Imagine you had a jar of jelly beans and you asked a large group of people to write down their estimate for the quantity of beans in the jar. What you would find is that, upon taking the average of everyone’s guesses, you arrive at a remarkably accurate estimate. Often, the accuracy of the average is higher than any individual estimate. How is this so?
Very simply, for each person’s estimate, imagine you defined that person’s “forecast error” as the difference between their estimate and the true answer. What happens when multiple estimates are combined is that the average of each person’s forecast error approaches zero. That is, they roughly cancel out. Some people overestimate, some underestimate; and the average of all the answers tends to approach the true answer.
The one big caveat here is that each person’s estimate needs to be made independently of each other. It would not work so well if everyone had a discussion about what they all thought the right answer was ahead of time. In such a scenario, participants would anchor (or bias) each other. And in this case, the forecast errors may not cancel towards zero.
What does any of this have to do with investing at Montgomery Global? Well, you may be interested to learn that, as part of our Investment Process, we explicitly allocate two research analysts to each stock in our portfolio. Furthermore, we ensure that said two analysts work on each stock independently, as much as practically possible.
In this way, each investment thesis is, at a minimum, the product of two independent perspectives. And the reason why this is valuable is that it helps minimize the forecast error, or bias, that would otherwise stem from only one analyst looking at said stock. This is but one example of how our Investment Process is designed to maximise the probability of investment success.