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IMAGINE YOU’RE TRYING to guess the winner of a basketball or ice hockey game. Which of these methods do you think would work best?
In a classic study, researchers Paul Slovic and Bernard Corrigan attempted to answer this question. Instead of basketball or ice hockey, they looked at horse racing, but the results are equally applicable.
In their study, Slovic and Corrigan asked expert handicappers to make predictions using varying amounts of data about the horses in a race. Some of the handicappers received just five statistics on each horse, while others received 10, 20 or 40. The study produced two interesting findings.
First, as handicappers received more data, they became more confident. Those who received 40 statistics about a horse were far more confident in their predictions than those who received 20. The latter, in turn, were more confident than those who received 10, and so forth.
Second, despite increasing confidence, more information didn’t necessarily lead to better results. While those with the least information ended up making the worst predictions, there appeared to be a point of diminishing returns as each handicapper received more information. In fact, beyond a certain point, more data actually resulted in less predictive accuracy.
Taken together, these findings provide an important lesson for individual investors: When it comes to making predictions, you definitely want to gather some information, but not too much. No question, you can’t simply flip a coin. At the same time, it’s counter-productive to dig too deeply into the data. An educated guess, it turns out, has the best chance of being right.
I mention this because it’s so counter-intuitive. Especially when it comes to financial decisions, everyone wants to feel that they’ve “done their homework” and conducted a thorough analysis. But as the authors of this study explain, the problem with having too much data is that we end up drowning in it. We overthink things, we become distracted by outlier cases, we generally miss the forest for the trees—and yet we don’t even realize it. We think we’re becoming more expert when, in fact, we’re just getting ourselves tangled up.
I want to draw an important distinction. The findings in the horse racing study apply only to questions that involve predictions. Examples include:
In all of these cases, because they require some element of prediction, I believe the horse racing study applies. This is when you want to make an educated guess. That educated guess may involve saying, “I just don’t know,” at which point you should focus on other issues, such as the risk reduction benefits of adding foreign stocks to a U.S. portfolio or whether you have the financial wherewithal to handle nursing home costs.
But there are lots of other cases that do not involve any element of prediction. In those cases, you definitely do not want to guess. Life insurance is a good example: Yes, your needs might evolve over time, as the children leave home and as your nest egg grows. But to calculate how much insurance you should carry today, you don’t need to make any predictions about how long you’ll live. To answer a question like that, don’t guess. Instead, just sharpen your pencil.
Adam M. Grossman’s previous articles include B Is for Bias, Humble Arithmetic and Repeat for Emphasis. Adam is the founder of Mayport Wealth Management, a fixed-fee financial planning firm in Boston. He’s an advocate of evidence-based investing and is on a mission to lower the cost of investment advice for consumers. Follow Adam on Twitter @AdamMGrossman.
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I love the nerd component you bring to these posts Adam. The Slovic and Corrigan study nicely highlights the mess we’re in, then again what a bore life would be without the other side of the human dichotomy. Still, good data is a far cry from useful information and the wisdom required to apply it correctly.