Why we draw comparisons to reference points we know
How much does a cow weigh on average? How tall is St. Stephen's Cathedral in Vienna? How big is Alaska? Before you start googling, make an educated guess. How did you go about it? Presumably by drawing comparisons to reference points you know: the weight of your dog, the height of your house, the size of Austria. All of these reference points are anchors.
Anchors help us make estimates. In everyday life, we use anchors whenever we need to estimate something. Things start to get problematic, though, if we attach too much significance to them. The human brain lets itself be influenced all too easily by anchors, as was demonstrated by psychologists Daniel Kahneman and Amos Tversky in a 1974 experiment. They asked their subjects to spin a wheel of fortune, setting an arbitrary anchor between 0 and 100. Next, they made them estimate the percentage of UN members that were African nations, asking them first whether they believed it was higher or lower than the number they had spun and then to indicate what they thought was the actual percentage. What Kahneman and Tversky found was that subjects who had spun higher numbers gave higher estimates than those who had spun lower numbers. In other words, our estimates are biased towards the underlying anchor, even if that anchor is an arbitrary and completely inappropriate one.
People also make strategic use of this phenomenon known as anchor effect: When it comes to price negotiations, for instance, the seller initially asks for an amount higher than the actual value of the product, and the buyer offers less than he or she is willing to pay for it, with either side trying to influence the other's estimate regarding the real value of the product. And in the context of estimating how much a property or a car is actually worth, the list price often serves as an anchor.
When forecasting the earnings of a company, financial analysts like to use the average earnings per share in an industry as an anchor. However, this anchor reflects merely the average of the industry as a whole. The company in question may fare significantly better or worse. As a result of this approach, successful companies tend to be underestimated and less successful ones overestimated. A better strategy is to adjust the anchor based on factors specific to the company in question!
Strategic asset allocation, too, is problematic if it is excessively influenced by an arbitrary anchor, such as the maxim that one should spread one's investments equally across all asset classes (e.g. shares, bonds, money-market products, properties and raw materials). Using such an anchor as a starting point makes good sense, but what one needs to do afterwards is incrementally adapt the target asset allocation to new pieces of information that one adds to the picture. For instance by asking friends for tips, by factoring in one's age and life expectancy when it comes to weighting the asset classes and by taking into account one's risk attitude, the expected returns and potential risks of one's investments and their correlations.
After having set an initial anchor, ask yourself: Is there any reason to assume that using this anchor will cause me to misestimate things? Consider the opposite of what you think is true. Gather additional information. Give the following questions some thought: What other anchors could I use? What anchors would my friends, colleagues or people with a different risk attitude and/or background set? Trust in the wisdom of the crowd. Set multiple anchors and try to improve your estimate based on all of these anchors. And always keep the following in mind: Anchors are a sine qua non when it comes to estimating something, but it is important that you set them realistically and adjust your estimates incrementally based on additional information.
This article was published in the Austrian business magazine GEWINN. Read the original article here (in German).