Quantum vs Classical Thinking in Finance

What Scientists Know That Traders Ignore

Prologue

If you stare at the markets long enough, you start to believe they can be solved. Patterns tempt you. Correlations whisper. The promise of signal over noise feels just within reach, especially with models sharper, faster, and deeper than ever.

But maybe we’ve misunderstood the nature of the game entirely.

In most of finance, uncertainty is treated like a math problem. Add more data, refine the model, optimize the forecast. The future is a probability distribution, and your job is to pick the side with better odds. It’s clean. Rational. Classical.

But here’s the catch: almost no one in physics, neuroscience, or cognitive science sees uncertainty that way anymore.

Not really.

They treat it as something deeper. Something that’s not always measurable, not always reducible, and often not even observable.

In quantum mechanics, the more you know about a particle’s position, the less you know about its momentum. In cognitive science, even our perception of a simple color can’t be trusted outside its context. In complexity theory, outcomes aren’t just hard to predict, but they’re entangled, path-dependent, and reflexive.

Fields like physics made this leap decades ago. As David Orrell argued in Quantum Economics (2013), the world doesn’t just unfold through cause and effect. It evolves through feedback, attention, and interpretation.

Finance never truly caught up.

The classical trap: How can you forecast a world that doesn’t sit still?

Classical finance sees the market as a machine. Prices are outputs. Inputs are fundamentals, expectations, maybe even a bit of noise. The goal is to model it and reduce uncertainty through information.

This is the foundation of nearly every textbook: CAPM, Black-Scholes, factor models, even most flavors of machine learning. They assume uncertainty can be tamed if you just observe enough, regress enough, or simulate hard enough.

But this breaks down fast.

Markets aren’t stationary. They’re reactive. Path-dependent. What you observe today isn’t just a reflection of yesterday’s fundamentals. It’s entangled with expectations, feedback loops, and how others are positioned.

This is not a fringe view. It’s exactly what David Orrell challenges in The Empirical Case for Quantum Models in Finance (2023). He shows that classical models, rooted in equilibrium and reductionist assumptions, repeatedly fail to explain or predict actual market behavior. Markets do not converge toward a stable truth. They oscillate, react, adapt. The data doesn’t just falsify classical finance but it points toward something fundamentally different.

Something quantum-like.

In a quantum system, the very act of observation changes the system. You don’t just passively measure reality. You participate in shaping it. And that’s not a metaphor. In Zhang & Huang’s 2010 model, they propose a quantum formalism for stock prices, where the “wave function” of a security encodes potential price states, and market observation collapses that function into a realized outcome. The market is not a calculator. It is a collapsing field of contingent beliefs.

That may sound abstract. But its consequences are very real.

Take the volatility clustering we observe around geopolitical shocks or Fed announcements. As Henkel (2016) argues, classical models assume continuous processes with smooth diffusion. But real prices spike, stall, or jump discontinuously. These behaviors are better captured by quantum-type dynamics where energy jumps and probabilistic state transitions are the norm, not the exception.

The deeper problem is not model error. It’s model ontology.

We treat the market as if it exists in one clear state, waiting to be decoded. But much like a quantum system, its “state” depends on how it’s being watched, interpreted, and acted upon. The market isn’t a fixed machine. It’s a reflexive observer-reactive medium.

And yet, most financial modeling still pretends we’re outside of it, cleanly measuring truth from a distance.

Quantum models reject that fantasy.

They don’t ask, “How can I find the right model for this environment?” They ask, “What changes when I interact with it? How should I update when the interaction disturbs the system?”

That’s why quantum thinking matters.

Not because it’s exotic. But because it’s closer to the way markets actually behave.

What uncertainty really means

Uncertainty in classical systems is just a placeholder. It’s what you don’t know now, but could know later, if you had more data, better models, faster computing. In this world, randomness is temporary. Given enough observation, it dissolves.

Quantum uncertainty is different.

Here, uncertainty is not ignorance. It is fundamental. You cannot fully know a particle’s position and momentum at the same time. The more precisely you observe one, the less you know of the other. It’s not a data problem. It’s baked into the nature of the system.

Now think about a trader watching the market.

They see a sudden spike in oil prices. What’s the “position” of the market? What’s its “momentum”? Any answer you offer affects the other. The moment you act on the spike, such as buying energy stocks, hedging exposure, or rerouting capital, the market shifts. The system updates itself based on your attempt to measure it.

This isn’t just poetic. It’s observable.

When a large trade is executed, when a regulatory headline hits, or when a prominent fund manager changes tone, the impact is not delayed. It is immediate. The act of observing or acting based on a signal alters the entire system’s state. Prices, flows, sentiment, all of it reconfigures.

