This month I wanted to explore markets in general rather than focus on a particular aspect of them.
Signal vs. Noise
Turn on any financial news channel and you will get a confident explanation of why markets are up or down today. Unemployment numbers came in strong. The Fed signaled a pause. Tensions in the Middle East escalated. The explanations sound reasonable. The problem is that if you watched the same channel long enough, you would notice that the same data point gets cited as the reason markets went up on Tuesday and the reason they went down on Thursday. This is what Nassim Taleb calls a narrative fallacy: the tendency to construct a plausible story around a random outcome after the fact. We are pattern-seeking creatures, and our minds oblige us by providing patterns whether they exist or not.
The research supports a more humbling conclusion. Short-term market movements are largely random. This is not a fringe view. It sits at the foundation of a substantial body of academic work and explains why most professional forecasters cannot predict market direction with any reliability over short time horizons. If daily price movements were genuinely explainable by the news of the day, you would expect professional analysts with access to the best data and fastest information to outperform consistently. They do not.
This does not mean markets are unintelligible. It means the daily news cycle is the wrong scale at which to try to understand them. The noise is loudest at short time horizons. The signal gets clearer as you zoom out. What we are looking for are the structural forces that take longer to establish and whose effects persist long enough to impact how you should build and manage a portfolio. Everything else is a distraction.
There are two widely popular views of financial markets and how they work. One view is that markets are complicated but ultimately understandable. It looks at all the information bombarding us and sees it all as inputs that explain why markets and particular stocks are up or down on a daily basis. That prices reflect all available information because rational actors competing for profit rapidly arbitrage away any mis pricing (also called price discovery). Think of a sprinkler. If you know where the sprinkler was located, and other things like the rotation, water pressure, diameter of the hose, etc., you can take all that information and figure out where the drops will fall. That’s a complicated system but ultimately, it’s linear. You can follow the cause (sprinkler) forward and calculate the outcome (where the drops fell). This is called the efficient market hypothesis (EMH) and it won Eugene Fama the Nobel Prize. The EMH describes markets as though they are a complicated system, one where all inputs are known, and prices are the logical output of all available information. This implies that you cannot consistently beat the market because the market has already priced in everything you can know. This is the principle behind passive and index investing. You can’t beat the market and so you should just buy a set of index funds, periodically rebalance your funds, and call it a day.
An early counter to the EMH was put forth by George Soros with his theory of reflexivity. His argument goes like this: In the EMH, fundamentals determine prices. Prices are the dependent variable. Soros argues that this is incomplete. Prices also influence fundamentals. A company with a rising stock price can use that stock to make acquisitions, attract talent, and lower its borrowing costs, which improves its actual business performance, which justifies the higher stock price. The price and the fundamentals are in a continuous two-way interaction, each shaping the other. This creates two phases in any market cycle. The first is a self-reinforcing trend, where the feedback loop between prices and fundamentals amplifies the initial move far beyond what either variable would justify in isolation. The second is a moment of recognition that Soros calls the point of reflexivity where the gap between the inflated price and the underlying reality becomes undeniable, the feedback loop reverses, and the self-reinforcing trend runs equally hard in the opposite direction. That is reflexivity.
The practical implication Soros draws is that markets are constantly overshooting in both directions, driven by the recursive relationship between perception and reality. Prices are not a readout of fundamentals. They are participants in shaping them. This view of a non-efficient market is further reinforced by other Nobel winners like Kahneman who developed a model of decision making around biases with his research partner Tversky and Richard Thaler who studied how biases influence economic decision making. It doesn’t sound super-efficient, does it?
The fact that both sides of this debate have compelling evidence is itself telling. Markets are neither the efficient machine Fama describes nor simply a collection of biased and irrational actors. The EMH captures something real: information does get incorporated into prices, and most active managers do fail to beat the index consistently over time. Soros and the behavioral economists also capture something real: prices overshoot; feedback loops amplify trends beyond what fundamentals justify, and human psychology leaves fingerprints all over market behavior. The reason both can be partially right is that they are each describing a piece of a larger and more complex picture.
My view is that markets are best understood not as complicated systems but as genuinely complex systems with properties that neither framework fully accounts for on its own. A complicated system is one where the relationship between cause and effect is knowable and linear, meaning that if you have enough information about the inputs, you can reliably predict the outputs. Complicated systems and complex systems are very different things. A complex system is a network of interconnected components that interact in non-linear ways to produce outcomes that can’t be predicted by looking at the parts alone.
Complex systems have three defining characteristics. First, feedback loops: the outputs of the system feedback in as new inputs, meaning the system is constantly reacting to itself. Second, nonlinearity: small inputs can produce either negligible effects or catastrophic ones depending on where the system is at that moment. Third, emergent stability: complex systems develop an equilibrium that absorbs shocks and adapts to maintain a steady state. In biology, we call this homeostasis. In markets we call it resilience, though as we will see, the two are not the same thing.
I think markets have all of these properties. Consider the dot-com bubble. Through the late 1990s, rising technology stock prices made it easier for those companies to raise capital, hire talent, and expand. That expansion generated earnings growth, or at least the appearance of it, which justified higher prices, which attracted more investment, which drove prices higher still. The feedback loop fed itself for years beyond what any rational valuation framework would have supported. Then sentiment shifted. Not because of a single dramatic event, but because the cumulative weight of stretched valuations finally became undeniable to enough participants simultaneously. What followed was not a gradual correction. It was a collapse. Think of snow accumulating on a mountainside. Each snowflake is identical to the one before it. The mountain absorbs them one by one without visible consequences. Then one skier, one sound, or simply one more snowflake triggers a catastrophic release of everything that built up before it. The trigger was not the cause. It was just the last input in a long invisible buildup. Markets work the same way. The valuations had been accumulating for years; each increment unremarkable on its own. Then the mountain let go. That is nonlinearity. That is complex system behavior. A complicated system would have corrected gradually as information changed. This one held, held, held, then broke.
So, if we are trying to navigate a complex system whose outcomes cannot be accurately predicted ahead of time, what do we do? How do we invest responsibly? We do not pretend we can find a clear, easy path through the forest. What we can do is study the terrain well enough to identify where the ground is more solid and where it is likely to give way. Where are the structural forces creating sustained erosion? Where are the dams redirecting flow in ways that will persist? Understanding secular macroeconomic trends does not tell you exactly where the market will go. It tells you which areas have the wind at their back, and which are accumulating fragility. That shifts the odds. In a complex system, you cannot eliminate uncertainty. But you can position yourself to benefit from its tendencies rather than be blindsided by them. You can find some of our views on these forces in our February note here.