Representativeness Bias: Easy Classifications


Representativeness bias is when you classify new info based on classifications that have worked in your past experiences. We derive meaning from our past experiences and lean on them to make life easier in the future. Representativeness bias is a nice shortcut that lets us put people, experiences, and investments into easy buckets based on key attributes. It’s especially tricky when you label these “buckets” with terms that have complex meaning attached to them like “value” or “growth”.

In investing, representativeness can manifest in two ways.

First, when you’ve been right about a trend for a long time, and the trend starts to transition, it can be hard to change your view when the symbol represents that trend. For example, $TSLA is a growth stock and has been a growth stock for some time. If there was ever a security that “represented” growth it would be $TSLA. When $TSLA transitions to a more stable company it will be difficult for the shareholder base (growth investors) to come to terms with that fact. To them, $TSLA has represented growth for so long than when the P/E starts to compress they’re likely to say to themselves, “things will pick up soon, $TSLA is a growth stock, after all”. There might even be a loud vocal minority that feel earnings growth can continue forever. Listening to these people would ignore the fact that most trend eventually revert to the mean (base rate neglect).

Second, when searching out investments, it can be easy to use a familiar classification like “growth stock” to describe a company without doing any real work to determine if it is a growing company. Let’s say you discover a stock with a sky high P/E ratio and P/E ratio is something you used in the past to classify growth stocks. Boom, XYZ is a growth stock! It’s easier to say that XYZ is a growth stock because it has some characteristics consistent with your beliefs about growth stocks than it is to research all the attributes that make us growth stocks.

Representativeness bias is the most straightforward example where mental shortcuts can get you into trouble if you use them to make investing decisions.

Actionable steps: Investing is about balancing unlimited amount of information with the actual information you need make good decisions. We are big fans of the keep it simple approach, but representativeness bias proves that keeping it too simple and ignoring the base rate or sample size (or both) is a recipe for disaster. Do the work required to come to a reasonable conclusion. Try to think in probabilities rather than simple classification buckets. If you insist on taking a big mental shortcuts, then one really easy thing you can do discount all your probability calculations by the factor of how lazy you are! Even when you do diligent work you can still be wrong. Probabilities are also useful because anything less than 100% leaves the door slightly ajar and creates less mental friction if the market proves you wrong and you need to take corrective steps.

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