This is why uncertainty in markets must be reframed.

Not as something to eliminate, but something to engage with. You cannot fully map the system in advance. But you can react to how it moves when you touch it. You can design strategies that are not prediction-based, but reaction-capable. You can optimize for adaptiveness, not just accuracy.

And you can stop pretending the world stands still long enough for your models to catch up.

Forecasting under fog: Why classical models fail subtly

Most models in finance rest on an implicit contract: give me enough data, and I’ll give you the truth. Regression, factor models, even many ML pipelines rely on stable relationships between variables. You assume the past is at least a decent proxy for the future. And if the future changes, you assume the model will catch up.

But what if those relationships are not just unstable and what if they’re unknowable?

Quantum thinking doesn’t say the market is random. It says it’s contextual. A stock’s price doesn’t exist in isolation. It exists relative to who’s watching it, what they know, and how they plan to act. Like a quantum particle, its “state” depends on its observer.

Let’s say you build a volatility forecasting model. You train it on a year of data, validate it, and deploy. Two months later, a geopolitical shock hits. The model reacts poorly, not because it didn’t see the shock coming, but because the shock altered how everything else behaves. Correlations broke. Regimes flipped. The system evolved, and your model stayed put.

This is not an edge case. It’s the default.

Classical models mistake the market for a machine. But the market behaves like an organism that is shaped by perception, feedback, and interaction. If your model can’t feel its environment shifting, it will be blind to the most important changes.

Quantum-style models aren’t “better” because they use physics. They’re better because they abandon the illusion of perfect foresight. They don’t try to forecast a fixed path. They try to adapt to an evolving one.

They ask not: What will happen?But: How should I respond when it does?

The market is not a mirror. It’s a medium.

In classical finance, the market is a mirror. Prices reflect fundamentals. Our job is to polish the mirror to get rid of noise, bias, inefficiencies, so we can see the truth underneath.

But what if that metaphor is wrong?

Quantum thinking suggests a deeper, more unsettling possibility. That the market isn’t a mirror at all but it’s a medium. Like water rippling under motion. Like an echo chamber where sound shapes the space.

Every trade you place. Every forecast you make. Every tweet that goes viral.

Each is an observation. And in quantum systems, observation changes the system. The more you try to measure, the more you disturb. The more you bet, the more your bet bends the world you’re trying to bet on.

This isn’t a new age metaphor. It’s a logic built into complex, reflexive environments. A hedge fund leaks a strategy. Others copy it. The edge disappears. What was alpha becomes noise. Not because the world changed, but because the act of knowing changed the world.

You can’t separate the observer from the system. You are part of the model you’re modeling.

This has implications for everything: strategy, research, and even ethics.

It means prediction is less about being right, and more about staying calibrated. About understanding how your presence distorts the game. About trading not on certainty, but on balance with humility.

It means models should not just output signals. They should tell us how much our own thinking is part of the feedback loop.

In classical finance, you stand outside the world and map it.

In quantum finance, you’re always inside it. And every map you draw reshapes the terrain.

Where quantum thinking is already creeping into finance

You might expect that if quantum ideas entered finance, they’d arrive through the front door like flashy startups, academic hype, or quantum computers humming in hedge fund basements.

But that’s not what happened.

Instead, quantum thinking seeped in sideways. Quietly. Through models that act quantum without ever calling themselves that.

Start with information-based asset pricing. Unlike classical theories where prices reflect “true” value plus noise, these models assume that value itself is latent and never fully knowable. What matters is how information about it flows, how quickly it gets absorbed, and how agents act on partial clues.

The result? Prices emerge as consensus guesses in flux, not hard truths waiting to be discovered. That’s a quantum idea at its core: the state of the system is shaped by how it’s observed.

Another quiet frontier is reflexive models, those that account for feedback loops between beliefs and behavior. George Soros wrote about reflexivity decades ago, but only recently have quants started building models around it. These models don’t just predict price moves. They simulate how forecasts themselves affect the market. Like wave functions collapsing, expectations shape outcomes.

Even in reinforcement learning, there’s a shift toward adaptive systems that learn not just from outcomes, but from changing dynamics. In complex environments, agents can’t rely on stable reward signals. They must infer structure from noise. The boundary between state and observation blurs, again, a quantum motif.

And then there’s the quantum computing work itself. While still early, researchers at places like JPMorgan, Goldman Sachs, and QCWare are experimenting with quantum-enhanced Monte Carlo simulations and portfolio optimization. These aren’t commercial systems yet. But they reflect a deeper hunch: the market’s messiness might be a better match for machines that thrive on entanglement and probability.

