SCOTT ADAMS, the creator of Dilbert, has this to say about making forecasts: “There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot cards, or crystal balls. Collectively, these methods are known as ‘nutty methods.’ Or you can put well-researched facts into sophisticated computer models, more commonly referred to as a complete waste of time.”
This is funny but, for the most part, I agree. It’s especially true in the world of investments. And yet, as you manage your financial life, some amount of forecasting is unavoidable. Anyone trying to build a retirement plan, for example, has to think about future market returns, interest rates, inflation and taxes. All of these factors—and others—will have an enormous impact on our financial future, so we need to make some estimates.
If forecasting is a necessary evil, it’s important to understand it—flaws and all. Daniel Kahneman, a founding father of behavioral finance, provides a useful framework. The first thing to understand about forecasts, he says, is that there are three factors that can cause them to go awry: incorrect or incomplete information, biases and noise.
1. Information. This one might seem self-explanatory. After all, the fundamental problem with any prediction is that it’s impossible to know what will happen in the future. That seems obvious. But for investors, it isn’t so simple. The reality is, there’s a lot of information that could help us make predictions. But sometimes that information is flawed, incomplete or irrelevant. As an example, you may recall the 1983 movie Trading Places.
In that film, a group of investors was betting on commodities—specifically, frozen orange juice. The prevailing wisdom was that a cold winter had hurt the orange harvest and would result in higher orange prices that year. But in the end, it turned out that the cold weather hadn’t had much of an impact. The harvest was fine and prices moved in the opposite direction from most investors’ expectations. In other words, investors were right about the weather but lacked information on the harvest itself. They only had one piece of the puzzle. To be sure, this is a fictional example, but this kind of thing happens all the time. As an investor, you need information that’s both reliable and complete.
We saw the same sort of effect in 2020. When the coronavirus shut down the economy, it was clear it would impact corporate earnings and thus stock prices. But no one knew precisely how things would turn out—which companies would be hurt, which would benefit and how long it would last. Again, we had a lot of information, but still there were a lot of holes.
2. Biases. When we talk about errors in investment forecasting, what we’re usually talking about are biases. When we lack information, that’s a problem that is largely out of our control. But biases are problems we cause ourselves. Biases refer to the way we use the information we have. Even in situations with perfect information, biases cause people to interpret that information differently or to cherry pick the information they wish to include.
We see investor biases around presidential elections, for example. Each candidate’s platform is usually pretty clear. Where investors differ, however, is in how they expect those policies to affect markets.
Kahneman’s book Thinking, Fast and Slow discusses biases—including investing biases—in detail. It’s a book I recommend.
3. Noise. In behavioral finance, biases get most of the attention. But Kahneman believes noise is an underrated contributor to investment forecasting. What is noise exactly? In short, it’s randomness in human thinking and behavior. Whereas biases have a logical basis—even if that basis is flawed—noise has no underlying logic at all. In Kahneman’s research, he’s found a surprising amount of noise in the world. Professionals as diverse as physicians, insurance adjusters and software developers all exhibit noise in their work.
What does it mean to exhibit noise? As an example, Kahneman cites pathologists making two assessments of the same biopsy. The correlation between two assessments by the same pathologist was, on average, just 60%. In other words, the same pathologist looking at the same data came to a different conclusion 40% of the time—for no clear reason.
Kahneman found the same thing across other professions and industries, including finance. “The problem,” Kahneman explains, “is that humans are unreliable decision makers; their judgments are strongly influenced by irrelevant factors, such as their current mood, the time since their last meal, and the weather.”
As an investor, how can you protect yourself—and your finances—from the landmines of bad information, bias and noise? Drawing on Kahneman’s work, as well as that of Philip Tetlock, co-author of Superforecasting, here are five recommendations:
Adam M. Grossman is the founder of Mayport, a fixed-fee wealth management firm. In his series of free e-books, he advocates an evidence-based approach to personal finance. Follow Adam on Twitter @AdamMGrossman and check out his earlier articles.