The Beauty of Simplicity by SG Value Investor, ValueEdge
I would first like to share an exercept from Paul Watzlawick’s book: How Real is Real?
In one experiment, two subjects, A and B, are seated facing a projection screen. There is a partition between them so that they cannot see each other, and they are requested not to communicate. They are then shown medical slides of healthy and sick cells and told that they must learn to recognize which is which by trial and error. In front of each of them are two buttons marked “Healthy” and “Sick,” respectively, and two signal lights marked “Right” and “Wrong.” Every time a slide is projected they have to press one of the buttons, whereupon one of the two signal lights flashes on.
“A” gets true feedback; that is, the lights tell him whether his guess was indeed right or wrong. His situation is one of simple discrimination, and in the course of the experiment, most “A” subjects learn to distinguish healthy from sick cells with a fair degree of correctness (i.e., about 80 percent of the time).
“B’s” situation is different. His feedback is based not on his own guesses, but on A’s. Therefore it does not matter what he decides about a particular slide; he is told “right” if “A” guessed right, “wrong” if “A” guessed wrong. B does not know this; he has been led to believe there is an order, that he has to discover this order, and that he can do so by making guesses and finding out if he is right or wrong.
In other words, there is no way in which he can discover that the answers he gets are noncontingent — that is, have NOTHING to do with his questions — and that therefore he is not learning anything about his guesses. So he is searching for an ORDER where there is none that HE could discover.
“A”‘s explanations are simple and concrete; “B”‘s are of necessity subtle and complex — after all, he had to form his hypothesis on the basis of very tenuous and contradictory hunches.
The amazing thing is that “A” does not simply shrug off “B’s” explanations as unnecessarily complicated or even absurd, but is impressed by their sophisticated “brilliance.” “A” tends to feel inferior and vulnerable because of the pedestrian simplicity of his assumption, and the more complicated “B’s” “delusions”, the more likely they are to convince “A”.
The moral of the story is essentially we should just prefer simpler models instead of those complex ones (e.g. DCF). Don’t get me wrong, I am not saying that complex models are inaccurate; I have worked on some personally. However, the thing with these complex models are the amount of assumptions made. As long as one goes wrong, the entire model would just fall apart. Furthermore, the thing with DCF would be that it is used alongside other models such as Comparable Transactions and Comparable Companies to derive a narrower range of values the company should be trading at. Never should a DCF be used as a standalone.
Furthermore, it has been proven that with complex models containing more information, the only thing that it increases is confidence but not accuracy of decisions. Researchers Tsai, Klayman and Hastie conducted such experiments to measure how with the acquisition of additional information affects both the accuracy of our decisions, and our confidence about the accuracy of those decisions. The end conclusion was that the amount of available information affects our confidence more than it does our accuracy. Essentially, more information simply leads to more overconfidence. Once again, I am not against getting additional information, just that it has to be relevant information. What do I mean by relevant information? Acquiring information regarding company’s economic moat or direction management plans on taking the company, those are relevant information. Irrelevant information would be such as asking hypothetical questions which would lead to a lengthy discussion with no conclusive ending.
One great example of a hypothetical question I read was regarding POSCO (ADR) (NYSE:PKX). An individual asked if steel would still be relevant in the next few years given how automobiles and aircrafts are switching to aluminium as it is much lighter and fuel-efficient. However, such discussions have been ongoing since 20 years ago. As interesting as such a discussion may be, no one can really tell if automobiles and aircrafts would switch completely to aluminium. Additionally, for a world that no longer has a demand for steel is slightly far-fetched though not impossible in theory.
To sum it up, as much as complicated models may make one’s investment look more impressive and solid, the key word here would be look. It has been shown that such models adds no utility to one’s accuracy of decisions and only leads to an increase in overconfidence. That said, I understand that following a simple strategy, focusing on important information and asking relevant questions are all easier said than done. Even I myself am only coming to realise this fact after having invested for approximately 4 years.
Disclaimer: The authors have no vested interest in POSCO (ADR) (NYSE:PKX)