Many investors cling to the comforting idea that markets oscillate around a stable mean, as if price movements are temporary deviations destined to snap back to an equilibrium. But what if mean reversion is an illusion? What if the true force at work is not a predictable return to normal, but an ever-evolving process of adaptation to new conditions?

This post challenges the myth of mean reversion and reveals why Outlier Hunting is the only way to navigate markets shaped by uncertainty and structural shifts.

The Comforting Myth of Mean Reversion

Mean reversion is a bedrock assumption in many financial models. It underlies strategies like statistical arbitrage, pairs trading, and valuation-based investing, where deviations from historical price ratios are seen as temporary mispricings rather than signals of structural shifts.

The mean-reverting assumption stems from classical equilibrium theory, which posits that financial markets, like physical systems, tend to revert to a stable equilibrium when external forces subside. This idea has roots in statistical physics, where Brownian motion and mean-reverting stochastic processes suggest that asset prices fluctuate around a mean with a tendency to return.

The problem? Markets are not equilibrium systems.

Unlike a pendulum that swings back to its central position, markets are complex adaptive systems (CAS) that evolve based on their past states. Price movements don’t “snap back” to a predetermined mean; they recalibrate based on the environment they emerge from.

Complex Systems Don’t Revert—They Adapt

In natural systems, equilibrium is a temporary illusion. Instead, we see path-dependent evolution, where the future state of the system depends on its past trajectory. This concept—called hysteresis in physics and non-stationarity in finance—means that once a system changes, it does not simply revert.

Unlike closed thermodynamic systems that settle into equilibrium over time, financial markets are complex adaptive systems (CAS). In CAS, equilibrium is fleeting, as the system continuously evolves based on new inputs, structural shifts, and feedback loops.

Here’s why mean reversion fails in real markets:

1. Non-Equilibrium Dynamics (Thermodynamics and Finance)

   • Financial markets exhibit self-organized criticality, much like earthquakes, wildfires, or weather systems. These systems don’t revert to a stable mean—they build up imbalances until a major shift occurs.
   • In thermodynamics, closed systems tend toward equilibrium, but open systems with continuous inputs never do. Markets are open systems, constantly subject to new information and trader impact.
   • Example: The 2008 crisis permanently altered how global risk was perceived. There was no return to “normal”—the system adapted to a new risk regime.

2. The Adaptive Market Hypothesis (Andrew Lo, 2004)

   • Traditional financial theories assume stationary distributions, meaning historical averages remain valid over time.
   • Andrew Lo’s Adaptive Market Hypothesis (AMH) challenges this, arguing that markets evolve like biological ecosystems. Strategies that worked in the past may stop working as market participants adjust.
   • Example: Momentum effects persisted for decades but have evolved due to increased algorithmic trading. The “mean” of expected return shifts over time.

3. Power Laws and Fat-Tailed Distributions (Mandelbrot, Taleb)

   • Markets are not normally distributed—they exhibit power-law behavior, meaning extreme events happen more frequently than predicted by Gaussian statistics.
   • If prices were truly mean-reverting, fat tails wouldn’t exist. Instead, financial markets produce outliers that shape long-term returns.
   • Example: The 2024 Cocoa trend was a textbook outlier event, where a commodity kept rallying far beyond what traditional models suggested.

4. Market Microstructure: Liquidity Gaps and Structural Breaks

   • Price formation depends on liquidity and order flow, not just fundamental value.
   • Liquidity vacuums and forced liquidations create sustained price movements, defying the notion that markets “naturally” return to a mean.
   • Example: When LCTM collapsed in 1998, it triggered cascading liquidations that extended well beyond its estimated “fair value.”

“To fully grasp why financial markets never return to a stable mean, we can look to nature, where the same principle plays out in ecosystems, climate, and geology.”

1. Evolution: Life Never Reverts to a Previous State

• Why it’s relevant: Biological evolution is the ultimate path-dependent system. Once a species evolves in response to environmental pressures, it doesn’t revert to an earlier state—even if conditions change again.
• Example: The evolution of land animals from fish irreversibly changed the course of life. Even if the sea became more hospitable again, land animals wouldn’t “mean revert” back to fish. They would adapt in new ways.
• Markets parallel: Once market participants react to a structural shift (e.g., central bank intervention, financial crisis), they never return to previous behavior—they adapt based on the new reality.

2. Climate Systems: No Return to a ‘Stable’ State

• Why it’s relevant: Earth’s climate doesn’t revert to a mean. Each phase shift (e.g., Ice Age, warming periods) changes the baseline state of the system.
• Example: The planet has gone through multiple Ice Ages, but each time it emerged into a different climate state—not a return to an earlier “equilibrium.”
• Markets parallel: After the 2008 financial crisis, central banks introduced quantitative easing, permanently altering global liquidity conditions. Markets did not revert to pre-crisis behavior—they adapted to a new liquidity regime.

