Back in my graduate school days, one of my physics teachers had the habit of impishly giving his students a little too much information for their own good on tests.
The set up was simple. He asked a classic mechanics question of the sort that gives many students the heebie-jeebies. It involved two trains hurtling toward each other at different speeds with the goal being to figure out when, and where, the collision would occur.
Everything needed for the calculation was provided. But a superfluous data point was also included. It wasn’t a speed, a distance, or a time. No, they were given the price of hamburger to chew on.
Alas, all too many students tried to mix the price of beef into their calculations and thereby produced some spectacularly incorrect answers.
One might charitably chalk up the error to a lifetime of taking tests that provided only the data needed to answer each question. The idea of leaving a crumb of information out of a calculation was alien to some students.
Outside the classroom we often encounter a surplus of information rather than a deficit.
Investors routinely face information overload. It’s easy to find hundreds, or even thousands, of data points on stocks with just a few clicks of the mouse. Only a small amount of which is actually useful.
Money manager James Montier looked at whether adding more data helped, or hurt, decision making in his book Behavioural Investing. It’s a hefty tome that I recommend to advanced students of the market who want to give their minds, and arms, a workout.
In it he describes a 1994 study by Fred D. Davis, et al. called Harmful Effects of Seemingly Helpful Information on Forecasts of Stock Earnings.
The work focused on the behaviour of MBA students taking a class on advanced financial statement analysis. They were asked to forecast quarterly earnings for various firms and determine how confident they were in each forecast.
Unbeknownst to them, they actually evaluated each firm three times using different data sets. A basic data set included past quarterly earnings, sales, and stock prices. The second data set added in some redundant and superfluous information. The third provided the basic data plus additional information on each company that was deemed to be useful.
Alas, providing more data proved to be a bust. The students’ forecasts were most accurate when they used the basic data. Accuracy decreased when they used either the second or third data sets.
Even worse, as they got more data the students’ confidence in their forecasts increased while their forecasts became less accurate.
It’s a nasty one-two combination and a problem that likely afflicts stock analysts, and individual investors, more generally.
It’s probably impossible to avoid the issue entirely. But keeping an investment journal, where the reasons behind each stock purchase and sale are recorded, might help. A journal will allow you to grade your efforts more accurately over time, which is important because the market conducts a doozy of an exam.
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