Charlie Munger, Vice Chairman of Berkshire Hathway and long-term investment partner of Warren Buffett, refers to these emotions, multiple biases and tendencies acting at the same time in the same direction guiding us towards an irrational action as the "Lollapalooza effect." In his book, Poor Charlie's Almanack, Munger talks about a number of cognitive biases that we should be aware of in order to make better investment and business decisions.
Many of us may have already witnessed these irrational tendencies in our own investments.
How many times are we so sure about a stock that we continue to hold it even though there may be ample signs that the sentiment and story may have changed?
We convince ourselves to hold on, even though an objective analysis would determine otherwise.
How many times we need to walk away, but stay-on and make just one more trade because we feel very confident that this one will make up for all the rest that have been going wrong?
How many times we have a gut-feel that this is an absolute bottom in the stock and we can’t go wrong buying it here or averaging-down?
Most of the time, the “gut-feel” investing decision eventually leaves one poorer and feeling exhausted, disappointed, stressed, and frustrated.
I know these feelings, for I too have experienced them.
Computer algorithms simply don't feel that way.
A legitimate question is why do experts perform poorly even when they’re given the model’s output before making their decisions?
Once again it’s our propensity to overestimate our abilities, and the feeling that we possess special insight that can allow us to enhance the model’s decision. So the experts when given the same model's output end up modifying it, and making the results poorer. Something as mundane as a rough commute or a missed train can affect the way we perceive things. Furthermore, the decision-making can be inconsistent from person-to-person, even when they look at the same data. All these factors significantly diminish our ability to bring the perceived “enhanced” performance compared to that of a system's emotionless and model-driven decision making.