3. Rivers and Erosion: The Irreversibility of Structural Change

• Why it’s relevant: Rivers don’t “mean revert” to an earlier path after an earthquake or flood alters their course.
• Example: The Mississippi River changed course after the 1927 Great Flood. Despite efforts to restore it, the river followed a new trajectory.
• Markets parallel: When a liquidity crisis or major policy shift occurs, market flows do not return to previous levels—they follow the new liquidity structure, forming new price trends.

4. Forest Succession: Ecosystems Never Revert to Their Original State

• Why it’s relevant: Ecosystems don’t “bounce back” to a prior equilibrium after disturbances. Instead, they undergo succession, evolving into new states based on prevailing conditions.
• Example: After a wildfire, the forest doesn’t return to exactly how it was before—it develops new plant species, soil composition, and animal populations.
• Markets parallel: Economic recessions act like forest fires. They clear inefficient businesses, but the recovery isn’t a return to the past—it’s an emergence of new industries, policies, and behaviors.

5. Fractals and Feedback Loops: Structural Complexity Over Time

• Why it’s relevant: Many complex systems, from blood vessels to neural networks, exhibit fractal structures, meaning they grow and evolve with feedback loops. These structures never “return to a mean”—they continuously develop new layers of complexity.
• Example: The human circulatory system develops based on early embryonic growth patterns, but once it forms, it doesn’t revert—it keeps branching and adapting based on new conditions.
• Markets parallel: Once financial markets develop new layers of complexity (e.g., algorithmic trading, global liquidity flows), they don’t strip away that complexity—they build upon it in adaptive ways.

Financial Markets as an Adaptive System

These natural examples reveal a fundamental truth: in complex systems, change is path-dependent and irreversible. Markets function the same way.

1. Traders adapt to new conditions.

• When new information enters the market, traders react—but their reactions become part of the system, altering future price dynamics.
• Markets are never the same after major events (e.g., 1987 crash, 2008 crisis, 2020 pandemic).

2. Liquidity flows change market behavior.

• Just as river paths shift over time, market liquidity permanently changes after major shifts (e.g., central bank policies, hedge fund blowups).
• Price behavior evolves in response to the availability (or absence) of liquidity.

3. Regimes shift permanently, not temporarily.

• The Great Inflation of the 1970s led to structural policy changes that forever altered monetary frameworks.
• The post-2008 era of low interest rates created an entirely new trading environment—one that didn’t revert but evolved.

The Illusion of Stability

Many traders mistake volatility clustering (a well-documented market phenomenon) for mean reversion. In reality:

• High-volatility periods don’t revert to low-volatility—they persist and evolve (e.g., Mandelbrot’s fractal market hypothesis).
• Price “consolidation” doesn’t mean reversal—it often precedes the next major trend.
• New market participants and leverage cycles create non-stationary effects that mean reversion models can’t capture.

Consider the Hurst Exponent, which measures whether time series display mean-reverting behavior. Markets often show H > 0.5, meaning they exhibit persistent trends rather than oscillations around a mean.

“The longer you expect markets to revert to a mean, the more outliers will surprise you.”

Outlier Hunting: The Strategy for an Adaptive Market

If mean reversion is an illusion, what’s the alternative? Outlier Hunting.

Instead of expecting markets to return to a past state, Outlier Hunters ride the structural shifts that define financial history.

• Markets don’t revert—they trend. Rather than betting on reversion, we capture sustained moves.
• Volatility is information. Instead of suppressing risk, we embrace its asymmetry.
• Simplicity beats complexity. Simple trend-following rules—cutting losses and letting profits run—exploit market adaptations rather than fighting them.

“The best way to trade markets that never revert? Ride trends, embrace volatility, and adapt.”

• Ride trends instead of fading them. Mean reversion traders bet against change—Outlier Hunters ride it.
• Accept volatility as a feature, not a bug. Instead of trying to suppress risk, trend followers use it to capture outlier moves.
• Recognize that markets evolve. The best strategies are those that adapt, rather than assume price will snap back to an old equilibrium.

Let Go of ‘Normal’

Markets don’t return to normal because there is no normal—only an evolving landscape of traders, liquidity flows, and structural changes.

Mean reversion is the mirage. Outliers are the reality.

The choice is clear: Chase an illusion, or embrace Outlier Hunting and position yourself on the right side of financial evolution.

“Nature doesn’t revert to a mean. Neither do markets. Adapt or be left behind.”