Friday, Jul 9th 2021

What zebra stripes teach us about financial markets


“When you start looking at a problem and it seems really simple, you don’t really understand the complexity of the problem. Then you get into the problem, and you see that it’s really complicated, and you come up with all these convoluted solutions. That’s sort of the middle, and that’s where most people stop. *But the really great person will keep on going and find the key, the underlying principle of the problem — and come up with an elegant, really beautiful solution that works.” – Steve Jobs*

Alan Turing’s work revealed that a simple and elegant formula could explain the natural complexity we see around us. He showed that structure can arise from seemingly complex processes in self-organizing systems. This explains how cells can develop in a systematic way to create zebra stripes; how both complexity can arise out of simplicity and simplicity can describe complexity.

Michael Mauboussin has expanded on this idea by describing the financial markets as a complex, adaptive system. The stock market is composed of heterogeneous agents that interact with one another and make decisions that evolve over time. Because the agents interact with one another, the whole of the market becomes greater than the sum of the individual agents.

It follows that a small change can drastically alter the trajectory of the entire system (what is popularly termed the “butterfly effect”). A small action in an ordered market can create chaos.

What are we to do as investors in this seemingly chaotic and unpredictable world? At Titan, we embrace the market’s complex and self-organizing behavior in both our security analysis and portfolio management framework.

Our analysts use scenario analysis based on in-depth, value-add research to determine a range of potential outcomes for an investment. We do not fall for the illusion of false precision adopted by media pundits and other investors. Our scenario analysis and stress-testing attempt to account for improbable outcomes that can arise in such systems.

We look to minimize improbable events by focusing on companies with a narrower distribution of potential outcomes. This is why we prefer to invest in monopolies and functional oligopolies, preferably with recurring revenue and pricing power. Removing competitive dynamics and cyclicality from the equation allows us to more accurately determine a company’s return prospects.

Our Occam’s Razor approach to investing starts with understanding what is knowable and what is important about a company. These two characteristics reveal a stock’s critical factors: the important drivers of a company’s performance. By basing the core of our analysis on just a few KPIs, we greatly minimize the number of ways we could be wrong and can more easily perform attribution analysis around a company’s performance. An added benefit is that this process allows us to maximize the ROI on our time.

In her timeless book, Thinking in Systems, Donella Meadows explains that one of the keys to successfully operating in a system is to expand time horizons. Oscillations are caused by delays in feedback loops in a system. The magnitude of oscillations is minimized by lengthening the amount of time between which changes are made to the system.

We believe that taking a long-term investment horizon greatly reduces performance volatility. As Charlie Munger puts it, “The first rule of compounding is to never interrupt it unnecessarily.”

Our appreciation for the complexity inherent in financial markets drives the elegant simplicity of our investment process. By focusing on what is important -- and by avoiding unnecessarily interrupting the compounding of our companies -- we can successfully navigate the financial markets together over time.

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