Signal to Noise Ratios
Noise is toxic. And tests have shown that our ideas are sticky. Once we come up with a hypothesis, right or wrong, we are slow to change our minds. If we base our early decision on some noise, and not valid information, that can lead to an early error in our judgment. Once we get moving down the wrong track, there are substantial costs to reverse since we are predisposed to not admit our errors and we will hang on to our poor decisions. Such belief perseverance is human nature – we don’t like to admit mistakes.
The adverse effect of a low signal to noise ratio on the value of information is well documented. Since we are presented with news on a continuous basis, we sometimes will place too much emphasis on the noise, mistaking it for valuable information. To control this, you can read a news summary once a week instead of more often, which leads to a higher degree of noise filtration, and thus higher quality signal content. Tweeting, sound bite journalism and other modern social networking phenomena exhibit high degrees of noise content. The scary thing is that tests have proven that we are predisposed towards a higher degree of confidence if we are receiving more input, regardless of its informational quality. This hinges on our societies recent adoption of a materialistic “more is better” approach in all matters – including information.
With so much information in our everyday lives or within our investment research activities, we should take a leaf from the software designer’s book. For information processing by computers, programmers create complex filters to discover the signal within all the noise. Once the signal is laid bare, events are more easily found using pattern recognition systems and delivered via event handlers.
Black Swans
A Black Swan is a statistical outlier that has extreme consequences. Although not anticipated, much study after the fact leads us to postulate explanations for the outlier– attaching some predictability to it that would have been considered inconceivable in advance. The financial market crash in fall 2008 is considered a classic example of a Black Swan. Few saw it coming, but now we often see “experts” exhibiting severe hindsight – or “I knew that would happen” – bias.
One sure way to avoid information overload is to ignore all the noise and only listen to the signal. So how do you hope to pick out the signal, ignoring the noise? Perhaps you should rely on an expert, maybe even pay them a sizable commission for their expert advice. Before the internet era, we didn’t really have an information overload problem.
The experts had all the info – and didn’t share it with the masses. The problem with relying on expert(s) is that they have proven to be as just as susceptible to Black Swans as the rest of us.