So, no, we haven’t replaced Black-Scholes with Schrödinger.

But the shift is underway.

Finance is beginning to model itself less like a game of chess, and more like a dance of probability, observation, and adaptation.

What scientists know that traders ignore

Markets don’t just move. They respond to the act of being analyzed.

This isn’t poetic license. It’s a reality long understood in quantum mechanics. The very process of measuring a quantum system changes its state. There’s no clean separation between observer and observed. In finance, the analogy is sharper than we admit.

When investors model the future, they shape it. A forecast drives flows, which influence price, which changes the basis of the next forecast. Feedback is baked in. Yet much of traditional finance still acts as if the market is a neutral backdrop, reacting passively to signals. That belief has deep roots in classical thinking; a belief in objectivity, in external truths waiting to be decoded.

Quantum logic, by contrast, accepts limitation. It assumes that information is incomplete, that outcomes are probabilistic, and that observation alters what’s observed. This is more than a philosophical position. It carries real implications for how we model risk, uncertainty, and decision-making.

Think of volatility. Classical risk models treat it as a property of assets. A number to be estimated and plugged into equations. But in markets shaped by attention, sentiment, and narrative, volatility is reactive. It’s a reflection of observer behavior. The more people fear instability, the more they create it. Like particles in a cloud chamber, paths emerge not from deterministic laws, but from collisions of inference and reaction.

Quantum thinking also reshapes how we interpret prediction. In classical finance, prediction failure is a flaw in the model. In a quantum view, prediction itself is suspect. You can’t assign a single trajectory to a system entangled with millions of agents, each adjusting to each other in real time. The best you can do is assign probabilities and update them continuously as the wave function of market belief evolves.

This doesn’t mean forecasting is futile. It means it’s participatory. Traders aren’t mapmakers. They’re part of the terrain. The moment you believe you’ve found structure, your footprint begins to change it.

The Market Watches Itself

Reflexivity isn’t just a buzzword thrown around by hedge fund titans. It’s a structural feature of markets, and it breaks every assumption classical finance rests on.

Start with a basic example. A central bank forecasts a slowdown and cuts interest rates. That forecast drives expectations. Expectations drive asset prices. But those price movements then feed back into economic activity such as housing picks up, stocks rally, sentiment improves. The very act of making a forecast alters the thing being forecasted. It’s not just that traders react. The system responds to its own reflection.

George Soros built an entire investment philosophy on this principle. In his view, markets aren’t mirrors but funhouse mirrors, constantly distorting themselves through interpretation. His trades weren’t just bets on fundamentals. They were bets on how beliefs about fundamentals would change other beliefs, recursively.

This isn’t philosophy. It’s how price actually forms.

Modern models are starting to catch up. You now see reflexivity baked into agent-based simulations, recursive utility functions, and dynamic belief updating systems. But even those struggle with a simple truth: you can’t fully separate signal from reaction.

Say an analyst upgrades a stock. The upgrade drives inflows. The inflows push the price higher. Momentum traders join in. Other analysts raise their targets. Suddenly, the price justifies the upgrade that caused it. This is not manipulation. It’s feedback.

In quantum terms, this is the observer effect playing out across millions of agents. Beliefs shape actions. Actions shape data. Data shapes new beliefs. The wave function never collapses. It just updates iteratively, probabilistically, endlessly.

This is why trying to model markets as passive systems is fundamentally flawed. The market doesn’t just respond to external forces. It responds to itself.

And if you don’t account for that, your models will be elegant, but wrong.

Epilogue

Finance doesn’t need new metaphors. It needs new assumptions.

The market is not a machine waiting to be decoded. It is a reactive, observer-bound system which is closer in spirit to quantum fields than classical equations. Its state changes when we look at it. Its behavior depends on how we participate. This is not mysticism. It’s physics, cognition, and feedback theory converging on the same uncomfortable truth: we are inside the system we model.

Classical finance taught us to isolate variables, remove noise, and pursue convergence. Quantum thinking suggests the opposite: entanglement, contextuality, interference. Not as exotic add-ons, but as the base conditions of the world we trade in.

This shift doesn’t make markets less rational. It makes them more real.

The challenge now is not to replace every equation with quantum math. It’s to shift how we think about uncertainty, interaction, and the limits of foresight. It’s to stop treating volatility as a bug and start reading it as a signal. It’s to design strategies that stay reflexively aware of their own impact.

If you still believe markets can be solved, you’re already too late.

The real edge lies in knowing they can’t.