. Technically, tail risk is the risk of an asset or portfolio of assets moving more than 3 standard deviations from its current price. More particularly, most investors are only interested in downside tail risk. Upside tail risk is merely proof of genius (and Wall Street has made many huge careers for people who have made one great call in a row).
- “Black Swan” Risk. A “black swan” is Nassim Taleb’s apt metaphor describing an event that is a surprise (to the observer), has a major impact (positive or negative), and after-the-fact is often inappropriately rationalized with the benefit of hindsight as likely, obvious and/or inevitable (the “narrative fallacy). It can be distinguished from tail risk — a more inclusive category — in that it is largely unpredictable. The metaphor refers to the problem of induction; a million observations of white swans cannot prove that all swans are white while a single observation of a black swan disproves the notion.
- Risk Assessment. Strictly speaking, risk assessment is the determination of the quantitative and/or qualitative values of risk related to a concrete situation and a recognized threat. Quantitative risk assessment requires calculations of two components of risk: the magnitude of the potential loss and the probability that the loss will occur. More generally, it’s the process of figuring out what can go wrong and why.
- Risk Management. It is the identification, assessment, and prioritization of risks followed by the creation and implementation of plans to deal with them. Peter Bernstein’s brilliant Against the Gods (1996) is the history of our ability to control (or at least manage and mitigate) risks. Significantly, the financial history of the 21st C. largely demonstrates (in excruciating detail) that and how we have overstated our advances in this area.
- Risk Budget. Investors try to create the most desirable risk/return combinations they can to achieve their goals. Doing so obviously means taking on risk in order to obtain expected return. The ideal is an optimal set of investments for maximizing expected return for a given level of overall portfolio risk. This “given level of overall portfolio risk” provides the risk budget, and the goal is to allocate this budget across investments in an optimal manner. Once a risk budget is in place, the portfolio components can be monitored to assure that risk positions do not diverge from those stated in the risk budget by more than pre-specified amounts. As with all investing, it’s much easier accomplished in theory than in practice.
- Behavioral Risk. For the vast majority of us, this is the most pervasive risk of all. As Shakespeare wrote (in Julius Caesar, I, ii, 140-141), “[t]he fault…is not in our stars, but in ourselves….” Football season is starting up. Watch pundits generally expect last year’s successful teams to do well (recency bias) even though roughly half of each year’s NFL play-off teams turn over each year. Watch fans expect their teams to do well (optimism bias). Watch all of us who are fans pretend that the physical risks of the game aren’t horrific and impossibly dangerous (confirmation bias). These risks are largely why, as Tadas Viskanta so elegantly puts it, investing is hard.
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Mentioned in #4, liquidity risk or the ability to ‘redeem’ at a fair value (regardless of performance or the investor’s situation) has no standard unit of measure. When was the last time man was unable to measure? Also what is 10 years of illiquidity worth? PRIME + 6%?
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