Moving from uncertainty to risk helps investors make better predictions. Getting from uncertainty to risk is a job for probabilities.
That’s the case made in chapter five of Michael Mauboussin’s book, "More Than You Know: Finding Financial Wisdom in Unconventional Places.” It’s an important chapter because it ties together three elements that have a powerful impact on our investment success.
The pioneering economist Frank Knight made a distinction between risk and uncertainty:
- Risk refers to an unknown outcome, but we do know the underlying outcome’s distribution.
- Uncertainty also refers to an unknown outcome, but in this case, we do not know the underlying distribution.
Mauboussin added that the dictionary defines risk as “the possibility of suffering harm or loss,” while uncertainty is defined as “the condition of being uncertain,” where the uncertain is unknown or not established. Therefore:
- Risk always involves the potential for loss.
- Something that is uncertain may or may not include the possibility of a loss.
According to the author, all of this matters because:
“Why should investors care about the distinctions between risk and uncertainty? The main reason is that investing is fundamentally an exercise in probability. Every day, investors must translate investment opportunities into probabilities—indeed, this is an essential skill. So we need to think carefully about how we come up with probabilities for various situations and where the potential pitfalls lie.”
In his book, “Calculated Risks,” cognitive scientist Gerd Gigerenzer argued there were three ways to move from uncertainty to probability (risk); he listed them from the most abstract to the most concrete:
- “Degrees of belief”: These are subjective probabilities, and what Mauboussin called “the most liberal means to translate uncertainty into a probability.” For example, I may intuitively believe there’s a good chance a specific stock will go up in value when the market slumps again.
- “Propensities”: This is where the properties of an object reflect the probabilities. For example, if you roll a die (dice), there is a one in six probability you will get any one of the six numbers on it. Mauboussin noted this type of assessment does not always account for all potential factors.
- “Frequencies”: In this case, probabilities are based on extensive observations, in an appropriate reference class. An example of this occurs when value investors avoid stocks with sky-high price-earnings ratios; experience has taught them it’s hard to make money with “expensive” stocks. It also seems to me that technical investors are heavily involved in this form of translating uncertainties into probabilities; they have multiple patterns, such as “Golden Cross” and “Cup and Saucer,” which tell them when to enter a position and other patterns that tell them when to exit.
Mauboussin’s examples include, for degrees of belief, “Much of the ink spilled on market prognostications is based on degrees of belief, with the resulting probabilities heavily colored by recent experience.” He added that degrees of belief often come with a great deal of emotion.
From the propensity perspective, he offered Jeremy Siegel’s report that U.S. stocks have averaged annual returns of nearly 7% over the previous 200 years (from Siegel’s book, “Stocks for the Long Run”). While next year’s annual return may not be 7%, it’s likely to be that in the longer term.
Turning to the frequency perspective, Mauboussin noted that annual returns between 1926 and 2006 (an appropriate reference class) produced a distribution of returns with an arithmetic return of 12% and a standard deviation of 20.1%. With such information, it is relatively easy to estimate the probabilities of returns in the future.
According to the author, most of the academic finance community embraces the frequency approach, and thus most models used by academics presume that “price changes follow a normal distribution.” He added, “One example is the Black-Scholes options-pricing model, where one of the key inputs is volatility—or the standard deviation of future price changes.”
But when it comes to changes in stock prices, the normal distribution is not applicable. Technically, this is because the mean is higher and the tails are fatter than in a normal distribution. To illustrate the point, Mauboussin did research that found extreme-return days (both positive and negative) played a bigger part in determining the market’s total returns than would be expected against a normal distribution.
In the last section of the chapter, he pointed out that predictions can change future payoffs. If too many buyers begin pursuing a specific stock, then they will drive up its price and push down its potential returns. This reminds me of the quandary faced by big institutional funds that try to buy or sell individual stocks without moving the market too much.
That, in turn, takes Mauboussin back to expected value, which he called “a central concept in any probabilistic exercise”. Expected value, as you may recall, is the probabilities of various outcomes, multiplied by the payoff of each of the outcomes.
Concluding the chapter, he wrote:
“Peter Bernstein once said, 'The fundamental law of investing is the uncertainty of the future.' As investors, our challenge is to translate those uncertainties into probabilities and payoffs in the search for attractive securities. An ability to classify probability statements can be very useful in this endeavor.”
Conclusion
Uncertainty is the unknown without probabilities. Risk is the unknown with probabilities. If we can turn uncertainty into risk and risk into probabilities, then we can make predictions with more confidence.
The academic work of Gigerenzer articulated three avenues by which uncertainty might be converted to risk and probabilities: degrees of belief, propensity and frequency. Propensity is more concrete (and actionable) than degrees of belief, and frequency is more concrete than both of the others.
Mauboussin also noted that while the market may have a normal distribution, individual stocks do not, and there are implications, including the substantial role that extreme-days play in setting averages.
Read more here:
- More Than You Know: What About Circumstances?
- More Than You Know: How Often You're Right Isn't All That Matters
- More Than You Know: Should Investing Be a Profession or a Business?
 Not a Premium Member of GuruFocus? Sign up for a free 7-day trial here.
