Patternicity: Investor Behaviour and Pattern Seeking
December 10, 2015
Creative Commons photo courtesy of Lucapost
Patternicity: Investor Behaviour and Pattern Seeking
You have heard the stories. An image of the Virgin Mary can be seen on the side of a grain silo! Trenches have been spotted on Mars! Maybe you were told not to go near the old McLean house on the night of a full moon.
When seeing is believing
What is this all about and how can it possibly be relevant to investing? It turns out that humans are hard-wired to seek patterns. The above – visual images, beliefs and superstitions – are typical, and investors experience it all the time. Seeing patterns helps investors feel that they have created order out of chaos. Due to pattern seeking, investors tend to jump on top-down themes and trends. So, if oil inventories are growing and the oil price is down, now must be a good time to sell oil company stocks, right? These patterns can be insightful, but they can also lead investors astray. We humans seek and find patterns…frequently even when no pattern exists. We think we understand the cause and effect of many things, but in many cases we are wrong. This error – seeing a pattern where none in fact exists, or in other words, the tendency to see meaning in meaningless noise – has been dubbed patternicity.1
Grunt and go on
There is an explanation for all of this and it derives from evolution. Human beings are genetically wired to see patterns. Ancient man depended on pattern identification to sense danger, to eat and to procreate. If leaves rustled in the jungle, it could be a predator or it could be the wind. Better to identify it as a predator and evade it, not become tiger dinner!
Scientists know pattern recognition errors occur. People routinely make errors in cognition – sometimes identifying a pattern that is not there and sometimes not discerning a pattern that is there. If you have a vague recollection of any of your scientific or statistical endeavours from high school or university, you may recall the first as a Type I error (a false positive) and the second as a Type II error (a false negative). Thankfully, we humans are not totally useless – we also get lots of things right (correctly perceiving patterns that are there and not seeing ones that don’t exist). These four combinations of reality and perception are illustrated in the matrix below.
To minimize Type I and II errors, scientists ensure rigour in their experiments and subject their findings to the self-correcting process of peer review, independent verification and replication. Statisticians add probabilistic rigour by increasing the sample size to reduce these errors.
So far, so good. But how does this relate to evolution and the average Joe?
Let’s go back to the jungle. In our example with the tiger, not sensing the tiger is a Type II error… and very costly. Sensing a tiger when none was in fact there, a Type I error, was far less costly… the only consequence being that your ancient ancestor received a rush of adrenaline, grabbed a weapon, and lived to tell the tale. Surely, over eons, humans would have evolved through natural selection to reduce both types of errors? If so, why are false patterns as prevalent as they are?
So here is the thinking. We evolved by natural selection through a process known as association learning – developing and honing our brains’ “pattern-recognition engine” to connect the dots and identify the pattern. To aid in survival and reproduction, we learned instinctively to reduce Type II errors. Unfortunately, discerning non-obvious patterns is all about probabilities and conditional probabilities, and we humans are terrible at probabilities. So the evolutionary process that succeeded in reducing Type II errors came at the expense of making more Type I errors (sensing patterns that are actually not there). It makes sense that we learned to avoid the costly errors, but the less costly errors weren’t that important and didn’t get reduced by natural selection. In the exhibit below we illustrate how evolution adjusted the matrix.
To reduce the Type II errors, the evolution of our pattern-recognition engine has moved the entire horizontal dividing line upwards – fewer costly Type II errors, but more Type I errors. In 2008 a pair of researchers from Harvard and the University of Helsinki2 tested this theory using evolutionary modelling to demonstrate that when the cost of believing a false pattern is real is less than the cost of not believing a real pattern, natural selection will favour patternicity (an increase in Type I errors ). This is also evidenced in nature. As an example, predators only avoid nonpoisonous snakes that mimic a poisonous species in areas where the poisonous species is common.
So what’s an investor to do?
In short, people, investors included, naturally seek and find patterns, frequently when none exist. For investors, the probabilistic component in pattern identification is exacerbated by other, ahem… behavioural faults. Namely, investors are inherently driven by the dual emotions of fear and greed. Whenever one emotion predominates, an investor’s assessment of the probabilities, poor at best, is further skewed. Needless to say, this is problematic and leads to losses.
At Nexus, we think that there are ways to control these errors. We recognize that we don’t know everything. So we make more, reasoned investments, rather than fewer, bigger “bets”. We believe in a detailed, fundamental bottom-up investment process to pick each stock, rather than a potentially more blunt top-down approach. We endeavour to remain a skeptic and be on the look-out for new data that “changes the pattern”. A team investment decision can reduce emotion and errors relative to an individual decision. This takes more time. But in stock picking it’s better to be slightly late and right than early and wrong.
1.Much of this article is derived from Michael Shermer’s article “Patternicity: Finding Meaningful Patterns in Meaningless Noise”, published in Scientific American, December 2008.
2.Kevin Foster and Hanna Kokko, The Evolution of Superstitious and Superstition-like Behaviour, Proceedings of the Royal Society B, September, 2008.