Skip to content

Herd Mentality

Herd mentality is the tendency to do what other people are visibly doing rather than what your own information suggests. The economics of it is more unsettling than the folk version, because following the crowd can be the individually rational move and still produce a collectively wrong answer.

Last reviewed by Steven Fox, CFP®, EA on

Quick Summary

  • The academic term is herd behavior, and the standard mechanism is an information cascade: you can see what other people chose but not why.
  • Nothing in the mechanism requires panic, stupidity, or social pressure. A fully rational person facing the same sequence of observations does the same thing.
  • Once a cascade forms, later participants add no new information to the pool, so the crowd's size stops being evidence about the crowd's judgment.
  • Cascades are fragile by construction. Because they rest on very little genuine information, a small amount of new public information can reverse one abruptly.
  • The practical reading is that "a lot of people are doing this" is weak evidence about value, and it gets weaker as the number grows.

Definition

Herd mentality is the tendency of individuals to align their decisions with the observed decisions of others rather than with their own private information. In economics and finance the phenomenon is usually called herd behavior, and the foundational model is Abhijit Banerjee's "A Simple Model of Herd Behavior" in the Quarterly Journal of Economics 107(3) in 1992. Sushil Bikhchandani, David Hirshleifer, and Ivo Welch developed the same year, in the Journal of Political Economy 100(5), the term that has stuck for the mechanism: an informational cascade.

The load-bearing feature of both models is what participants can and cannot see. People decide in sequence. Each has a private piece of information, and each can observe what everyone ahead of them did but not what any of them knew. That asymmetry is enough to produce herding without any appeal to irrationality, emotion, or a desire to fit in.

Advanced Explanation

How a cascade forms, and why it is rational. Suppose your own information points weakly one way, and you can see that several people before you chose the other. Each of those choices is evidence, because presumably each was made on some information. Once enough of them accumulate, the information you can infer from their actions outweighs your own single observation, and following them is the correct inference rather than a failure of nerve. The trouble is what happens next: because you followed the crowd instead of your own signal, your information never enters the public pool either. The next person sees one more choice and no more evidence. From that point the sequence can run indefinitely on the same small stock of genuine information, and every additional participant makes the crowd larger without making it better informed.

Two properties follow, and both are practically useful. The first is that a cascade can settle on the wrong answer, because only the earliest participants' information actually got in, and early participants are not selected for being right. The second is fragility: precisely because so little real information underlies the pattern, a modest piece of new public information can flip it. Bikhchandani, Hirshleifer, and Welch emphasize this, and it explains a familiar shape in markets, where a long, apparently stable consensus reverses faster than the change in fundamentals seems to justify. A crowd is not a heavy object; it is a thin one that looks heavy.

This is a different mechanism from the ones it gets confused with. Conformity, in the sense of not wanting to look foolish or to stand apart, is a social-pressure story and it exists, but it is not what these models describe. Extrapolating from a recent run of good outcomes is recency, a separate error about which evidence gets weight. Seeking out only supportive coverage is confirmation, an error in the search rather than in the inference. Herding is narrower and stranger: correct reasoning from an impoverished evidence base that the reasoning itself keeps impoverished.

A second and genuinely distinct channel is reputational. David Scharfstein and Jeremy Stein argued in "Herd Behavior and Investment," in the American Economic Review 80(3) in 1990, that a professional whose incentives depend on relative judgment has reason to follow consensus even when their own information disagrees, because being wrong alone is penalized more heavily than being wrong with everybody else. That mechanism has nothing to do with what anyone believes and everything to do with how they are evaluated, and it points the same direction. Their formal model was subsequently the subject of a published comment and reply in the same journal, so treat the channel as a well-known argument rather than a settled measurement.

Where it shows up in ordinary financial life. Fund flows follow performance, so the money most often arrives after the returns that attracted it. Asset classes acquire and lose respectability in ways that track visible participation more closely than any change in the underlying claim. And the question people actually ask, in a rising market or a falling one, is usually what other people are doing, which is exactly the observation the cascade model says is least informative once the crowd is large.

One honest limit on the concept. Herding is a mechanism, not a diagnosis. Observing that many people hold something does not establish that a cascade is operating, and a widely held view can be widely held because it is correct. What the model does is remove the comfort from the observation: agreement is informative only to the extent the agreeing parties reached their views independently, and visible agreement is evidence that they did not.

How to Remember

You can see the choices and not the reasons. The tenth person in a line is not looking at ten pieces of evidence, they are looking at one piece of evidence and nine people who could not see it either.

