When people talk about bubbles, the story is usually psychological. Investors get euphoric, abandon discipline, extrapolate recent returns, and eventually panic. This narrative goes back at least to Kindleberger’s Manias, Panics, and Crashes and remains the default explanation in both the media and policy circles. Even Eugene Fama, in his Nobel lecture, framed bubbles as irrational price increases that imply predictable crashes.

Gadi Barlevy’s paper takes a more subtle and possibly more unsettling position: irrationality is not the key ingredient. Bubbles can arise even when agents are fully rational. What matters instead is who knows what, and who knows that others know it.

That distinction sounds technical, but it has important implications for how traders should think about markets.

What Economists Mean by a Bubble

Barlevy starts by clarifying a common confusion. In academic finance, a bubble is not defined by boom-and-bust price behavior. It is defined by overvaluation: a price that exceeds the present discounted value of expected future cash flows. Boom-and-bust dynamics are related, but not identical. Some large price increases never crash. Others crash violently. Overvaluation can exist with or without drama.

This matters because much of the “there are no bubbles” debate implicitly assumes that if prices don’t predictably collapse, bubbles can’t exist. Barlevy shows that this logic is flawed.

Early theoretical work showed that bubbles were impossible in simple models with rational agents. The reason was not rationality itself, but symmetry of beliefs. When all agents share the same information and agree on fundamentals, nobody is willing to overpay for an asset they might end up holding forever. In finite horizons, backward induction kills bubbles. In infinite horizons with a finite number of agents, transversality conditions do the same job.

These results were often interpreted as “rational agents can’t generate bubbles.” Barlevy’s key move is to say: that’s the wrong conclusion. What those models actually show is that bubbles are impossible when rational agents share identical beliefs.

Once agents can disagree — even temporarily — everything changes.

If you believe an asset is overvalued but also believe someone else may value it more in the future, buying it can be rational. This is the classic “greater fool” logic, but Barlevy emphasizes that it does not require stupidity. It only requires heterogeneous beliefs.

Importantly, heterogeneity can arise even among fully rational agents. People may observe different signals. Information may not be common knowledge. Agents may start with different priors in novel situations. Rationality only requires Bayesian updating, not agreement at every point in time.

In such settings, agents knowingly trade overvalued assets because they expect to sell them to others with different beliefs. These bubbles look almost identical, in their price dynamics, to bubbles driven by extrapolation or overconfidence.

Why Some Bubbles Must Crash — and Others Don’t

A striking insight of the paper is that not all rational bubbles behave the same way.

In models where overvaluation is common knowledge, bubbles must be able to survive indefinitely to exist at all. This makes them empirically fragile and inconsistent with many real-world episodes.

By contrast, in models where information is private or incompletely shared, bubbles can arise in finite settings and must eventually crash once enough agents realize the truth. The crash is not driven by panic, but by information gradually becoming common knowledge.

Other models, such as those involving risk-shifting by intermediaries or leverage, produce bubbles that may or may not collapse depending on realized outcomes. Overvaluation does not mechanically imply a crash.

This neatly explains why empirical studies find that large price run-ups do not reliably predict negative future returns. Bubbles can exist even when crashes are unpredictable.

Implications for Investors

The practical lesson is: markets can be inefficient without being irrational.

Price signals may reflect strategic interaction among agents with different information, constraints, or incentives. Betting against an apparent bubble can be costly and premature if other agents remain willing and able to push prices higher. Rational arbitrageurs may even ride the bubble for a time, amplifying it rather than correcting it.

This also helps explain why policy debates around bubbles are so confused. If bubbles are driven by irrational beliefs, intervention looks paternalistic. If they arise from rational agents interacting under asymmetric information or constraints, intervention must be justified on efficiency grounds instead.

For traders, the takeaway is simpler and more brutal: knowing an asset is overvalued does not tell you when it will stop being overvalued. Timing depends on information diffusion, constraints, and incentives — not on the existence of a bubble per se.

In that sense, Barlevy’s paper is less about bubbles than about humility. Markets can be wrong for rational reasons. And that makes them far harder to trade than the folk psychology story would suggest.

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