Beware the Noise

Behavioral economics has taken on greater importance by investors in recent years but has been an edge exploited by systematic investors for decades. The founders of behavioral economic theory, Daniel Kahneman and Amos Tversky, identified biases we all possess as part of the human condition. We are often asked for examples of such biases.

One example particularly pertinent to investing is that the pleasure associated with a profit is much less relative to the pain associated with a loss. As such, investors tend to realize small gains quickly while delaying locking in losses. Delayed losses often turn into larger losses or lost opportunity costs. While these behaviors feel good from a psychological perspective (lots of small gains and holding onto hope that all current losses eventually come back to break even), they assure mediocre returns in the long run. After all, investment returns are asymmetrical, a 20% loss requires a 25% gain to get back to par. Taking losses quickly and being patient with gains puts the power of compounding in your favor but is much more difficult to implement psychologically as taking consistent and regular losses can be damaging to the ego. I’ve certainly never heard anyone discuss their recent losses at a cocktail party.

Daniel Kahneman has co-authored a new book “Noise: A Flaw in Human Judgement,“ where he turns his focus from biases to noise.

Whereas bias is the average error related to a correct answer, noise is the variability of the error. Kahneman constructed a consulting exercise for an insurance company to help determine accurate premiums for its policies. They created policy cases and submitted them to underwriters for pricing. They expected 10% variability in pricing or noise, and instead they experienced 55% variability. Overpriced premiums cause the company to lose business while underpriced policies cause insurance companies to lose money. As such, the insurance company was losing hundreds of millions of dollars a year due to variability or noise. Similar to the biases previously identified, Kahneman outlines three types of noise: level, pattern and occasion. Pattern noise, one he considers likely the most important, relates to the complex way in how people view the world.

Many investment managers make discretionary decisions influenced by their biases subject to noise and opaque to outsiders. With such complexities, how is one able to discern skill from luck? With data increasing exponentially, decision making suffers by inherent biases and noise. Systematic investment managers realized early on that removing the emotional pitfalls from the investment process and focusing on testable and repeatable disciplines would result in better outcomes. In our increasingly complex world, beware the noise.