Used in a Sentence

“Herd mentality is the reason the fund's largest inflows arrived in the quarter after its best year, since every new investor could see the money going in and none of them could see why.”

How It Works

Resolving the mechanism rather than an amount, because the arithmetic that matters here is a count of information rather than a count of dollars.

A hypothetical example, using the standard sequential setup. Five people decide in turn whether to buy into a fund. Each privately sees one piece of evidence that is right two times out of three. Nobody sees anyone else's evidence, and everybody sees the decisions.

Person 1 sees favorable evidence and buys. Person 2 sees unfavorable evidence. She can infer one favorable signal from Person 1's purchase, which cancels her own, leaving her indifferent; assume she resolves the tie by following the person ahead of her, which is one of the conventions these models use. She buys. Person 3 now observes two purchases. From where he stands those look like two favorable signals, though in truth they are one favorable and one unfavorable, because Person 2's evidence was never transmitted. His own unfavorable signal is outweighed two to one, so he buys as well, and adds nothing. Persons 4 and 5 know that Person 3's purchase was uninformative, so the pool of real evidence they face is still the same two apparent signals, and they buy regardless of what they privately see.

So four of five investors hold a position resting on one genuine piece of favorable evidence, and the unfavorable signals held by Persons 2 and 3 never reached anyone at all. The cascade is real but it is not inevitable: had Person 2 broken the tie by following her own evidence instead, Person 3 would have seen a disagreement, no cascade would have formed, and the pool would have kept collecting information. A cascade turns on the first participant who stops contributing.

Pros and Cons

Pros

  • Following better-informed people is often the right move, and the models say so rather than denying it.
  • Copying a widely used default is cheap, and for decisions where no individual advantage is available it is a reasonable answer.
  • Understanding the mechanism reframes a market consensus as thin rather than heavy, which is directly useful when it reverses.
  • The fix is simple to state, which is to ask what would change your mind before you notice what other people are doing.

Cons

  • The crowd's size is not evidence of the crowd's judgment, and it feels like evidence.
  • Because it can be individually rational, the usual defenses against bias ("think harder", "be independent") do not engage it.
  • Cascades reverse abruptly, so the exit is crowded at the same moment the entrance was.
  • Reputational incentives push professionals the same way, which means consensus can be reinforced by people who privately disagree with it.
  • The concept is easy to overuse. Any widely held position can be labeled herding after the fact, which explains nothing.

People Also Asked

Answers to the most frequently asked questions.

What is the difference between herd mentality and herd behavior?
They describe the same thing, and the second is the term the research uses. "Herd behavior" is the phrase in the economics literature, including Banerjee's 1992 paper in the Quarterly Journal of Economics, while "herd mentality" is the ordinary-language version and is what people search for. The mechanism most often cited for either is the informational cascade named by Bikhchandani, Hirshleifer, and Welch in 1992.
Is herding irrational?
Not in the models that explain it best. In an information cascade each participant can observe what others chose but not what they knew, so once enough choices accumulate, the information inferred from them genuinely outweighs one person's private signal. Following is the correct inference on the evidence available. What goes wrong is collective rather than individual: because nobody after the first few contributes new information, the crowd grows without learning anything.
Why do market consensuses reverse so suddenly?
One explanation is that a cascade is fragile by construction. If a long run of similar decisions rests on the information of only the earliest participants, then very little new public information is needed to overturn it, because there was very little supporting it in the first place. That is a property Bikhchandani, Hirshleifer, and Welch drew attention to, and it predicts exactly the pattern of a stable-looking consensus that breaks faster than the underlying facts changed.
How is herd mentality different from recency bias?
Recency bias is about which evidence you weight: recent experience gets more attention than it deserves, so expectations extrapolate whatever just happened. Herding is about whose evidence you use: you substitute other people's observed decisions for your own information. The two frequently run together in a rising market, because the recent past looks good and a lot of people are visibly participating, but they are separate mechanisms with separate countermeasures.
How do you avoid it in practice?
By making the decision rule before you look at what other people are doing, and writing it down. Because herding is a reasonable response to observing choices, the intervention that works is not resolving to be independent but arranging to decide on information you gathered yourself, and to know in advance what would change your mind. A written policy for a portfolio, with target allocations and the conditions under which they change, is the ordinary form this takes.

Have a question a definition can't answer?

Advice-only advisors answer questions like this for a transparent flat fee — no products, no commissions, no asset management.

Find an Advisor